Quantum dots (QDs), nanoscale semiconductor crystals, have emerged as a revolutionary class of nanomaterials with unique optical and electrochemical properties, making them highly promising for applications in disease diagnosis and treatment. Their tunable emission spectra, long-term photostability, high quantum yield, and excellent charge carrier mobility enable precise control over light emission and efficient charge utilization, which are critical for biomedical applications. This article provides a comprehensive review of recent advancements in the use of quantum dots for disease diagnosis and therapy, highlighting their potential and the challenges involved in clinical translation. Quantum dots can be classified based on their elemental composition and structural configuration. For instance, IB-IIIA-VIA group quantum dots and core-shell structured quantum dots are among the most widely studied types. These classifications are essential for understanding their diverse functionalities and applications. In disease diagnosis, quantum dots have demonstrated remarkable potential due to their high brightness, photostability, and ability to provide precise biomarker detection. They are extensively used in bioimaging technologies, enabling high-resolution imaging of cells, tissues, and even individual biomolecules. As fluorescent markers, quantum dots facilitate cell tracking, biosensing, and the detection of diseases such as cancer, bacterial and viral infections, and immune-related disorders. Their ability to provide real-time, in vivo tracking of cellular processes has opened new avenues for early and accurate disease detection. In the realm of disease treatment, quantum dots serve as versatile nanocarriers for targeted drug delivery. Their nanoscale size and surface modifiability allow them to transport therapeutic agents to specific sites, improving drug bioavailability and reducing off-target effects. Additionally, quantum dots have shown promise as photosensitizers in photodynamic therapy (PDT). When exposed to specific wavelengths of light, quantum dots interact with oxygen molecules to generate reactive oxygen species (ROS), which can selectively destroy malignant cells, vascular lesions, and microbial infections. This targeted approach minimizes damage to healthy tissues, making PDT a promising strategy for treating complex diseases. Despite these advancements, the translation of quantum dots from research to clinical application faces significant challenges. Issues such as toxicity, stability, and scalability in industrial production remain major obstacles. The potential toxicity of quantum dots, particularly to vital organs, has raised concerns about their long-term safety. Researchers are actively exploring strategies to mitigate these risks, including surface modification, coating, and encapsulation techniques, which can enhance biocompatibility and reduce toxicity. Furthermore, improving the stability of quantum dots under physiological conditions is crucial for their effective use in biomedical applications. Advances in surface engineering and the development of novel encapsulation methods have shown promise in addressing these stability concerns. Industrial production of quantum dots also presents challenges, particularly in achieving consistent quality and scalability. Recent innovations in synthesis techniques and manufacturing processes are paving the way for large-scale production, which is essential for their widespread adoption in clinical settings. This article provides an in-depth analysis of the latest research progress in quantum dot applications, including drug delivery, bioimaging, biosensing, photodynamic therapy, and pathogen detection. It also discusses the multiple barriers hindering their clinical use and explores potential solutions to overcome these challenges. The review concludes with a forward-looking perspective on the future directions of quantum dot research, emphasizing the need for further studies on toxicity mitigation, stability enhancement, and scalable production. By addressing these critical issues, quantum dots can realize their full potential as transformative tools in disease diagnosis and treatment, ultimately improving patient outcomes and advancing biomedical science.

In recent years, with the large-scale use of plastic products, the degree of plastic pollution has increased, becoming a serious global problem. Microplastics and nanoplastics (MNPs), as emerging environmental pollutants, are widely found in organisms and the environment. These plastic particles enter the human body through 3 exposure pathways: breathing, the food chain’s bioaccumulation and transfer, and skin contact, thereby exerting toxic effects. The physical attributes of MNPs, including their shape, size, and surface characteristics, are not static but rather undergo dynamic transformations in response to changing environmental conditions. These changes can significantly influence their behavior and interactions within different ecosystems. When considering MNPs as carriers of chemicals, two primary mechanisms can be distinguished. (1) MNPs have the capacity to adsorb pollutants from their surrounding environment. These pollutants may encompass a wide range of substances, such as heavy metals, organic compounds, and other contaminants that are commonly found in water, soil, or air. (2) MNPs may also carry chemical agents that are artificially introduced during their commercial production process. For example, flame retardants and pigments are often added to plastics to enhance their performance or appearance. These artificially added chemicals can remain associated with MNPs throughout their lifecycle and may contribute to their overall toxicological impact. Cardiovascular diseases (CVDs) are a general term for diseases of the heart, arteries, veins, and capillaries, and are one of the main causes of disability and death. CVDs have higher incidence, mortality, and recurrence rates, and more complications, which reduce the quality of life and happiness of patients, the phenomenon is gradually showing a trend of early onset, therefore early-stage prevention for CVDs is of critical importance. This article reviews the properties of MNPs and their potential threats to the cardiovascular system, aiming to explore how MNPs cause CVDs through certain physiological effects, toxicity mechanisms, and related pathways. Our review primarily focus on elucidating several critical mechanisms through which MNPs exert their adverse effects. Specifically, the review examines how the enhancement of oxidative stress can trigger the expression of pro-inflammatory factors, which in turn leads to the formation of a chronic inflammatory microenvironment within biological systems. Additionally, MNPs possess the capacity to adsorb toxic metals and organic substances from their surroundings. Furthermore, the review summarizes that sewage irrigation and atmospheric deposition are significant factors contributing to the co-pollution of heavy metals with MNPs in environmental settings. The interaction between heavy metals and MNPs has been shown to have detrimental effects on agricultural productivity, as it can inhibit crop growth and simultaneously increase the absorption rate of heavy metals in plants. When these contaminated plants enter the food chain, the accumulated heavy metals can ultimately be ingested by humans. This process poses a potential risk for inducing acute coronary syndrome and other CVDs, thereby underscoring the importance of understanding and mitigating the impact of MNPs on human health. In addition, our review also gives examples of the long-term effects of MNPs on cardiovascular function and the adverse consequences such as arrhythmia and atherosclerosis, the limitations of the current studies of MNPs affecting cardiovascular system health and future directions are also explored.

Alzheimer’s disease (AD) is a chronic, progressive, and irreversible neurodegenerative disorder that typically begins with a subtle onset and progresses slowly. Pathologically, it is characterized by two hallmark features: the extracellular accumulation of amyloid β-protein (Aβ), forming senile plaques, and the intracellular hyperphosphorylation of tau protein, resulting in neurofibrillary tangles (NFTs). These pathological changes are accompanied by substantial neuronal and synaptic loss, particularly in critical brain regions such as the cerebral cortex and hippocampus. Clinically, AD presents as a gradual decline in memory, language abilities, and spatial orientation, significantly impairing the quality of life of affected individuals. With the aging population steadily increasing in China, the incidence of AD is rising, making it a major public health concern that requires urgent attention. The growing societal and economic burden of AD underscores the pressing need to identify effective diagnostic biomarkers and develop novel therapeutic strategies. Among the various molecular signaling pathways involved in neurological disorders, the Notch signaling pathway is especially noteworthy due to its evolutionary conservation and regulatory roles in cell proliferation, differentiation, development, and apoptosis. In the central nervous system, Notch signaling is essential for neurodevelopment and synaptic plasticity and has been implicated in several neurodegenerative processes. Although some studies suggest that Notch signaling may influence AD-related pathology, its precise role in AD remains poorly understood. In particular, the interaction between Notch signaling and non-coding RNAs (ncRNAs)—key regulators of gene expression—has received limited attention. NcRNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), are known to exert extensive regulatory functions at both transcriptional and post-transcriptional levels. Dysregulation of these molecules has been widely associated with various diseases, including cancers, cardiovascular conditions, and neurodegenerative disorders. Notably, interactions between ncRNAs and major signaling pathways such as Notch can produce widespread biological effects. While such interactions have been increasingly reported in several disease models, comprehensive studies investigating the regulatory relationship between Notch signaling and ncRNAs in the context of AD remain scarce. Given the capacity of ncRNAs to modulate signaling cascades and form complex regulatory networks, a deeper understanding of their crosstalk with the Notch pathway could provide novel insights into AD pathogenesis and reveal potential targets for diagnosis and treatment. In this study, we investigated the regulatory landscape involving the Notch signaling pathway and associated ncRNAs in AD using bioinformatics approaches. By integrating data from multiple public databases, we systematically identified significantly dysregulated Notch pathway-related genes and their interacting ncRNAs in AD. Based on this analysis, we constructed a lncRNA-miRNA-mRNA regulatory network to elucidate the potential mechanisms linking Notch signaling to ncRNA-mediated gene regulation in AD pathogenesis. Furthermore, we explored the internal relationships and molecular mechanisms within this network and assessed the feasibility and clinical relevance of these molecules as early diagnostic biomarkers and potential therapeutic targets for AD. This study aims to deepen our understanding of the molecular basis of AD and offer novel strategies for its diagnosis and treatment.

Clathrin-mediated endocytosis (CME) is a critical process by which cells internalize macromolecular substances and initiate vesicle trafficking, serving as the foundation for many cellular activities. Central to this process are clathrin-coated structures (CCSs), which consist of clathrin-coated pits (CCPs) and clathrin plaques. While clathrin-coated pits are well-established in the study of endocytosis, clathrin plaques represent a more recently discovered but equally important component of this system. These plaques are large, flat, and extended clathrin-coated assemblies found on the cytoplasmic membrane. They are distinct from the more typical clathrin-coated pits in terms of their morphology, larger surface area, and longer lifespan. Recent research has revealed that clathrin plaques play roles that go far beyond endocytosis, contributing to diverse cellular processes such as cellular adhesion, mechanosensing, migration, and pathogen invasion. Unlike traditional clathrin-coated pits, which are transient and dynamic structures involved primarily in the internalization of molecules, clathrin plaques are more stable and extensive, often persisting for extended periods. Their extended lifespan suggests that they serve functions beyond the typical endocytic role, making them integral to various cellular processes. For instance, clathrin plaques are involved in the regulation of intercellular adhesion, allowing cells to better adhere to one another or to the extracellular matrix, which is crucial for tissue formation and maintenance. Furthermore, clathrin plaques act as mechanosensitive hubs, enabling the cell to sense and respond to mechanical stress, a feature that is essential for processes like migration, tissue remodeling, and even cancer progression. Recent discoveries have also highlighted the role of clathrin plaques in cellular signaling. These plaques can serve as scaffolds for signaling molecules, orchestrating the activation of various pathways that govern cellular behavior. For example, the recruitment of actin-binding proteins such as F-actin and vinculin to clathrin plaques can influence cytoskeletal dynamics, helping cells adapt to mechanical changes in their environment. This recruitment also plays a pivotal role in regulating cellular migration, which is crucial for developmental processes. Additionally, clathrin plaques influence receptor-mediated signal transduction by acting as platforms for the assembly of signaling complexes, thereby affecting processes such as growth factor signaling and cellular responses to extracellular stimuli. Despite the growing body of evidence that supports the involvement of clathrin plaques in a wide array of cellular functions, much remains unknown about the precise molecular mechanisms that govern their formation, maintenance, and turnover. For example, the factors that regulate the recruitment of clathrin and other coat proteins to form plaques, as well as the signaling molecules that coordinate plaque dynamics, remain areas of active research. Furthermore, the complex interplay between clathrin plaques and other cellular systems, such as the actin cytoskeleton and integrin-based adhesion complexes, needs further exploration. Studies have shown that clathrin plaques can respond to mechanical forces, with recent findings indicating that they act as mechanosensitive structures that help the cell adapt to changing mechanical environments. This ability underscores the multifunctional nature of clathrin plaques, which, in addition to their role in endocytosis, are involved in cellular processes such as mechanotransduction and adhesion signaling. In summary, clathrin plaques represent a dynamic and versatile component of clathrin-mediated endocytosis. They play an integral role not only in the internalization of macromolecular cargo but also in regulating cellular adhesion, migration, and signal transduction. While much has been learned about their structural and functional properties, significant questions remain regarding the molecular mechanisms that regulate their formation and their broader role in cellular physiology. This review highlights the evolving understanding of clathrin plaques, emphasizing their importance in both endocytosis and a wide range of other cellular functions. Future research is needed to fully elucidate the mechanisms by which clathrin plaques contribute to cellular processes and to better understand their implications for diseases, including cancer and tissue remodeling. Ultimately, clathrin plaques are emerging as crucial hubs that integrate mechanical, biochemical, and signaling inputs, providing new insights into cellular function and the regulation of complex cellular behaviors.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Tumor immunotherapy has emerged as the fourth major therapeutic modality, following surgery, radiotherapy, and chemotherapy. Unlike traditional treatments that primarily target tumor cells directly, immunotherapy harnesses the body’s immune system to recognize and eliminate cancer cells. Over the past decade, various immunotherapeutic strategies have been developed, including immune checkpoint inhibitors (ICIs), chimeric antigen receptor (CAR) T cell therapy, cancer vaccines, and cytokine-based therapies. However, the immunosuppressive tumor microenvironment (TME) poses a significant obstacle to the effectiveness of these treatments. Polyamines—including putrescine, spermidine, and spermine—are polycationic metabolites that often accumulate abnormally in the TME and act as critical immunoregulatory molecules. T cells play a central role in antitumor immunity, yet their function is frequently influenced by immunoregulatory factors within the TME. Elevated polyamine levels in the TME have been implicated in dampening antitumor T cell responses, thereby facilitating tumor immune evasion. Polyamines in the TME originate from both tumor cells and tumor-associated immune cells. Tumor cells often overexpress the oncogene Myc, which drives the upregulation of polyamine biosynthetic enzymes, resulting in excessive intracellular polyamine production. Additionally, M2-polarized tumor-associated macrophages (M2-TAMs) contribute to polyamine accumulation by upregulating arginase-I (Arg-I), an enzyme that catalyzes the conversion of arginine into ornithine—a key precursor in the polyamine biosynthetic pathway. These combined sources lead to sustained polyamine enrichment in the TME, contributing to immune dysfunction and supporting tumor progression. Moreover, polyamines indirectly affect T cell activity by modulating macrophage polarization and directly suppress tumor cell apoptosis, further promoting an immunosuppressive environment. This review highlights the multifaceted roles of polyamines in modulating tumor-infiltrating T cell function, with a particular focus on their influence on CD4+ T cell differentiation, CD8+ T cell cytotoxicity, and immune checkpoint molecule expression. Recent studies suggest that polyamines suppress CD4+ T cell activation and differentiation by modulating the MAPK/ERK signaling pathway. Additionally, polyamines can impair T cell receptor (TCR) signaling and promote immune evasion through the upregulation of PD-L1 expression on tumor cells. These effects collectively contribute to weakened antitumor T cell responses. Polyamine blocking therapy (PBT), which primarily targets polyamine biosynthesis and transport, has emerged as a novel adjunctive immunotherapeutic strategy in cancer treatment. By reducing polyamine levels in the TME, PBT restores T cell effector functions and alleviates immunosuppression. Notably, studies have demonstrated that combining PBT with ICIs produces synergistic antitumor effects and may overcome resistance to ICI monotherapy. Although research has revealed the inhibitory effects of polyamines on T cell immune function, the underlying regulatory mechanisms remain to be fully elucidated. Moreover, due to compensatory mechanisms employed by tumor cells to maintain polyamine homeostasis, multi-targeted approaches may be necessary to achieve safe and effective therapeutic outcomes. Future PBT strategies may benefit from the integration of multi-omics technologies and the development of nanocarrier-based drug delivery systems, which could collectively enhance their specificity, efficacy, and applicability in cancer immunotherapy. This review systematically elucidates the immunomodulatory effects of polyamines on T cell function within the TME and provides theoretical support and novel insights for the advancement of tumor immunotherapeutic strategies.

Lung cancer is the most common malignant tumor worldwide, ranking first in both incidence and mortality rates. According to the latest statistics from the International Agency for Research on Cancer (IARC), approximately 2.5 million new cases and around 1.8 million deaths from lung cancer occurred in 2022, placing a tremendous burden on global healthcare systems. The high mortality rate of lung cancer is closely linked to its subtle early symptoms, which often lead to diagnosis at advanced stages. This not only complicates treatment but also results in substantial economic losses. Current treatment options for lung cancer include surgery, radiotherapy, chemotherapy, targeted drug therapy, and immunotherapy. Among these, immunotherapy has emerged as the most groundbreaking advancement in recent years, owing to its unique antitumor mechanisms and impressive clinical benefits. Unlike traditional therapies such as radiotherapy and chemotherapy, immunotherapy activates or enhances the patient’s immune system to recognize and eliminate tumor cells. It offers advantages such as more durable therapeutic effects and relatively fewer toxic side effects. The main approaches to lung cancer immunotherapy include immune checkpoint inhibitors, tumor-specific antigen-targeted therapies, adoptive cell therapies, cancer vaccines, and oncolytic virus therapies. Among these, immune checkpoint inhibitors and tumor-specific antigen-targeted therapies have received approval from the U.S. Food and Drug Administration (FDA) for clinical use in lung cancer, significantly improving outcomes for patients with advanced non-small cell lung cancer. Although other immunotherapy strategies are still in clinical trials, they show great potential in improving treatment precision and efficacy. This article systematically reviews the latest research progress in lung cancer immunotherapy, including the development of novel immune checkpoint molecules, optimization of treatment strategies, identification of predictive biomarkers, and findings from recent clinical trials. It also discusses the current challenges in the field and outlines future directions, such as the development of next-generation immunotherapeutic agents, exploration of more effective combination regimens, and the establishment of precise efficacy prediction systems. The aim is to provide a valuable reference for the continued advancement of lung cancer immunotherapy.

Eukaryotic translation initiation factor 5A (eIF5A) is the only known protein in eukaryotes that contains a hydroxyputrescine lysine modification. Only the modified form of eIF5A is biologically active and is widely involved in protein translation, mRNA degradation, autophagy, and other intracellular processes. Epithelial-mesenchymal transition (EMT) is a process in which epithelial cells transform into mesenchymal phenotype cells through a highly regulated program. It plays a key role in embryonic development, tissue regeneration, and wound healing. Based on its biological functions, EMT can be classified into three types: I, II, and III. Type III EMT is the core mechanism underlying malignant tumor cell invasion and metastasis. This EMT mechanism involves the canonical pathway induced by transforming growth factor-β (TGF-β) and is regulated by various growth factors (TRAF6, EGF, IGF, HGF, VEGF), transcription factors (Twist, Slug, NF-κB, E12/E47, SIP1, ZEB1, etc.), and signaling pathways such as Wnt/β-catenin and PEAK1. eIF5A can influence tumor cell proliferation, invasion, and metastasis by regulating EMT-related signaling pathways. The known signaling pathways through which eIF5A regulates EMT include the canonical Smad signaling pathway and non-canonical pathways such as Rho/Rac1, Twist, STAT3, and MAT1. Additionally, certain miRNA family members, such as miR-30b, miR-599, and miR-203, can bind to the 3"-UTR of eIF5A2, inhibiting its expression and subsequently suppressing the EMT process in cancer cells, including gastric cancer and colorectal cancer. GC7, an inhibitor targeting the key enzyme DHPS involved in eIF5A modification, has been shown to reverse the EMT mechanism in oral squamous cell carcinoma, lung cancer, and breast cancer by regulating cytokine-mediated signaling pathways, including HIF-1α, STAT3/c-MYC, and Twist. However, to date, no inhibitors directly targeting eIF5A have been developed. In recent years, the mechanism of eIF5A activation catalyzed by DHPS and DOHH has become increasingly clear. As the only protein involved in lysine deoxyhydroxymethylation, DHPS may play a more critical role than eIF5A in the overall signal transduction process. Through in-depth analysis of the DHPS protein structure and its active site, researchers have shifted their approach to DHPS inhibitor development from substrate analog inhibitors (such as GC7, CNI-1493, DHSI-15, etc.) to allosteric inhibitors (11g, 26d, 8m, GL-1, etc.). GC7 is not suitable for clinical trials due to its lack of specificity and low bioavailability, and the therapeutic potential of novel allosteric inhibitors has yet to be clarified. Therefore, there is a significant gap in the development of covalent drugs targeting DHPS for cancer treatment in clinical settings. This paper reviews the research progress on eIF5A in regulating EMT, focusing on the molecular mechanisms by which eIF5A influences tumor cell invasion and migration. It also discusses the characteristics and current limitations of inhibitors targeting the hypusine pathway, aiming to provide insights for studying tumor metastasis mechanisms and drug discovery.

Objective With the continuous evolution of severe acute respiratory syndromes-coronary virus 2 (SARS-CoV-2) Omicron subvariants, particularly the emergence of BA.2.86 and its descendant JN.1, the efficacy of current neutralizing antibodies has faced substantial challenges. The JN.1 variant, noted for its pronounced immune evasion capacity, has rapidly become the globally dominant strain. Elucidating its escape mechanisms is therefore essential to guide the development of next-generation broad-spectrum vaccines and neutralizing antibody therapeutics. This study aimed to investigate the immune evasion mechanisms of JN.1 against broadly neutralizing antibodies, focusing on the effects of key receptor-binding domain (RBD) mutations on antibody binding and neutralization, thereby providing theoretical support for countering ongoing viral evolution.Methods We employed a multidisciplinary approach to systematically assess the binding and neutralizing activities of three broad-spectrum neutralizing antibodies (XGv074, XGv302, and XGv303) against BA.2.86 and JN.1. Binding affinities (KD values) of antibodies to variant RBDs were determined using bio-layer interferometry (BLI). Cryo-electron microscopy (cryo-EM) was used to resolve the structure of the BA.2.86 Spike trimer in complex with antibody antigen-binding fragments (Fabs), achieving a resolution of 3.47 ? for the BA.2.86 S-trimer bound to XGv302. Molecular dynamics simulations and binding free-energy decomposition were conducted to quantify the contributions of key mutations at the antibody–RBD interface. Additionally, sequence alignment and structural modeling were performed to evaluate the role of conformational flexibility in the antibody heavy-chain complementarity-determining region 3 (HCDR3) in mediating tolerance to mutations.Results Experimental data showed that XGv074, XGv302, and XGv303 retained neutralizing activity against BA.2.86 but exhibited markedly reduced binding to JN.1, with only XGv074 maintaining weak neutralization (IC₅₀ = 2.3 mg/L). Cryo-EM structures revealed that all three antibodies targeted the RBD tip, overlapping with the ACE2-binding region. The JN.1-specific L455S mutation disrupted the hydrophobic interaction network between XGv302 and the RBD (involving key residues such as Y421 and L455), resulting in complete loss of neutralization. Binding free-energy decomposition further identified L455 and Y421 as energetic hotspots (ΔG< –3 kcal/mol), with the L455S mutation directly impairing antibody binding. XGv074, owing to greater conformational flexibility in its HCDR3 region, partially tolerated the mutation and retained weak binding. Molecular dynamics simulations showed that the L455S mutation not only eliminated the energetic contribution of this residue but also caused a concurrent decrease in binding free energy of neighboring residues, thereby reducing overall interface stability.Conclusion The JN.1 variant escapes broad-spectrum neutralizing antibodies primarily through the L455S mutation in the RBD, which disrupts energetic hotspots and remodels the antibody-binding interface. Antibody conformational flexibility enhances adaptability to such mutations, providing new insights for broad-spectrum antibody design. These findings highlight the critical roles of epitope energy distribution and antibody flexibility in maintaining neutralization breadth, offering essential guidance for the rational design of next-generation vaccines and antibody therapeutics: specifically, by targeting conserved energetic hotspots while enhancing CDR flexibility to counter immune evasion driven by viral evolution.

Objective N6-methyladenosine (m6A), the most prevalent epigenetic modification in eukaryotic RNA, plays a pivotal role in regulating cellular differentiation and developmental processes, with its dysregulation implicated in diverse pathological conditions. Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development. However, traditional experimental methods are time-consuming and costly. Although various computational approaches have been proposed, challenges remain in feature learning, predictive accuracy, and generalization. Here, we present m6A-PSRA, a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways. The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity. This ensures that the biological context conveyed by nucleotide order is retained. A bidirectional long short-term memory network (BiLSTM) then processes the encoded matrix, capturing both forward and backward dependencies between bases to resolve contextual correlations. The second branch employs a k-mer tokenization strategy (k=3), decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns. A pre-trained Doc2vec model maps these subsequences into fixed-dimensional vectors, reducing feature dimensionality while extracting latent global semantic information via context learning. Both branches integrate residual networks (ResNet) and a self-attention mechanism: ResNet mitigates vanishing gradients through skip connections, preserving feature integrity, while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction. This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans, mice, and rats, m6A-PSRA consistently outperformed existing methods in accuracy (ACC) and area under the curve (AUC), achieving 90% ACC and 95% AUC in every tissue tested, indicating strong cross-species and cross-tissue adaptability. Validation on independent datasets—including three human cell lines (MOLM1, HEK293, A549) and a long-sequence dataset (m6A_IND, 1 001 nt)—confirmed stable performance across varied biological contexts and sequence lengths. Ablation studies demonstrated that the dual-branch architecture, residual network, and self-attention mechanism each contribute critically to performance, with their combination reducing interference between pathways. Motif analysis revealed an enrichment of m6A sites in guanine (G) and cytosine (C), consistent with known regulatory patterns, supporting the model"s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features, achieving high prediction accuracy and robust generalization across tissues and species, providing an efficient computational tool for m6A methylation site prediction.

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder affecting a substantial proportion of women of reproductive age. It is frequently associated with ovulatory dysfunction, infertility, and an increased risk of chronic metabolic diseases. A hallmark pathological feature of PCOS is the arrest of follicular development, closely linked to impaired intercellular communication between the oocyte and surrounding granulosa cells. Transzonal projections (TZPs) are specialized cytoplasmic extensions derived from granulosa cells that penetrate the zona pellucida to establish direct contact with the oocyte. These structures serve as essential conduits for the transfer of metabolites, signaling molecules (e.g., cAMP, cGMP), and regulatory factors (e.g., microRNAs, growth differentiation factors), thereby maintaining meiotic arrest, facilitating metabolic cooperation, and supporting gene expression regulation in the oocyte. The proper formation and maintenance of TZPs depend on the cytoskeletal integrity of granulosa cells and the regulated expression of key connexins, particularly CX37 and CX43. Recent studies have revealed that in PCOS, TZPs exhibit significant structural and functional abnormalities. Contributing factors—such as hyperandrogenism, insulin resistance, oxidative stress, chronic inflammation, and dysregulation of critical signaling pathways (including PI3K/Akt, Wnt/β-catenin, and MAPK/ERK)—collectively impair TZP integrity and reduce their formation. This disruption in granulosa-oocyte communication compromises oocyte quality and contributes to follicular arrest and anovulation. This review provides a comprehensive overview of TZP biology, including their formation mechanisms, molecular composition, and stage-specific dynamics during folliculogenesis. We highlight the pathological alterations in TZPs observed in PCOS and elucidate how endocrine and metabolic disturbances—particularly androgen excess and hyperinsulinemia—downregulate CX43 expression and impair gap junction function, thereby exacerbating ovarian microenvironmental dysfunction. Furthermore, we explore emerging therapeutic strategies aimed at preserving or restoring TZP integrity. Anti-androgen therapies (e.g., spironolactone, flutamide), insulin sensitizers (e.g., metformin), and GLP-1 receptor agonists (e.g., liraglutide) have shown potential in modulating connexin expression and enhancing granulosa-oocyte communication. In addition, agents such as melatonin, AMPK activators, and GDF9/BMP15 analogs may promote TZP formation and improve oocyte competence. Advanced technologies, including ovarian organoid models and CRISPR-based gene editing, offer promising platforms for studying TZP regulation and developing targeted interventions. In summary, TZPs are indispensable for maintaining follicular homeostasis, and their disruption plays a pivotal role in the pathogenesis of PCOS-related folliculogenesis failure. Targeting TZP integrity represents a promising therapeutic avenue in PCOS management and warrants further mechanistic and translational investigation.

Endometriosis (EM) and adenomyosis (AM) are chronic, estrogen-dependent gynecological disorders that significantly impair the quality of life and reproductive health of millions of women worldwide. Clinically, both conditions are characterized by dysmenorrhea, abnormal uterine bleeding, infertility, and high recurrence rates. Despite decades of research, their pathogenesis remains incompletely understood, and current therapeutic options are limited in both efficacy and long-term safety. Emerging studies have identified glycolytic metabolic reprogramming (GMR)—a shift from mitochondrial oxidative phosphorylation (OXPHOS) to aerobic glycolysis—as a unifying and critical feature in the development and progression of EM and AM. In ectopic lesions, enhanced glycolysis supports cellular proliferation, survival, and adaptation to hypoxic microenvironments. Key glycolytic enzymes, including hexokinase 2 (HK2), phosphofructokinase-1 (PFK1), pyruvate dehydrogenase kinase (PDK), and lactate dehydrogenase A (LDHA), are markedly upregulated, whereas oxidative metabolism is suppressed, reflecting a Warburg-like metabolic phenotype. Notably, single-cell and spatial transcriptomic analyses reveal significant heterogeneity between EM and AM lesions. EM lesions often contain cell clusters co-expressing glycolytic and OXPHOS-related genes, suggesting metabolic flexibility. In contrast, AM tissues exhibit a more uniform, glycolysis-dominant profile, with preferential HK2 expression over HK1—potentially linked to defective repair of the endometrial basal layer. Multiple regulatory layers contribute to this glycolytic shift. Hypoxia-inducible factors (HIFs) act as upstream transcriptional activators in response to oxygen deprivation. Kinase cascades, such as those involving PIM2 and AURKA, enhance glycolytic enzyme activity via phosphorylation. Epigenetic mechanisms—including N6-methyladenosine (m6A) RNA modification and histone H3K18 lactylation—further stabilize glycolytic gene expression and reinforce metabolic reprogramming. These alterations form an integrated regulatory network that sustains high glycolytic flux in ectopic cells. Importantly, GMR profoundly affects the immune microenvironment. Lactate produced by glycolytic stromal cells promotes M2 macrophage polarization and impairs the function of cytotoxic T cells and dendritic cells, leading to immune evasion and chronic inflammation. Meanwhile, immune cells themselves undergo metabolic reprogramming, exhibiting increased dependence on glycolysis and diminished oxidative capacity. This bidirectional metabolic-immune feedback loop facilitates lesion persistence and disease progression. GMR is also closely linked to infertility in EM and AM. In the ovarian microenvironment, glycolytic imbalance leads to lactate accumulation in follicular fluid, negatively affecting oocyte quality and embryo development. In the endometrium, excessive glycolysis disrupts decidualization, angiogenesis, and immune tolerance—processes essential for implantation and pregnancy. Targeting glycolysis offers promising therapeutic potential. Small-molecule inhibitors such as dichloroacetate and meclozine target PDK and HK2, respectively. Natural compounds like cinnamic acid and protoberberine derivatives exhibit both anti-glycolytic and anti-inflammatory effects. Traditional Chinese medicine formulations, including Guizhi Fuling Wan, have shown efficacy in modulating metabolism, vascular remodeling, and fibrosis. Combination therapies, such as atorvastatin with resveratrol, may provide synergistic benefits by inhibiting both glucose uptake and lactate export. In conclusion, glycolytic metabolic reprogramming is a central mechanism linking inflammation, immune dysfunction, lesion progression, and reproductive failure in endometriotic diseases. Future research should focus on identifying metabolic subtypes, developing combined metabolic-immune therapies, and evaluating the safety of these treatments in reproductive-age women. These insights may pave the way toward personalized, mechanism-driven interventions for EM and AM.

Diabetic cardiomyopathy is a distinct form of cardiomyopathy that can lead to heart failure, arrhythmias, cardiogenic shock, and sudden death. It has become a major cause of mortality in diabetic patients. The pathogenesis of diabetic cardiomyopathy is complex, involving increased oxidative stress, activation of inflammatory responses, disturbances in glucose and lipid metabolism, accumulation of advanced glycation end products (AGEs), abnormal autophagy and apoptosis, insulin resistance, and impaired intracellular Ca²⁺ homeostasis. Recent studies have shown that adenosine monophosphate-activated protein kinase (AMPK) plays a crucial protective role by lowering blood glucose levels, promoting lipolysis, inhibiting lipid synthesis, and exerting antioxidant, anti-inflammatory, anti-apoptotic, and anti-ferroptotic effects. It also enhances autophagy, thereby alleviating myocardial injury under hyperglycemic conditions. Consequently, AMPK is considered a key protective factor in diabetic cardiomyopathy. As part of diabetes prevention and treatment strategies, both pharmacological and exercise interventions have been shown to mitigate diabetic cardiomyopathy by modulating the AMPK signaling pathway. However, the precise regulatory mechanisms, optimal intervention strategies, and clinical translation require further investigation. This review summarizes the role of AMPK in the prevention and treatment of diabetic cardiomyopathy through drug and/or exercise interventions, aiming to provide a reference for the development and application of AMPK-targeted therapies. First, several classical AMPK activators (e.g., AICAR, A-769662, O-304, and metformin) have been shown to enhance autophagy and glucose uptake while inhibiting oxidative stress and inflammatory responses by increasing the phosphorylation of AMPK and its downstream target, mammalian target of rapamycin (mTOR), and/or by upregulating the gene expression of glucose transporters GLUT1 and GLUT4. Second, many antidiabetic agents (e.g., teneligliptin, liraglutide, exenatide, semaglutide, canagliflozin, dapagliflozin, and empagliflozin) can promote autophagy, reverse excessive apoptosis and autophagy, and alleviate oxidative stress and inflammation by enhancing AMPK phosphorylation and its downstream targets, such as mTOR, or by increasing the expression of silent information regulator 1 (SIRT1) and peroxisome proliferator-activated receptor-α (PPAR-α). Third, certain anti-anginal (e.g., trimetazidine, nicorandil), anti-asthmatic (e.g., farrerol), antibacterial (e.g., sodium houttuyfonate), and antibiotic (e.g., minocycline) agents have been shown to promote autophagy/mitophagy, mitochondrial biogenesis, and inhibit oxidative stress and lipid accumulation via AMPK phosphorylation and its downstream targets such as protein kinase B (PKB/AKT) and/or PPAR-α. Fourth, natural compounds (e.g., dihydromyricetin, quercetin, resveratrol, berberine, platycodin D, asiaticoside, cinnamaldehyde, and icariin) can upregulate AMPK phosphorylation and downstream targets such as AKT, mTOR, and/or the expression of nuclear factor erythroid 2-related factor 2 (Nrf2), thereby exerting anti-inflammatory, anti-apoptotic, anti-pyroptotic, antioxidant, and pro-autophagic effects. Fifth, moderate exercise (e.g., continuous or intermittent aerobic exercise, aerobic combined with resistance training, or high-intensity interval training) can activate AMPK and its downstream targets (e.g., acetyl-CoA carboxylase (ACC), GLUT4, PPARγ coactivator-1α (PGC-1α), PPAR-α, and forkhead box protein O3 (FOXO3)) to promote fatty acid oxidation and glucose uptake, and to inhibit oxidative stress and excessive mitochondrial fission. Finally, the combination of liraglutide and aerobic interval training has been shown to activate the AMPK/FOXO1 pathway, thereby reducing excessive myocardial fatty acid uptake and oxidation. This combination therapy offers superior improvement in cardiac dysfunction, myocardial hypertrophy, and fibrosis in diabetic conditions compared to liraglutide or exercise alone.

The innate immune system serves as the body"s first line of defense against pathogens and plays a central role in inflammation regulation, immune homeostasis, and tumor immunosurveillance. In recent years, with the growing recognition of the concept "exercise is medicine," increasing attention has been paid to the immunoregulatory effects of physical activity. Accumulating evidence suggests that regular, moderate-intensity exercise significantly enhances innate immunity by strengthening the skin–mucosal barrier, increasing levels of secretory immunoglobulin A (sIgA), and improving the functional capacity of key immune cells such as natural killer (NK) cells, neutrophils, macrophages, and dendritic cells. It also modulates the complement system and various inflammatory mediators. This review comprehensively summarizes the effects of exercise on each component of the innate immune system and highlights the underlying molecular mechanisms, including activation of AMP-activated protein kinase (AMPK), inhibition of nuclear factor-kappa B (NF-κB), enhancement of mitochondrial function via the PGC-1α/TFAM axis, and initiation of autophagy through the ULK1/mTOR pathway. Emerging mechanisms are also discussed, such as exercise-induced epigenetic modifications (e.g., histone acetylation and miRNA regulation), modulation of the gut microbiota, and metabolite-mediated immune programming (e.g., short-chain fatty acids [SCFAs], β-hydroxybutyrate). The effects of exercise on innate immunity vary considerably among individuals, depending on factors such as age, sex, and comorbidities. For example, adolescents exhibit enhanced NK cell mobilization, whereas older adults benefit from reduced chronic inflammation and immune aging. Sex hormones and metabolic conditions (e.g., obesity, diabetes, chronic obstructive pulmonary disease, cancer) further modulate the immune response to exercise. Based on these insights, we propose a personalized approach to exercise prescription guided by the FITT principle (Frequency, Intensity, Time, and Type), aiming to optimize immune outcomes across diverse populations. Importantly, given the dual role of exercise in immune activation and regulation, caution is warranted: while moderate exercise enhances immune defense, excessive or high-intensity activity may induce transient immunosuppression. In pathological contexts such as infection, autoimmune diseases, or tissue injury, exercise intensity and timing must be carefully adjusted. This review provides practical guidelines for exercise-based immune modulation and underscores the need for dose–response studies and advancements in precision exercise medicine. In conclusion, exercise represents a safe and effective strategy for enhancing innate immune function and mitigating chronic inflammatory diseases.

TFIIB-related factor 1 (BRF1) is an important transcription factor. It specifically regulates the transcription of RNA polymerase III-dependent genes (RNA Pol III genes). The products of these genes are some small non-coding RNAs, including transfer RNAs (tRNAs) and 5S ribosomal RNAs (5S rRNA). The transcription levels of tRNAs and 5S rRNA vary with changes in intracellular BRF1 amounts. tRNAs and 5S rRNA play a crucial role in determining protein synthesis. Studies have demonstrated that dysregulation of tRNAs and 5S rRNA is closely related to cell growth, proliferation, transformation, and even tumorigenesis. BRF1 is a key factor determining the generation of tRNAs and 5S rRNA. Increasing BRF1 expression enhances cell proliferation and transformation, promoting tumor development. In contrast, repressing BRF1 activity decreases the rates of cell proliferation and transformation, and inhibits tumor growth. High levels of BRF1 are found in the samples of patients suffering from hepatocellular carcinoma, breast cancer, gastric carcinoma, lung cancer, prostate carcinoma, and other cancers. It indicates that high levels of BRF1 are closely related to the occurrence of human cancer and may be a common landmark of tumors. But there is discrepancy in the regulatory mechanisms and signaling pathways of BRF1 overexpression in different cancers. In general, high levels of BRF1 in patients suffering from cancer show short survival period and poor prognosis. However, there is one exception, namely breast cancer. Approximate 80% of cases of breast cancer are estrogen receptor-positive (ER+) and 20% are ER-. The cases with high levels of BRF1 reveal longer survival period and better prognosis after they accepted the hormone treatment by Tamoxifen (Tam), compared to the cases with low level BRF1. It seems like a contradiction. Most of the cases with high levels of BRF1 belong to ER+ status. Tam has been used to treat ER+ cases of breast cancer after diagnosis and surgery. Thus, hormone therapy, such as Tam, is more effective on these patients. This is because, on one hand, that Tam competes with E2 (17β-estradiol) to bind to estrogen receptor α (ERα), but does not dissociate to occupy the receptors, blocking E2 binding to this receptor and inhibiting its biological effects. On other hand, Tam can inhibit the expression of BRF1, leading to a decline of intracellular BRF1 levels. Therefore, the actual levels of BRF1 are lower in the patients with ER+ breast cancer. It appears the prognosis of the BRF1 high expression cases better than that of the low. Myocardial hypertrophy manifests magnification of cardiomyocyte volume rather than number increasing in the postnatal heart. Myocardial hypertrophy is a critical risk factor underlying cardiovascular diseases. No matter how myocardial hypertrophy occur, it will ultimately lead to myocardial dysfunction and heart failure. Hypertrophic growth of cardiomyocytes requires a large amount of protein synthesis to meet its needs of cardiomyocyte growth. Animal models and cell experiments have shown that myocardial hypertrophy stimulates a significant increase in BRF1 expression and transcription of tRNAs and 5S rRNA. Interestingly, elevated levels of BRF1 are found in the myocardium tissues of patients with myocardial hypertrophy. These studies demonstrate that BRF1 indeed plays a critical role in myocardial hypertrophy. In summary, high levels of BRF1 are found in patients suffering from different cancers and myocardial hypertrophy. It implies that BRF1 is a promising biological target of cancer and cardiomyopathy. BRF1 is expected to become a common biomarker for early diagnosis and prognostic observation of different human cancers. It is also an important biomarker for the diagnosis and treatment of cardiomyopathy. BRF1 not only holds an important position in the field of basic medical research but also has great prospects for translational medicine. In the present article, we summarize the progress on studies of BRF1 expressions in cancer and cardiomyopathy, proposes future research directions. It is a new research area. Here, we emphasize the significancy of BRF overexpression in the two huge diseases of human, cancer and cardiomyopathy to raise people"s attention to this field.

Neurodegenerative diseases (NDs) are a wide variety of disorders characterized by the progressive and irreversible loss of neuronal structure and functions leading to cognitive impairments. The common types of NDs include Alzheimer"s disease, amyotrophic lateral sclerosis, Huntington"s disease, and Parkinson"s disease. The sharing pathological hallmarks of these diseases are the aberrant aggregation and amyloid deposition. However, the underlying molecular mechanisms of protein misfolding and aberrant aggregation remain elusive. Amyloid protein is prone to aggregate from its native disordered monomeric state into well-ordered amyloid fibril state via nucleation-dependent polymerization mechanism, in which follows sigmoidal growth kinetics with three steps: lag phase, growth phase, and plateau phase. The formation and subsequent distribution of these pathological amyloid fibrils are closely related to the onset and progression of NDs. Additionally, the aberrant aggregation of these disease-associated proteins proceeds via liquid-liquid phase separation (LLPS) and liquid-to-solid phase transition (LSPT) leading to amyloid fibril formation in the condensed phase. The phase transition from liquid-like droplets or dynamic condensates to solid-like hydrogel or amyloids is intimately linked to the pathogenesis of several NDs. In this review, we discuss two typical pathways of amyloid fibrils formation. One route involves aggregation in the bulk solution environment, proceeding via nucleation and elongation steps to form amyloid fibrils. In this scenario, protein aggregation initiates with the nucleation step to form oligomeric nuclei. Then the nuclei serve as templates for the subsequent elongation step ultimately leading to the formation of amyloid fibrils. When sufficient fibrils have formed during self-assembly, the secondary nucleation is triggered to generate new species of oligomers and fibrillar aggregates. The other route of fibril formation occurs in the condensed phase through LLPS and LSPT to form amyloid aggregates and deposits. The occurrence of a phase separation leads to the liquid-like droplets formation during the early stage of aggregation. Over time, these dynamic biomolecular condensates gradually solidify and ultimately evolve into a hydrogel state enriched by amyloid aggregates through a phase transition process. Evidence indicates that pathological phase transitions are early events in the pathogenesis of several NDs. It should be noted that these two routes are not independent or mutually exclusive. They are interconnected and function cooperatively during aberrant aggregation. The pathological progression of NDs is closely related to the dominant aggregation pathway involved in aberrant aggregation. Moreover, the molecular mechanisms underlying the formation of pathogenic amyloid deposits are intricately linked to the structural and functional characteristics of aggregates. These aggregates may not only directly participate in fibrillization, but also indirectly promote the development of NDs by affecting the normal physiological cellular functions. Therefore, in-depth research on the structural and functional properties of both intermediates and fibrils is of great significance for understanding the molecular mechanisms of protein misfolding and aberrant aggregation. Overall, this paper reviews the amyloid deposition and pathological phase transitions in NDs. By delving into the molecular mechanisms of amyloid fibrillization, the aim is to better understand the pathogenesis of NDs, and to provide valuable insights into the development of therapeutic strategies targeting amyloid aggregation and aberrant phase transition.

The emergence of the clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated proteins (Cas) system represents a revolutionary paradigm shift in molecular diagnostics, offering transformative potential for RNA analysis within the rigorous demands of forensic science. Conventional forensic RNA detection methodologies, such as reverse transcription-quantitative polymerase chain reaction (RT-qPCR) or microarray analysis, are significantly hampered by inherent limitations including complex, multi-step protocols requiring sophisticated laboratory infrastructure, pronounced susceptibility to inhibitors prevalent in complex forensic matrices (e.g., humic acids, heme, indigo dyes), and often inadequate sensitivity for trace or degraded samples typical of crime scenes, thereby failing to meet the critical operational imperatives of forensic practice: rapidity, high specificity, sensitivity, portability, and robustness against interference. This review posits that CRISPR-Cas-based RNA detection technology provides a groundbreaking solution by leveraging the programmable, sequence-specific recognition conferred by the synergistic interaction between a designed guide RNA (gRNA) and Cas effector proteins (e.g., Cas12a, Cas13a, Cas14). Upon target RNA binding, specific Cas enzymes undergo conformational activation, exhibiting collateral cleavage activity―a unique catalytic amplification mechanism where the enzyme non-specifically cleaves surrounding reporter molecules, enabling ultra-high sensitivity. To further enhance detection limits, CRISPR-Cas systems are strategically integrated with isothermal pre-amplification techniques like recombinase polymerase amplification (RPA) or loop-mediated isothermal amplification (LAMP), which efficiently amplify target RNA at constant temperatures, eliminating the need for thermal cyclers. This powerful cascade―isothermal pre-amplification followed by CRISPR-mediated sequence-specific recognition and collateral signal amplification―achieves exceptional sensitivity, often down to the single-molecule (attomolar) level, while drastically reducing analysis time to potentially 30-60 min. Crucially, the compatibility of CRISPR-Cas detection with simple, equipment-free readout systems, such as lateral flow strips (LFS) for visual colorimetric results or portable fluorescence/electrochemical sensors, facilitates true point-of-need (PON) forensic analysis directly at crime scenes, morgues, or field labs. This enables rapid applications like specific body fluid identification (e.g., distinguishing menstrual blood via miRNA, identifying saliva via mRNA), post-mortem interval (PMI) estimation through RNA degradation/expression patterns, donor age inference via age-related RNA markers, tissue identification, and microbial forensics, thereby accelerating investigative leads, minimizing sample degradation risks, and optimizing resource allocation. However, significant challenges impede widespread adoption, including persistent environmental interference inhibiting enzymes, fluctuations in Cas/amplification enzyme activity affecting reproducibility, a critical lack of standardized protocols and validated quality assurance/quality control (QA/QC) frameworks essential for forensic reliability and court admissibility, and current limitations in multiplex detection capability. Consequently, future research must prioritize overcoming multiplexing bottlenecks for comprehensive analysis, enhancing system robustness through Cas protein engineering and optimized reagents, developing fully integrated, sample-to-answer microfluidic or lateral flow devices for user-friendly field deployment, and collaboratively establishing universally accepted validation guidelines, performance standards, and stringent QA/QC procedures. Furthermore, the urgent development of clear ethical guidelines governing the use of this highly sensitive technology, particularly concerning RNA data privacy and potential misuse, is imperative. This review systematically outlines the principles, forensic applications, current limitations, and future trajectories of CRISPR-RNA detection, with the authors" conviction that focused efforts addressing these challenges will translate this technology into a cornerstone of next-generation forensic practice, driving unprecedented efficiency and innovation in field investigations and laboratory analysis to enhance justice delivery.

Sleep deprivation (SD) has emerged as a significant modifiable risk factor for Alzheimer’s disease (AD), with mounting evidence demonstrating its multifaceted role in accelerating AD pathogenesis through diverse molecular, cellular, and systemic mechanisms. Firstly, SD is refined within the broader spectrum of sleep-wake and circadian disruption, emphasizing that both acute total sleep loss and chronic sleep restriction destabilize the homeostatic and circadian processes governing glymphatic clearance of neurotoxic proteins. During normal sleep, concentrations of interstitial Aβ and tau fall as cerebrospinal fluid oscillations flush extracellular waste; SD abolishes this rhythm, causing overnight rises in soluble Aβ and tau species in rodent hippocampus and human CSF. Orexinergic neurons sustain arousal, and become hyperactive under SD, further delaying sleep onset and amplifying Aβ production. At the molecular level, SD disrupts Aβ homeostasis through multiple converging pathways, including enhanced production via β-secretase (BACE1) upregulation, coupled with impaired clearance mechanisms involving the glymphatic system dysfunction and reduced Aβ-degrading enzymes (neprilysin and insulin-degrading enzyme). Cellular and histological analyses revealed that these proteinopathies are significantly exacerbated by SD-induced neuroinflammatory cascades characterized by microglial overactivation, astrocyte reactivity, and sustained elevation of pro-inflammatory cytokines (IL-1β, TNF-α, IL-6) through NF-κB signaling and NLRP3 inflammasome activation, creating a self-perpetuating cycle of neurotoxicity. The synaptic and neuronal consequences of chronic SD are particularly profound and potentially irreversible, featuring reduced expression of critical synaptic markers (PSD95, synaptophysin), impaired long-term potentiation (LTP), dendritic spine loss, and diminished neurotrophic support, especially brain-derived neurotrophic factor (BDNF) depletion, which collectively contribute to progressive cognitive decline and memory deficits. Mechanistic investigations identify three core pathways through which SD exerts its neurodegenerative effects: circadian rhythm disruption via BMAL1 suppression, orexin system hyperactivity leading to sustained wakefulness and metabolic stress, and oxidative stress accumulation through mitochondrial dysfunction and reactive oxygen species overproduction. The review critically evaluates promising therapeutic interventions including pharmacological approaches (melatonin, dual orexin receptor antagonists), metabolic strategies (ketogenic diets, and Mediterranean diets rich in omega-3 fatty acids), lifestyle modifications (targeted exercise regimens, cognitive behavioral therapy for insomnia), and emerging technologies (non-invasive photobiomodulation, transcranial magnetic stimulation). Current research limitations include insufficient understanding of dose-response relationships between SD duration/intensity and AD pathology progression, lack of long-term longitudinal clinical data in genetically vulnerable populations (particularly APOE ε4 carriers and those with familial AD mutations), the absence of standardized SD protocols across experimental models that accurately mimic human chronic sleep restriction patterns, and limited investigation of sex differences in SD-induced AD risk. The accumulated evidence underscores the importance of addressing sleep disturbances as part of multimodal AD prevention strategies and highlights the urgent need for clinical trials evaluating sleep-focused interventions in at-risk populations. The review proposes future directions focused on translating mechanistic insights into precision medicine approaches, emphasizing the need for biomarkers to identify SD-vulnerable individuals, chronotherapeutic strategies aligned with circadian biology, and multi-omics integration across sleep, proteostasis and immune profiles may delineate precision-medicine strategies for at-risk populations. By systematically examining these critical connections, this analysis positions sleep quality optimization as a viable strategy for AD prevention and early intervention while providing a comprehensive roadmap for future mechanistic and interventional research in this rapidly evolving field.

Perceptual decision making refers to the process by which individuals make choices and judgments based on sensory information, serving as a fundamental ability for human adaptation to complex environments. While traditional research has focused on perceptual decision making in isolated contexts, growing evidence highlights the profound influence of social contexts prevalent in real-world scenarios. As a crucial factor supporting individual survival and development, social context not only provides rich information sources but also shapes perceptual decision making through top-down processing mechanisms, prompting researchers to recognize the inherently social nature of human decisions. Empirical studies have demonstrated that social information, such as others" choices or group norms, can systematically bias individuals" perceptual decisions, often manifesting as conformity behaviors. Social influence can also facilitate performance under certain conditions, particularly when individuals can accurately identify and adopt high-quality social information. The impact of social context on perceptual decisions is modulated by a variety of external and internal factors, including group characteristics (e.g., group size, response consistency), attributes of peers (e.g., familiarity, social status, distinctions between human and artificial partners), as well as individual differences such as confidence, personality traits, and developmental stage. The motivations driving social influence encompass three primary mechanisms: improving decision accuracy through informational influence, gaining social acceptance through normative influence, and maintaining positive self-concept. Recent computational approaches have employed diverse theoretical frameworks to provide valuable insights into the cognitive mechanisms underlying social influence in perceptual decision making. Reinforcement learning models demonstrate how social feedback shapes future choices through reward-based updating. Bayesian inference frameworks describe how individuals integrate personal beliefs with social information based on their respective reliabilities, dynamically updating beliefs to optimize decisions under uncertainty. Drift diffusion models offer powerful tools to decompose social influence into distinct cognitive components, allowing researchers to differentiate between changes in perceptual processing and shifts in decision criteria. Collectively, these models establish a comprehensive methodological foundation for disentangling the multiple pathways by which social context shapes perceptual decisions. Neuroimaging and electrophysiological studies provide converging evidence that social context influences perceptual decision making through multi-level neural mechanisms. At early perceptual processing stages, social influence modulates sensory evidence accumulation in parietal cortex and directly alters primary visual cortex activity, while guiding selective attention to stimulus features consistent with social norms through attentional alignment mechanisms. At higher cognitive levels, the reward system (ventral striatum, ventromedial prefrontal cortex) is activated during group-consistent decisions; emotion-processing networks (anterior cingulate cortex, insula, amygdala) regulate experiences of social acceptance and rejection; and mentalizing-related brain regions (dorsomedial prefrontal cortex, temporoparietal junction) support inference of others" mental states and social information integration. These neural circuits work synergistically to achieve top-down multi-level modulation of perceptual decision making. Understanding the mechanisms by which social context shapes perceptual decision making has broad theoretical and practical implications. These insights inform the optimization of collective decision-making, the design of socially adaptive human-computer interaction systems, and interventions for cognitive disorders such as autism spectrum disorder and anorexia nervosa. Future studies should combine computational modeling and neuroimaging approaches to systematically investigate the multi-level and dynamic nature of social influences on perceptual decision making.

Schizophrenia is a severe psychiatric disorder characterized by positive symptoms (e.g., hallucinations), negative symptoms (e.g., social withdrawal), and cognitive impairments. Among these, cognitive impairment is a core feature that severely compromises patients’ social functioning and long-term prognosis. Antipsychotics, the first-line treatment for schizophrenia, are generally effective in managing positive symptoms. However, their efficacy in alleviating negative symptoms and cognitive deficits remains limited. Moreover, long-term use may lead to metabolic syndrome and extrapyramidal side effects. Consequently, non-pharmacological interventions have garnered increasing attention as alternative or adjunctive strategies for cognitive remediation in schizophrenia. In recent years, techniques grounded in neuroplasticity theory have advanced rapidly. These interventions aim to alleviate cognitive impairments by modulating neural circuits (e.g., enhancing prefrontal-hippocampal connectivity) and synaptic plasticity (e.g., modulating the BDNF/TrkB pathway) from multiple dimensions. Such approaches not only enhance cognitive function but also reduce medication-related adverse effects and improve treatment compliance. This article comprehensively reviews the clinical evidence and recent technological advances in non-pharmacological interventions targeting cognitive impairments in schizophrenia. The interventions discussed include cognitive remediation therapy (CRT), repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), electro-acupuncture (EA), aerobic exercise (AE), and light therapy (LT). CRT, the most extensively studied and evidence-based intervention, uses structured cognitive training tasks to enhance neuroplasticity and has consistently demonstrated efficacy in improving executive function and social cognition. Both rTMS and tDCS are non-invasive brain stimulation techniques that modulate cortical excitability and neural network connectivity. While rTMS has shown promise in improving working memory and attention—particularly in patients with prominent negative symptoms—its clinical efficacy remains inconsistent, likely due to variability in stimulation parameters and patient heterogeneity. In contrast, tDCS has demonstrated encouraging effects on working memory and attention with a relatively rapid onset, although optimal stimulation protocols have yet to be standardized. EA, which combines traditional acupuncture with electrical stimulation, has been shown to improve memory function, possibly through upregulation of brain-derived neurotrophic factor (BDNF) and enhanced cerebral blood flow. It may be especially useful in treatment-resistant cases. AE is a low-cost and widely accessible intervention that promotes hippocampal neuroplasticity and BDNF expression, thereby improving memory and attention. It is recommended as a foundational adjunctive therapy, particularly for patients with chronic schizophrenia. LT, although still experimental, has yielded promising results in animal models by modulating neuroinflammation and enhancing neurogenesis via the BDNF/CREB signaling pathway. However, clinical evidence remains limited, necessitating further large-scale trials to validate its efficacy and safety. In addition to reviewing individual interventions, this article highlights the potential of combination strategies—such as CRT combined with AE or rTMS—to produce synergistic cognitive benefits. Future directions include the development of personalized treatment protocols, early intervention during neurodevelopmental windows (e.g., adolescence), and the integration of biomarkers and neuroimaging to guide therapeutic decisions. This synthesis aims to provide clinicians and researchers with a comprehensive framework for advancing non-pharmacological cognitive rehabilitation in schizophrenia.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline, functional impairment, and neuropsychiatric symptoms. It represents the most prevalent form of dementia among the elderly population. Accumulating evidence indicates that oxidative stress plays a pivotal role in the pathogenesis of AD. Notably, elevated levels of oxidative stress have been observed in the brains of AD patients, where excessive reactive oxygen species (ROS) can cause extensive damage to lipids, proteins, and DNA, ultimately compromising neuronal structure and function. Amyloid β-protein (Aβ) has been shown to induce mitochondrial dysfunction and calcium overload, thereby promoting the generation of ROS. This, in turn, exacerbates Aβ aggregation and enhances tau phosphorylation, leading to the formation of two pathological features of AD: extracellular Aβ plaque deposition and intracellular neurofibrillary tangles (NFTs). These events ultimately culminate in neuronal death, forming a vicious cycle. The interplay between oxidative stress and these pathological processes constitutes a core link in the pathogenesis of AD. The signaling pathways mediating oxidative stress in AD include Nrf2, RCAN1, PP2A, CREB, Notch1, NF-κB, ApoE, and ferroptosis. Nrf2 signaling pathway serves as a key regulator of cellular redox homeostasis, exerts important antioxidant capacity and protective effects in AD. RCAN1 signaling pathway, as a calcineurin inhibitor, and modulates AD progression through multiple mechanisms. PP2A signaling pathway is involved in regulating tau phosphorylation and neuroinflammation processes. CREB signaling pathway contributes to neuroplasticity and memory formation; activation of CREB improves cognitive function and reduce oxidative stress. Notch1 signaling pathway regulates neuronal development and memory, participates in modulation of Aβ production, and interacts with Nrf2 to co-regulate antioxidant activity. NF-κB signaling pathway governs immune and inflammatory responses; sustained activation of this pathway forms “inflammatory memory”, thereby exacerbating AD pathology. ApoE signaling pathway is associated with lipid metabolism; among its isoforms, ApoE-ε4 significantly increases the risk of AD, leading to elevated oxidative stress, abnormal lipid metabolism, and neuroinflammation. The ferroptosis signaling pathway is driven by iron-dependent lipid peroxidation, and the subsequent release of lipid peroxidation products and ROS exacerbate oxidative stress and neuronal damage. These interconnected pathways form a complex regulatory network that regulates the progression of AD through oxidative stress and related pathological cascades. In terms of therapeutic strategies targeting oxidative stress, among the drugs currently used in clinical practice for AD treatment, memantine and donepezil demonstrate significant therapeutic efficacy and can improve the level of oxidative stress in AD patients. Some compounds with antioxidant effects (such as α-lipoic acid and melatonin) have shown certain potential in AD treatment research and can be used as dietary supplements to ameliorate AD symptoms. In addition, non-drug interventions such as calorie restriction and exercise have been proven to exerted neuroprotective effects and have a positive effect on the treatment of AD. By comprehensively utilizing the therapeutic characteristics of different signaling pathways, it is expected that more comprehensive multi-target combination therapy regimens and combined nanomolecular delivery systems will be developed in the future to bypass the blood-brain barrier, providing more effective therapeutic strategies for AD.

Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy worldwide, accounting for more than 90% of all oral cancers, and is characterized by high invasiveness and poor long-term prognosis. Its etiology is multifactorial, involving tobacco use, alcohol consumption, and human papillomavirus (HPV) infection. Oral leukoplakia and erythroplakia are the most frequent premalignant lesions, with oral leukoplakia being the most common. Both OSCC and premalignant lesions are closely associated with aberrant activation of multiple signaling pathways. Post-translational modifications (such as ubiquitination and deubiquitination) play key roles in regulating these pathways by controlling protein stability and activity. Growing evidence indicates that dysregulated ubiquitination/deubiquitination can mediate OSCC initiation and progression via aberrant activation of signaling pathways. The ubiquitination/deubiquitination process mainly involves E3 ligases (E3s) that catalyze substrate ubiquitination, deubiquitinating enzymes (DUBs) that remove ubiquitin chains, and the 26S proteasome complex that degrades ubiquitinated substrates. Abnormal expression or mutation of E3s and DUBs can lead to altered stability of critical tumor-related proteins, thereby driving OSCC initiation and progression. Therefore, understanding the aberrantly activated signaling pathways in OSCC and the ubiquitination/deubiquitination mechanisms within these pathways will help elucidate the molecular mechanisms and improve OSCC treatment by targeting relevant components. Here, we summarize four aberrantly activated signaling pathways in OSCC―the PI3K/AKT/mTOR pathway, Wnt/β-catenin pathway, Hippo pathway, and canonical NF-κB pathway―and systematically review the regulatory mechanisms of ubiquitination/deubiquitination within these pathways, along with potential drug targets. PI3K/AKT/mTOR pathway is aberrantly activated in approximately 70% of OSCC cases. E3s such as FBXW7, NEDD4, and DUBs such as USP7, USP10 exert regulatory effects via ubiquitination/deubiquitination of pathway components. Among them, FBXW7 and USP10 inhibit pathway activation, whereas NEDD4 and USP7 promote it. Aberrant activation of the Wnt/β-catenin pathway leads to β-catenin nuclear translocation and induction of cell proliferation. Related E3s include c-Cbl, RNF43, etc.; DUBs include USP9X, USP20, etc. Among them, c-Cbl and RNF43 inhibit pathway activation, whereas USP9X and USP20 promote it. Inactivation of the Hippo pathway promotes YAP/TAZ nuclear translocation, facilitating cancer cell metastasis. Related E3s include CRL4DCAF1, SIAH2, etc.; DUBs include USP1, USP21, etc. Among them, CRL4DCAF1 and SIAH2 inhibit pathway activation, whereas USP1 and USP21 promote it. Persistent activation of the canonical NF-κB pathway is associated with an inflammatory microenvironment and chemotherapy resistance. Related E3s include TRAF6, LUBAC, etc.; DUBs include A20, CYLD, etc. Among them, A20 and CYLD inhibit pathway activation, whereas TRAF6 and LUBAC promote it. Targeting these E3s and DUBs provides directions for OSCC drug research. Small-molecule inhibitors such as YCH2823 (a USP7 inhibitor), GSK2643943A (a USP20 inhibitor), and HOIPIN-8 (a LUBAC inhibitor) have shown promising antitumor activity in preclinical models; PROTAC molecules, by binding to surface sites of target proteins and recruiting E3s, achieve targeted ubiquitination and degradation of proteins insensitive to small-molecule inhibitors, for example, PU7-1-mediated USP7 degradation, offering new strategies to overcome traditional drug limitations. Currently, NX-1607 (a Cbl-b inhibitor) has entered phase I clinical trials, with preliminary results confirming its safety and antitumor activity. Future research on aberrant E3s and DUBs in OSCC and the development of highly specific inhibitors will be of great significance for OSCC precision therapy.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of "cardiovascular-kidney-metabolic (CKM) syndrome" in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a "metabolic toxic environment", fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%–30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Objective Transfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks.Methods We began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model"s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets.Results The proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model"s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research.Conclusion This study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5"UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Thrombospondin 4 (THBS4; TSP4), a crucial component of the extracellular matrix (ECM), serves as an important regulator of tissue homeostasis and various pathophysiological processes. As a member of the evolutionarily conserved thrombospondin family, THBS4 is a multidomain adhesive glycoprotein characterized by six distinct structural domains that mediate its diverse biological functions. Through dynamic interactions with various ECM components, THBS4 plays pivotal roles in cell adhesion, proliferation, inflammation regulation, and tissue remodeling, establishing it as a key modulator of microenvironmental organization. The transcription and translation of THBS4 gene, as well as the activity of the THBS4 protein, are tightly regulated by multiple signaling pathways and extracellular cues. Positive regulators of THBS4 include transforming growth factor- β (TGF- β), interferon-γ (IFNγ), granulocyte-macrophage colony-stimulating factor (GM-CSF), bone morphogenetic proteins (BMP12/13), and other regulatory factors (such as B4GALNT1, ITGA2/ITGB1, PDGFRβ, etc.), which upregulate THBS4 at the mRNA and/or protein level. Conversely, oxidized low-density lipoprotein (OXLDL) acts as a potent negative regulator of THBS4. This intricate regulatory network ensures precise spatial and temporal control of THBS4 expression in response to diverse physiological and pathological stimuli. Functionally, THBS4 acts as a critical signaling hub, influencing multiple downstream pathways essential for cellular behavior and tissue homeostasis. The best-characterized pathways include: (1) the PI3K/AKT/mTOR axis, which THBS4 modulates through both direct and indirect interactions with integrins and growth factor receptors; (2) Wnt/β-catenin signaling, where THBS4functions as either an activator or inhibitor depending on the cellular context; (3) the suppression of DBET/TRIM69, contributing to its diverse regulatory roles. These signaling connections position THBS4 as a master regulator of cellular responses to microenvironmental changes. Substantial evidence links aberrant THBS4 expression to a range of pathological conditions, including neoplastic diseases, cardiovascular disorders, fibrotic conditions, neurodegenerative diseases, musculoskeletal disorders, and atopic dermatitis. In cancer biology, THBS4 exhibits context-dependent roles, functioning either as a tumor suppressor or promoter depending on the tumor type and microenvironment. In the cardiovascular system, THBS4 contributes to both adaptive remodeling and maladaptive fibrotic responses. Its involvement in fibrotic diseases arises from its ability to regulate ECM deposition and turnover. The diagnostic and therapeutic potential of THBS4 is particularly promising in oncology and cardiovascular medicine. As a biomarker, THBS4 expression patterns correlate significantly with disease progression and patient outcomes. Therapeutically, targeting THBS4-mediated pathways offers novel opportunities for precision medicine approaches, including anti-fibrotic therapies, modulation of the tumor microenvironment, and enhancement of tissue repair. This comprehensive review systematically explores three key aspects of THBS4 research (1) The fundamental biological functions of THBS4 in ECM organization; (2) its mechanistic involvement in various disease pathologies; (3) its emerging potential as both a diagnostic biomarker and therapeutic target. By integrating recent insights from molecular studies, animal models, and clinical investigations, this review provides a framework for understanding the multifaceted roles of THBS4 in health and disease. The synthesis of current knowledge highlights critical research gaps and future directions for exploring THBS4-targeted interventions across multiple disease contexts. Given its unique position at the intersection of ECM biology and cellular signaling, THBS4 represents a promising frontier for the development of novel diagnostic tools and therapeutic strategies in precision medicine.

The pathogenesis of neurodegenerative diseases (NDDs) is fundamentally linked to complex and profound alterations in metabolic networks within the brain, which exhibit marked spatial heterogeneity. While conventional bulk metabolomics is powerful for detecting global metabolic shifts, it inherently lacks spatial resolution. This methodological limitation hampers the ability to interrogate critical metabolic dysregulation within discrete anatomical brain regions and specific cellular microenvironments, thereby constraining a deeper understanding of the core pathological mechanisms that initiate and drive NDDs. To address this critical gap, spatial metabolomics, with mass spectrometry imaging (MSI) at its core, has emerged as a transformative approach. It uniquely overcomes the limitations of bulk methods by enabling high-resolution, simultaneous detection and precise localization of hundreds to thousands of endogenous molecules—including primary metabolites, complex lipids, neurotransmitters, neuropeptides, and essential metal ions—directly in situ from tissue sections. This powerful capability offers an unprecedented spatial perspective for investigating the intricate and heterogeneous chemical landscape of NDD pathology, opening new avenues for discovery. Accordingly, this review provides a comprehensive overview of the field, beginning with a discussion of the technical features, optimal application scenarios, and current limitations of major MSI platforms. These include the widely adopted matrix-assisted laser desorption/ionization (MALDI)-MSI, the ultra-high-resolution technique of secondary ion mass spectrometry (SIMS)-MSI, and the ambient ionization method of desorption electrospray ionization (DESI)-MSI, along with other emerging technologies. We then highlight the pivotal applications of spatial metabolomics in NDD research, particularly its role in elucidating the profound chemical heterogeneity within distinct pathological microenvironments. These applications include mapping unique molecular signatures around amyloid-β-protein (Aβ) plaques, uncovering the metabolic consequences of neurofibrillary tangles composed of hyperphosphorylated tau protein, and characterizing the lipid and metabolite composition of Lewy bodies. Moreover, we examine how spatial metabolomics contributes to constructing detailed metabolic vulnerability maps across the brain, shedding light on the biochemical factors that render certain neuronal populations and anatomical regions selectively susceptible to degeneration while others remain resilient. Looking beyond current applications, we explore the immense potential of integrating spatial metabolomics with other advanced research methodologies. This includes its combination with three-dimensional brain organoid models to recapitulate disease-relevant metabolic processes, its linkage with multi-organ axis studies to investigate how systemic metabolic health influences neurodegeneration, and its convergence with single-cell and subcellular analyses to achieve unprecedented molecular resolution. In conclusion, this review not only summarizes the current state and critical role of spatial metabolomics in NDD research but also offers a forward-looking perspective on its transformative potential. We envision its continued impact in advancing our fundamental understanding of NDDs and accelerating translation into clinical practice—from the discovery of novel biomarkers for early diagnosis to the development of high-throughput drug screening platforms and the realization of precision medicine for individuals affected by these devastating disorders.

Objective Tobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction.Methods The study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools.Results Experimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence.Conclusion The TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Human milk is universally recognized as the optimal and most natural source of nutrition for newborns, offering benefits that extend far beyond basic energy and macronutrient provision. Among its complex constituents, human milk oligosaccharides (HMOs) represent the third most abundant solid component, surpassed only by lactose and lipids. HMOs are distinguished by their exceptionally high structural diversity—over 200 distinct structures have been identified to date. This structural complexity underlies the extensive biological functions HMOs perform within the infant’s body. HMOs play a pivotal role in promoting healthy growth, development, and overall well-being in infants and young children, functioning as indispensable bioactive molecules. Their key physiological activities include: immunomodulation and allergy prevention by promoting immune tolerance and reducing the risk of allergic diseases; potent anti-inflammatory and antioxidant effects that protect vulnerable infant tissues; support for brain development and cognitive enhancement through multiple mechanisms; anti-pathogenic properties, acting as soluble receptor analogs or “decoy” molecules to competitively block viral, bacterial, and other pathogen adhesion, thereby preventing colonization and infection in the gastrointestinal tract; and functioning as blood group substances. At the translational and application level, HMO research is actively driving cross-disciplinary innovation. Building on a deep understanding of their immunological and neurodevelopmental benefits, certain structurally defined HMOs have been successfully incorporated into infant formula. These HMO-supplemented formulas have received regulatory approval and are now commercially available worldwide, providing a nutritional alternative that more closely resembles human milk for infants who are not exclusively breastfed. This represents a significant step toward narrowing the compositional gap between formula and breast milk. Simultaneously, research into the symbiotic relationship between HMOs and the gut microbiota—particularly their role as selective prebiotic substrates promoting the growth of beneficial bacteria—has catalyzed the development of novel functional foods, dietary supplements, and microbiome-targeted therapies. These include advanced synbiotic formulations that combine specific probiotic strains with HMOs to synergistically optimize gut health and function. Furthermore, the intrinsic qualities of HMOs—including their natural origin, safety profile, biocompatibility, and proven antioxidant properties—have attracted growing interest in the emerging field of high-performance cosmetics. They are increasingly being explored as innovative functional ingredients in skincare products aimed at reducing oxidative stress and supporting skin health. This review aims to systematically synthesize recent advancements in HMO research, offering a comprehensive analysis centered on their complex composition and structural diversity; the molecular and cellular mechanisms underlying their diverse biological functions; their translational potential across sectors such as nutrition, medicine, and consumer care (including cosmetics); and the major challenges that persist in the field. It critically examines both foundational discoveries and recent breakthroughs. By integrating these interconnected themes, the review provides a holistic and up-to-date perspective on the scientific landscape of HMOs, highlighting their essential role in early-life nutrition and their expanding relevance across health and wellness applications. It also outlines promising directions for future research, with the goal of advancing evidence-based innovation in infant health and beyond.

Objective Recent studies have highlighted the critical role of NUDT19 in the initiation, progression, and prognosis of specific cancer types. However, its involvement in pan-cancer analysis has not been fully characterized. This study aims to systematically explore the expression patterns, clinical significance, and immune-related functions of NUDT19 in various cancer types through multi-omics analysis, further revealing its potential role in cancer, particularly its functional and therapeutic target value in leukemia.Methods To achieve this goal, various bioinformatics approaches were employed to evaluate the expression patterns, clinical significance, and immune-related functions of NUDT19 in tumors and normal tissues. Additionally, we analyzed the mutation characteristics of NUDT19 and its relationship with epigenetic modifications. Using the single-cell analysis tool SingleCellBase, we explored the distribution of NUDT19 across different cell subpopulations in tumors. To validate these findings, qRT-PCR was used to measure NUDT19 expression levels in specific tumor cell lines, and we established acute myeloid leukemia (AML) cell lines (HL-60 and THP-1) to conduct NUDT19 knockdown and overexpression experiments, assessing its effects on leukemia cell proliferation, apoptosis, and invasion.Results Pan-cancer analysis revealed the dysregulated expression of NUDT19 across multiple cancer types, which was closely associated with poor prognosis, clinical staging, and diagnostic markers. Furthermore, NUDT19 was significantly correlated with tumor biomarkers, immune-related genes, and immune cell infiltration in different cancers. Mutation analysis showed that multiple mutations in NUDT19 were significantly associated with epigenetic changes. Single-cell analysis revealed the heterogeneity of NUDT19 expression in cancer cells, suggesting its potentially diverse functional roles in different cell subpopulations. qRT-PCR experiments confirmed the significant upregulation of NUDT19 in various tumor cell lines. In AML cell lines, NUDT19 knockdown led to reduced cell proliferation and invasion, with increased apoptosis, while NUDT19 overexpression significantly enhanced cell proliferation and invasion while reducing apoptosis.Conclusion This study demonstrates the diverse roles of NUDT19 in various cancer types, with a particularly prominent functional role in leukemia. NUDT19 is not only associated with tumor initiation and progression but may also influence cancer progression through the regulation of the immune microenvironment and epigenetic mechanisms. Our research highlights the potential of NUDT19 as a therapeutic target, particularly for targeted therapies in malignancies such as leukemia, with significant clinical application prospects.

T7 RNA polymerase (T7 RNAP) is one of the simplest known RNA polymerases. Its unique structural features make it a critical model for studying the mechanisms of RNA synthesis. This review systematically examines the static crystal structure of T7 RNAP, beginning with an in-depth examination of its characteristic “thumb”, “palm”, and “finger” domains, which form the classic “right-hand-like” architecture. By detailing these structural elements, this review establishes a foundation for understanding the overall organization of T7 RNAP. This review systematically maps the functional roles of secondary structural elements and their subdomains in transcriptional catalysis, progressively elucidating the fundamental relationships between structure and function. Further, the intrinsic flexibility of T7 RNAP and its applications in research are also discussed. Additionally, the review presents the structural diagrams of the enzyme at different stages of the transcription process, and through these diagrams, it provides a detailed description of the complete transcription process of T7 RNAP. By integrating structural dynamics and kinetics analyses, the review constructs a comprehensive framework that bridges static structure to dynamic processes. Despite its advantages, T7 RNAP has a notable limitation: it generates double-stranded RNA (dsRNA) as a byproduct. The presence of dsRNA not only compromises the purity of mRNA products but also elicits nonspecific immune responses, which pose significant challenges for biotechnological and therapeutic applications The review provides a detailed exploration of the mechanisms underlying dsRNA formation during T7 RNAP catalysis, reviews current strategies to mitigate this issue, and highlights recent progress in the field. A key focus is the semi-rational design of T7 RNAP mutants engineered to minimize dsRNA generation engineered to minimize dsRNA generation and enhance catalytic performance. Beyond its role in transcription, T7 RNAP exhibits rapid development and extensive application in fields, including gene editing, biosensing, and mRNA vaccines. This review systematically examines the structure-function relationships of T7 RNAP, elucidates the mechanisms of dsRNA formation, and discusses engineering strategies to optimize its performance. It further explores the engineering optimization and functional expansion of T7 RNAP. Furthermore, this review also addresses the pressing issues that currently need resolution, discusses the major challenges in the practical application of T7 RNAP, and provides an outlook on potential future research directions. In summary, this review provides a comprehensive analysis of T7 RNAP, ranging from its structural architecture to cutting-edge applications. We systematically examine: (1) the characteristic right-hand domains (thumb, palm, fingers) that define its minimalistic structure; (2) the structure-function relationships underlying transcriptional catalysis; and (3) the dynamic transitions during the complete transcription cycle. While highlighting T7 RNAP"s versatility in gene editing, biosensing, and mRNA vaccine production, we critically address its major limitation—dsRNA byproduct formation—and evaluate engineering solutions including semi-rationally designed mutants. By synthesizing current knowledge and identifying key challenges, this work aims to provide novel insights for the development and application of T7 RNAP and to foster further thought and progress in related fields.

Cardiovascular disease (CVD) remains one of the leading causes of mortality among adults globally, with continuously rising morbidity and mortality rates. Metabolic disorders are closely linked to various cardiovascular diseases and play a critical role in their pathogenesis and progression, involving multifaceted mechanisms such as altered substrate utilization, mitochondrial structural and functional dysfunction, and impaired ATP synthesis and transport. In recent years, the potential role of peroxisome proliferator-activated receptors (PPARs) in cardiovascular diseases has garnered significant attention, particularly peroxisome proliferator-activated receptor alpha (PPARα), which is recognized as a highly promising therapeutic target for CVD. PPARα regulates cardiovascular physiological and pathological processes through fatty acid metabolism. As a ligand-activated receptor within the nuclear hormone receptor family, PPARα is highly expressed in multiple organs, including skeletal muscle, liver, intestine, kidney, and heart, where it governs the metabolism of diverse substrates. Functioning as a key transcription factor in maintaining metabolic homeostasis and catalyzing or regulating biochemical reactions, PPARα exerts its cardioprotective effects through multiple pathways: modulating lipid metabolism, participating in cardiac energy metabolism, enhancing insulin sensitivity, suppressing inflammatory responses, improving vascular endothelial function, and inhibiting smooth muscle cell proliferation and migration. These mechanisms collectively reduce the risk of cardiovascular disease development. Thus, PPARα plays a pivotal role in various pathological processes via mechanisms such as lipid metabolism regulation, anti-inflammatory actions, and anti-apoptotic effects. PPARα is activated by binding to natural or synthetic lipophilic ligands, including endogenous fatty acids and their derivatives (e.g., linoleic acid, oleic acid, and arachidonic acid) as well as synthetic peroxisome proliferators. Upon ligand binding, PPARα activates the nuclear receptor retinoid X receptor (RXR), forming a PPARα-RXR heterodimer. This heterodimer, in conjunction with coactivators, undergoes further activation and subsequently binds to peroxisome proliferator response elements (PPREs), thereby regulating the transcription of target genes critical for lipid and glucose homeostasis. Key genes include fatty acid translocase (FAT/CD36), diacylglycerol acyltransferase (DGAT), carnitine palmitoyltransferase I (CPT1), and glucose transporter (GLUT), which are primarily involved in fatty acid uptake, storage, oxidation, and glucose utilization processes. Advancing research on PPARα as a therapeutic target for cardiovascular diseases has underscored its growing clinical significance. Currently, PPARα activators/agonists, such as fibrates (e.g., fenofibrate and bezafibrate) and thiazolidinediones, have been extensively studied in clinical trials for CVD prevention. Traditional PPARα agonists, including fenofibrate and bezafibrate, are widely used in clinical practice to treat hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) levels. These fibrates enhance fatty acid metabolism in the liver and skeletal muscle by activating PPARα, and their cardioprotective effects have been validated in numerous clinical studies. Recent research highlights that fibrates improve insulin resistance, regulate lipid metabolism, correct energy metabolism imbalances, and inhibit the proliferation and migration of vascular smooth muscle and endothelial cells, thereby ameliorating pathological remodeling of the cardiovascular system and reducing blood pressure. Given the substantial attention to PPARα-targeted interventions in both basic research and clinical applications, activating PPARα may serve as a key therapeutic strategy for managing cardiovascular conditions such as myocardial hypertrophy, atherosclerosis, ischemic cardiomyopathy, myocardial infarction, diabetic cardiomyopathy, and heart failure. This review comprehensively examines the regulatory roles of PPARα in cardiovascular diseases and evaluates its clinical application value, aiming to provide a theoretical foundation for further development and utilization of PPARα-related therapies in CVD treatment.

Objective The proteome of biological evidence contains rich genetic information, namely single amino acid polymorphisms (SAPs) in protein sequences. However, due to the lack of efficient and convenient analysis tools, the application of SAP in public security still faces many challenges. This paper aims to meet the application requirements of SAP analysis for forensic biological evidence’s proteome data.Methods The software is divided into three modules. First, based on a built-in database of common non-synonymous single nucleotide polymorphisms (nsSNPs) and SAPs in East Asian populations, the software integrates and annotates newly identified exonic nsSNPs as SAPs, thereby constructing a customized SAP protein sequence database. It then utilizes a pre-installed search engine—either pFind or MaxQuant—to perform analysis and output SAP typing results, identifying both reference and variant types, along with their corresponding imputed nsSNPs. Finally, SAPTyper compares the proteome-based typing results with the individual"s exome-derived nsSNP profile and outputs the comparison report.Results SAPTyper accepts proteomic DDA mass spectrometry raw data (DDA acquisition mode) and exome sequencing results of nsSNPs as input and outputs the report of SAPs result. The pFind and Maxquant search engines were used to test the proteome data of 2 hair shafts of 2 individuals, and both obtained SAP results. It was found that the results of the Maxquant search engine were slightly less than those of pFind. This result shows that SAPTyper can achieve SAP fingding function. Moreover, the pFind search engine was used to test the proteome data of 3 hair shafts from 1 European person and 1 African person in the literature. Among the sites fully matched by the literature method, sites detected by SAPTyper are also included; for semi-matching sites, that is, nsSNPs are heterozygous, both literature method and SAPTyper method had the risk of missing detection for one type of the allele. Comparing the analysis results of SAPTyper with the SAP test results reported in the literature, it was found that some imputed nsSNP sites identified by the literature method but not detected by SAPTyper had a MAF of less than 0.1% in East Asian populations, and therefore they were not included in the common nsSNP database of East Asian populations constructed by this software. Since the database construction of this software is based on the genetic variation information of East Asian populations, it is currently unable to effectively identify representative unique common variation sites in European or African populations, but it can still identify SAP sites shared by these populations and East Asian populations.Conclusion An automated SAP analysis algorithm was developed for East Asian populations, and the software named SAPTyper was developed. This software provides a convenient and efficient analysis tool for the research and application of forensic proteomic SAP and has important application prospects in individual identification and phenotypic inference based on SAP.

Objective Fat infiltration has been shown to be closely related to muscle mass loss and a variety of muscle diseases. This study proposes a method based on phase-angle electrical impedance tomography (ΦEIT) to visualize the electrical characteristic response caused by muscle fat infiltration, aiming to provide a new technical means for early non-invasive detection of muscle mass deterioration.Methods This study was divided into two parts. First, a laboratory pork model was constructed to simulate different degrees of fat infiltration by injecting 1 ml or 2 ml of emulsified fat solution into different muscle compartments, and the phase angle images were reconstructed using ΦEIT. Second, a human experiment was conducted to recruit healthy subjects (n=8) from two age groups (20-25 years old and 26-30 years old). The fat content percentage η_fat of the left and right legs was measured by bioelectrical impedance analysis (BIA), and the phase angle images of the left and right calves were reconstructed using ΦEIT. The relationship between the global average phase angle Φ_M and the spatial average phase angle Φ_Mi of each muscle compartment and fat infiltration was further analyzed.Results In the laboratory pork model, the grayscale value of the image increased with the increase of η_fat and Φ_M showed a downward trend. The results of human experiments showed that at the same fat content percentage, the Φ_M of the 26-30-year-old group was about 20%-35% lower than that of the 20-25-year-old group. The fat content percentage was significantly negatively correlated with Φ_M. In addition, the M₂ (soleus) compartment was most sensitive to fat infiltration, and the spatial average phase angles of the M₂ (soleus), M₃ (tibialis posterior and flexor digitorum longus), and M₄ (tibialis anterior, extensor digitorum longus, and peroneus longus) compartments all showed significant inter-group differences.Conclusion ΦEIT imaging can effectively distinguish different degrees of fat infiltration, especially in deep, small or specially located muscles, showing high sensitivity, demonstrating the potential application of this method in local muscle mass monitoring and early non-invasive diagnosis.

Lipid droplets (LDs) are dynamic organelles that are ubiquitous across most organisms, including animals, plants, protists, and microorganisms. Their core consists of neutral lipids, surrounded by a phospholipid monolayer adorned with a specific set of proteins. As critical intracellular hubs of metabolic regulation, lipid droplets play essential roles in maintaining physiological homeostasis and contributing to the progression of various pathological processes. They store neutral lipids for energy production during periods of starvation or for membrane biosynthesis, and they sequester fatty acids to mitigate lipotoxicity. Clinically, dysregulation of lipid droplet function is associated with a wide range of diseases, including metabolic dysfunction-associated steatotic liver disease (MASLD), obesity, type 2 diabetes mellitus (T2DM), neurodegenerative disorders, and cancer. Research into the biological functions of lipid droplets—as dynamic organelles and their links to multiple diseases—has emerged as a cutting-edge focus in cell biology. In recent years, significant advances have been made in understanding lipid droplet biogenesis. Researchers have developed a more refined framework that elucidates how LDs are assembled in the endoplasmic reticulum (ER). Triacylglycerols and sterol esters are synthesized between the inner and outer leaflets of the ER bilayer, and when they exceed the critical nucleation concentration (CNC), they coalesce to form neutral lipid lenses. These then bud from the ER under the coordinated action of key proteins such as Seipin, fat storage-inducing transmembrane protein 2 (FIT2), and the peroxisomal membrane protein Pex30. This budding process is driven by changes in membrane curvature and surface tension, induced by the asymmetric distribution of phospholipids. Nascent lipid droplets recruit lipid-synthesizing enzymes via ER-LD bridging structures, enabling localized lipid production and surface expansion, ultimately resulting in the formation of mature LDs. Biochemical and biophysical approaches have revealed important features of this process, underscoring the critical roles of ER membrane biophysical properties and specific phospholipids. Structural biology and proteomic studies have identified key regulators—particularly Seipin and FIT2—as central players in LD biogenesis. This review systematically summarizes recent advances in the molecular mechanisms of LD biogenesis. It delves into the processes of LD nucleation, membrane budding, and expansion in eukaryotic cells, with a special focus on how core factors such as Seipin and FIT2 dynamically regulate LD morphology. In addition, it examines the mechanisms and pathways by which class I and class II proteins are targeted to LDs, compares LD biogenesis involving different neutral lipid cores, and discusses the disease relevance of specific regulatory proteins. Finally, the review outlines critical unresolved questions in the field of LD biogenesis, offering clear directions for future research and providing a comprehensive framework for deepening our understanding of LD formation and its implications for disease intervention.

RNA modifications constitute a crucial class of post-transcriptional chemical alterations that profoundly influence RNA stability and translational efficiency, thereby shaping cellular protein expression profiles. These diverse chemical marks are ubiquitously involved in key biological processes, including cell proliferation, differentiation, apoptosis, and metastatic potential, and they exert precise regulatory control over these functions. A major advance in the field is the recognition that RNA modifications do not act in isolation. Instead, they participate in complex, dynamic interactions—through synergistic enhancement, antagonism, competitive binding, and functional crosstalk—forming what is now termed the “RNA modification interactome” or “RNA modification interaction network.” The formation and functional operation of this interactome rely on a multilayered regulatory framework orchestrated by RNA-modifying enzymes—commonly referred to as “writers,” “erasers,” and “readers.” These enzymes exhibit hierarchical organization within signaling cascades, often functioning in upstream-downstream sequences and converging at critical regulatory nodes. Their integration is further mediated through shared regulatory elements or the assembly into multi-enzyme complexes. This intricate enzymatic network directly governs and shapes the interdependent relationships among various RNA modifications. This review systematically elucidates the molecular mechanisms underlying both direct and indirect interactions between RNA modifications. Building upon this foundation, we introduce novel quantitative assessment frameworks and predictive disease models designed to leverage these interaction patterns. Importantly, studies across multiple disease contexts have identified core downstream signaling axes driven by specific constellations of interacting RNA modifications. These findings not only deepen our understanding of how RNA modification crosstalk contributes to disease initiation and progression, but also highlight its translational potential. This potential is exemplified by the discovery of diagnostic biomarkers based on interaction signatures and the development of therapeutic strategies targeting pathogenic modification networks. Together, these insights provide a conceptual framework for understanding the dynamic and multidimensional regulatory roles of RNA modifications in cellular systems. In conclusion, the emerging concept of RNA modification crosstalk reveals the extraordinary complexity of post-transcriptional regulation and opens new research avenues. It offers critical insights into the central question of how RNA-modifying enzymes achieve substrate specificity—determining which nucleotides within specific RNA transcripts are selectively modified during defined developmental or pathological stages. Decoding these specificity determinants, shaped in large part by the modification interactome, is essential for fully understanding the biological and pathological significance of the epitranscriptome.

Alzheimer’s disease (AD), a progressive neurodegenerative disorder and the leading cause of dementia in the elderly, is characterized by severe cognitive decline, loss of daily living abilities, and neuropsychiatric symptoms. This condition imposes a substantial burden on patients, families, and society. Despite extensive research efforts, the complex pathogenesis of AD, particularly the early mechanisms underlying cognitive dysfunction, remains incompletely understood, posing significant challenges for timely diagnosis and effective therapeutic intervention. Among the various cellular components implicated in AD, GABAergic interneurons have emerged as critical players in the pathological cascade, playing a pivotal role in maintaining neural network integrity and function in key brain regions affected by the disease. GABAergic interneurons represent a heterogeneous population of inhibitory neurons essential for sustaining neural network homeostasis. They achieve this by precisely modulating rhythmic oscillatory activity (e.g., theta and gamma oscillations), which are crucial for cognitive processes such as learning and memory. These interneurons synthesize and release the inhibitory neurotransmitter GABA, exerting potent control over excitatory pyramidal neurons through intricate local circuits. Their primary mechanism involves synaptic inhibition, thereby modulating the excitability and synchrony of neural populations. Emerging evidence highlights the significant involvement of GABAergic interneuron dysfunction in AD pathogenesis. Contrary to earlier assumptions of their resistance to the disease, specific subtypes exhibit vulnerability or altered function early in the disease process. Critically, this impairment is not merely a consequence but appears to be a key driver of network hyperexcitability, a hallmark feature of AD models and potentially a core mechanism underlying cognitive deficits. For instance, parvalbumin-positive (PV⁺) interneurons display biphasic alterations in activity. Both suppressing early hyperactivity or enhancing late activity can rescue cognitive deficits, underscoring their causal role. Somatostatin-positive (SST⁺) neurons are highly sensitive to amyloid-beta (Aβ) dysfunction. Their functional impairment drives AD progression via a dual pathway: compensatory hyperexcitability promotes Aβ generation, while released SST-14 forms toxic oligomers with Aβ, collectively accelerating neuronal loss and amyloid deposition, forming a vicious cycle. Vasoactive intestinal peptide-positive (VIP⁺) neurons, although potentially spared in number early in the disease, exhibit altered firing properties (e.g., broader spikes, lower frequency), contributing to network dysfunction (e.g., in CA1). Furthermore, VIP release induced by 40 Hz sensory stimulation (GENUS) enhances glymphatic clearance of Aβ, demonstrating a direct link between VIP neuron function and modulation of amyloid pathology. Given their central role in network stability and their demonstrable dysfunction in AD, GABAergic interneurons represent promising therapeutic targets. Current research primarily explores three approaches: increasing interneuron numbers (e.g., improving cortical PV⁺ interneuron counts and behavior in APP/PS1 mice with the antidepressant citalopram; transplanting stem cells differentiated into functional GABAergic neurons to enhance cognition), enhancing neuronal activity (e.g., using low-dose levetiracetam or targeted activation of specific molecules to boost PV⁺ interneuron excitability, restoring neural network γ-oscillations and memory; non-invasive neuromodulation techniques like 40 Hz repetitive transcranial magnetic stimulation (rTMS), GENUS, and minimally invasive electroacupuncture to improve inhibitory regulation, promote memory, and reduce Aβ), and direct GABA system intervention (clinical and animal studies reveal reduced GABA levels in AD-affected brain regions; early GABA supplementation improves cognition in APP/PS1 mice, suggesting a therapeutic time window). Collectively, these findings establish GABAergic interneuron intervention as a foundational rationale and distinct pathway for AD therapy. In conclusion, GABAergic interneurons, particularly the PV⁺, SST⁺, and VIP⁺ subtypes, play critical and subtype-specific roles in the initiation and progression of AD pathology. Their dysfunction significantly contributes to network hyperexcitability, oscillatory deficits, and cognitive decline. Understanding the heterogeneity in their vulnerability and response mechanisms provides crucial insights into AD pathogenesis. Targeting these interneurons through pharmacological, neuromodulatory, or cellular approaches offers promising avenues for developing novel, potentially disease-modifying therapies.

Objective This study proposes a novel single-cell protein localization method based on a class perception graph convolutional network (CP-GCN) to overcome several critical challenges in protein microscopic image analysis, including the scarcity of cell-level annotations, inadequate feature extraction, and the difficulty in achieving precise protein localization within individual cells. The methodology involves multiple innovative components designed to enhance both feature extraction and localization accuracy.Methods First, a class perception module (CPM) is developed to effectively capture and distinguish semantic features across different subcellular categories, enabling more discriminative feature representation. Building upon this, the CP-GCN network is designed to explore global features of subcellular proteins in multicellular environments. This network incorporates a category feature-aware module to extract protein semantic features aligned with label dimensions and establishes a subcellular relationship mining module to model correlations between different subcellular structures. By doing so, it generates co-occurrence embedding features that encode spatial and contextual relationships among subcellular locations, thereby improving feature representation. To further refine localization, a multi-scale feature analysis approach is employed using the K-means clustering algorithm, which classifies multi-scale features within each subcellular category and generates multi-cell class activation maps (CAMs). These CAMs highlight discriminative regions associated with specific subcellular locations, facilitating more accurate protein localization. Additionally, a pseudo-label generation strategy is introduced to address the lack of annotated single-cell data. This strategy segments multicellular images into single-cell images and assigns reliable pseudo-labels based on the CAM-predicted regions, ensuring high-quality training data for single-cell analysis. Under a transfer learning framework, the model is trained to achieve precise single-cell-level protein localization, leveraging both the extracted features and pseudo-labels for robust performance.Results Experimental validation on multiple single-cell test datasets demonstrates that the proposed method significantly outperforms existing approaches in terms of robustness and localization accuracy. Specifically, on the Kaggle 2021 dataset, the method achieves superior mean average precision (mAP) metrics across 18 subcellular categories, highlighting its effectiveness in diverse protein localization tasks. Visualization of the generated CAM results further confirms the model"s capability to accurately localize subcellular proteins within individual cells, even in complex multicellular environments.Conclusion The integration of the CP-GCN network with a pseudo-labeling strategy enables the proposed method to effectively capture heterogeneous cellular features in protein images and achieve precise single-cell protein localization. This advancement not only addresses key limitations in current protein image analysis but also provides a scalable and accurate solution for subcellular protein studies, with potential applications in biomedical research and diagnostic imaging. The success of this method underscores the importance of combining advanced deep learning architectures with innovative training strategies to overcome data scarcity and improve localization performance in biological image analysis. Future work could explore the extension of this framework to other types of microscopic imaging and its application in large-scale protein interaction studies.

Metaflammation is a crucial mechanism in the onset and advancement of metabolic disorders, primarily defined by the activation of immune cells and increased concentrations of pro-inflammatory substances. The function of brain-derived neurotrophic factor (BDNF) in modulating immune and metabolic processes has garnered heightened interest, as BDNF suppresses glial cell activation and orchestrates inflammatory responses in the central nervous system via its receptor tyrosine kinase receptor B (TrkB), while also diminishing local inflammation in peripheral tissues by influencing macrophage polarization. Exercise, as a non-pharmacological intervention, is extensively employed to enhance metabolic disorders. A crucial mechanism underlying its efficacy is the significant induction of BDNF expression in central (hypothalamus, hippocampus, prefrontal cortex, and brainstem) and peripheral (liver, adipose tissue, intestines, and skeletal muscle) tissues and organs. This induction subsequently regulates inflammatory responses, ameliorates metabolic conditions, and decelerates disease progression. Consequently, BDNF is considered a pivotal molecule in the motor-metabolic regulation axis. Despite prior suggestions that BDNF may have a role in the regulation of exercise-induced inflammation, systematic data remains inadequate. Since that time, the field continues to lack structured descriptions and conversations pertinent to it. As exercise physiology research has advanced, the academic community has increasingly recognized that exercise is a multifaceted activity regulated by various systems, with its effects contingent upon the interplay of elements such as type, intensity, and frequency of exercise. Consequently, it is imperative to transcend the prior study paradigm that concentrated solely on localized effects and singular mechanisms and transition towards a comprehensive understanding of the systemic advantages of exercise. A multitude of investigations has validated that exercise confers health advantages for individuals with metabolic disorders, encompassing youngsters, adolescents, middle-aged individuals, and older persons, and typically enhances health via BDNF secretion. However, exercise is a double-edged sword; the relationship between exercise and health is not linearly positive. Insufficient exercise is ineffective, while excessive exercise can be detrimental to health. Consequently, it is crucial to scientifically develop exercise prescriptions, define appropriate exercise loads, and optimize health benefits to regulate bodily metabolism. BDNF mitigates metaflammation via many pathways during exercise. Initially, BDNF suppresses pro-inflammatory factors and facilitates the production of anti-inflammatory factors by modulating bidirectional transmission between neural and immune cells, therefore diminishing the inflammatory response. Secondly, exercise stimulates the PI3K/Akt, AMPK, and other signaling pathways via BDNF, enhancing insulin sensitivity, reducing lipotoxicity, and fostering mitochondrial production, so further optimizing the body’s metabolic condition. Moreover, exercise-induced BDNF contributes to the attenuation of systemic inflammation by collaborating with several organs, enhancing hepatic antioxidant capacity, regulating immunological response, and optimizing “gut-brain” axis functionality. These processes underscore the efficacy of exercise as a non-pharmacological intervention for enhancing anti-inflammatory and metabolic health. Despite substantial experimental evidence demonstrating the efficacy of exercise in mitigating inflammation and enhancing BDNF levels, numerous limitations persist in the existing studies. Primarily, the majority of studies have concentrated on molecular biology and lack causal experimental evidence that explicitly confirms BDNF as a crucial mediator in the exercise regulation of metaflammation. Furthermore, the outcomes of current molecular investigations are inadequately applicable to clinical practice, and a definitive pathway of “exercise-BDNF-metaflammation” remains unestablished. Moreover, the existing research methodology, reliant on animal models or limited human subject samples, constrains the broad dissemination of the findings. Future research should progressively transition from investigating isolated and localized pathways to a comprehensive multilevel and multidimensional framework that incorporates systems biology and exercise physiology. Practically, there is an immediate necessity to undertake extensive, double-blind, randomized controlled longitudinal human studies utilizing multi-omics technologies (e.g., transcriptomics, proteomics, and metabolomics) to investigate the principal signaling pathways of BDNF-mediated metaflammation and to elucidate the causal relationships and molecular mechanisms involved. Establishing a more comprehensive scientific evidence system aims to furnish a robust theoretical framework and practical guidance for the mechanistic interpretation, clinical application, and pharmaceutical development of exercise in the prevention and treatment of metabolic diseases.

Systemic lupus erythematosus (SLE) is an autoimmune disease of unknown etiology, primarily characterized by systemic inflammation and hyperactivation of both B and T lymphocytes. Key immunological features include increased consumption of complement components, sustained overproduction of type I interferons (IFN-I), and persistent production of a broad spectrum of autoantibodies, such as anti-dsDNA antibodies. However, the use of autoantibodies as biomarkers for the early detection of SLE is associated with a high false-positive rate, suggesting that antibody characteristics evolve during disease progression. N-glycosylation is a critical post-translational modification of antibodies that significantly influences their structure and receptor-binding properties, thereby modulating biological activities and functions. In particular, glycosylation patterns affect the antibody’s affinity for Fc gamma receptors (FcγRs), subsequently regulating various antibody-mediated immune responses. Numerous studies have investigated the impact of individual monosaccharides—such as sialic acid, fucose, and N-acetylglucosamine, which constitute N-glycans—on the immunological functions of antibodies. This review systematically summarizes the aberrant immunoglobulin G (IgG) N-glycosylation patterns observed in SLE patients, with a focus on correlations between disease progression or complications and quantitative alterations in individual glycan components. We first review how different types of N-glycosylation modifications affect the biological activity and functional properties of IgG, particularly regarding the effects of specific monosaccharides—such as sialic acid, fucose, and galactose—on FcγR binding affinity and the resulting downstream immune functions. We then summarize the differential expression of IgG N-glycans and glycosyltransferase genes between SLE patients and healthy controls, and outline the associations between glycosylation changes and SLE-related pathological responses. In response to the inconsistencies and limitations in current research, we propose potential explanations from the perspectives of study methodologies, participant characteristics, and variations in N-glycan structures, aiming to provide a constructive reference for future studies. Given the close relationship between antibody glycosylation and SLE, this review highlights the potential of IgG N-glycosylation patterns as promising biomarkers for early diagnosis and disease monitoring. In terms of therapy, we discuss how IgG glycosylation can enhance the efficacy of intravenous immunoglobulin (IVIg) treatment and introduce emerging therapeutic strategies that aim to modulate endogenous IgG N-glycans as a novel glycan-based approach for SLE management. In summary, N-glycans are essential structural components of antibodies that regulate immune responses by modulating antibody-receptor interactions. Aberrant glycosylation is closely associated with the pathogenesis of autoimmune diseases, including SLE. However, due to the structural diversity of N-glycans and the complexity of glycosylation processes, the precise roles of IgG N-glycosylation in SLE pathophysiology remain incompletely understood. Moreover, therapeutic strategies targeting IgG glycosylation are still in early development and have not yet reached clinical application. Continued progress in glycan analysis technologies and other biological tools, along with interdisciplinary collaboration, will be essential for advancing this field.

Parkinson’s disease (PD), the second most common neurodegenerative disease after Alzheimer’s disease, manifests a variety of motor symptoms, such as bradykinesia, resting tremor, rigidity, postural balance disorder, and also presents non-motor symptoms, including cognitive decline, depression, constipation, and sleep disorders. Currently, treatment for PD primarily encompasses pharmacological interventions, with levodopa being the first-line therapy, and non-pharmacological approaches such as deep brain stimulation (DBS). However, both approaches exhibit therapeutic limitations, with potential adverse reactions emerging from long-term use. Levodopa is associated with dyskinesia, while DBS may lead to mental confusion, cognitive decline, and depression. Exercise, as an effective adjuvant strategy for drug treatment of PD, can significantly improve PD motor disorders. Recently, studies have found that the mechanisms of exercise improving PD motor symptoms are associated with exerkines. Exerkine refers to signalling moieties secreted in response to acute and/or chronic exercise. This review mainly summarizes the improvement of PD motor disorders by various exerkines and the underlying mechanisms. Firstly, exercise can trigger the secretion of brain-derived neurotrophic factor (BDNF) and glial cell line-derived neurotrophic factor (GDNF) in the substantia nigra (SN) and the striatum, potentially improving PD. Recent evidence has suggested that both BDNF and GDNF could improve motor symptoms of PD via restoring the number of dopaminergic neurons in the SN and striatum, increasing striatal dopamine contents, and reducing α-synuclein (α-syn) accumulation in the SN. In addition, BDNF also alleviates motor symptoms of PD by enhancing long-term potentiation and increasing the spine density of spiny projection neurons in the striatum, while GDNF by inhibiting neuroinflammation in the SN via suppressing the activation of microglia, reducing interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) expressions, reducing the phosphorylation of inhibitor of nuclear factor kappa Bα (IκBα), and increasing the anti-inflammatory factors IL-10 and transforming growth factor-β (TGF-β). Secondly, exercise, a main trigger for irisin secretion from skeletal muscle, can improve PD motor symptoms by stimulating the irisin/adenosine monophosphate-activated protein kinase (AMPK)/Sirtuin-1 (SIRT1) pathway. Specifically, irisin alleviates motor symptoms in PD through multiple mechanisms, including inhibiting excessive mitochondrial fission by reducing the expressions of dynamin-related protein 1 (Drp1) and mitochondrial fission protein 1 (Fis1), and alleviating the apoptosis of dopaminergic neurons by increasing B-cell lymphoma 2 (Bcl-2) expression and reducing Bcl-2-associated X protein (Bax) and caspase 3 expressions, and restoring the number of dopaminergic neurons. Thirdly, new biomarkers of PD (cathepsin B and Fetuin-A) also play roles in PD development. Cathepsin B can promote the clearance of pathogenic α-syn in PD by enhancing the function of lysosomes, including strengthening the lysosomal degradation capacity, elevating the transport rate, and increasing the activity of lysosomal glucocerebrosidase (GCase). Fetuin-A has been demonstrated to improve PD by restoring the number and the morphology of Purkinje cells, which are the only efferent neurons in the cerebellar cortex and play an important role in maintaining motor coordination. This review aims to facilitate a deep understanding of the mechanism by which exercise improves PD motor symptoms and provide a theoretical basis for promotion of exercise in PD.

Objective Traditional Chinese medicine (TCM) constitutes a valuable cultural heritage and an important source of antitumor compounds. Poria (Poria cocos (Schw.) Wolf), the dried sclerotium of a polyporaceae fungus, was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia. Traditionally recognized for its diuretic, spleen-tonifying, and sedative properties, modern pharmacological studies confirm that Poria exhibits antioxidant, anti-inflammatory, antibacterial, and antitumor activities. Pachymic acid (PA; a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid), isolated from Poria, is a principal bioactive constituent. Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms, though these remain incompletely characterized. Neuroblastoma (NB), a highly malignant pediatric extracranial solid tumor accounting for 15% of childhood cancer deaths, urgently requires safer therapeutics due to the limitations of current treatments. Although PA shows multi-mechanistic antitumor potential, its efficacy against NB remains uncharacterized. This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking, dynamic simulations, and in vitro assays, aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays.Methods This study employed network pharmacology to identify potential targets of PA in NB, followed by validation using molecular docking, molecular dynamics simulations, MM/PBSA free energy analysis, RT-qPCR and Western blot experiments. Network pharmacology analysis included target screening via TCMSP, GeneCards, DisGeNET, SwissTargetPrediction, SuperPred, and PharmMapper, Subsequently, potential targets were predicted by intersecting the results from these databases via Venn analysis. Following target prediction, topological analysis was performed to identify key targets using Cytoscape software. Molecular docking was conducted using AutoDock Vina, with the binding pocket defined based on crystal structures. Molecular dynamics (MD) simulations were performed for 100 ns using GROMACS, and RMSD, RMSF, SASA, and hydrogen bonding dynamics were analyzed. MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex. In vitro validation included RT-qPCR and Western blot, with GAPDH used as an internal control.Results The CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability. GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress, vesicle lumen, and protein tyrosine kinase activity. KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/Akt, MAPK, and Ras signaling pathways. Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1, EGFR, SRC, and HSP90AA1. RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1, EGFR, and SRC while increasing the HSP90AA1 mRNA and protein levels.Conclusion It was suggested that PA may exert its anti-NB effects by inhibiting AKT1, EGFR, and SRC expression, potentially modulating the PI3K/Akt signaling pathway. These findings provide crucial evidence supporting PA"s development as a therapeutic candidate for NB.

Objective As the central hub of the classical visual pathway, the primary visual cortex not only encodes and processes visual information but also establishes dense neural circuit connections with higher-order cognitive brain regions. Numerous studies have shown that 40 Hz flicker stimulation can induce γ oscillations in the brain and significantly improve learning and cognitive impairments in patients with neurodegenerative diseases. Moreover, flickering light phenomena naturally occur in daily environments. Given that the primary visual cortex serves as the brain’s first cortical hub for receiving visual input, it is essential to comprehensively understand how non-invasive light flicker stimulation modulates its information processing mechanisms. This study systematically investigates the effects of non-invasive light flicker stimulation at different frequencies on the functional properties of neurons in the primary visual cortex of adult mice, aiming to uncover how such stimulation modulates this region and, consequently, affects overall brain function.Methods Three groups of adult mice (approximately 12 weeks old) were exposed to light flicker stimulation at frequencies of 20 Hz, 40 Hz, and 60 Hz, respectively, for a duration of two months. A control group was exposed to the same light intensity without flickering. Following the stimulation period, in vivo multi-channel electrophysiological recordings were conducted. During these recordings, anesthetized mice were presented with various types of moving sinusoidal light gratings to assess the effects of different flicker frequencies on the functional properties of neurons in the primary visual cortex.Results The experimental results demonstrate that two months of light flicker stimulation at 20 Hz, 40 Hz, and 60 Hz enhances the orientation tuning capabilities of neurons in the primary visual cortex. Specifically, 40 Hz and 60 Hz stimulation improved contrast sensitivity, whereas 20 Hz had no significant effect. Further analysis revealed that all three frequencies reduced neuronal response variability (as measured by the Fano factor), increased the signal-to-noise ratio, and decreased noise correlation (rsc) between neurons.Conclusion Non-invasive light flicker stimulation enhances orientation tuning (e.g., orientation bandwidth) and contrast sensitivity (e.g., contrast threshold and C₅₀) in neurons of the primary visual cortex. This enhancement is likely due to improved information processing efficiency, characterized by reduced neuronal variability and increased signal-to-noise ratio. These findings suggest that the primary visual cortex can achieve precise and efficient information encoding in complex lighting environments by selectively adapting to different flicker frequencies and optimizing receptive field properties. This study provides new experimental evidence on how various types of light flicker influence visual perception and offers insights into the mechanisms through which specific frequencies enhance brain function.

Dormant tumor cells constitute a population of cancer cells that reside in a non-proliferative or low-proliferative state, typically arrested in the G0/G1 phase and exhibiting minimal mitotic activity. These cells are commonly observed across multiple cancer types, including breast, lung, and ovarian cancers, and represent a central cellular component of minimal residual disease (MRD) following surgical resection of the primary tumor. Dormant cells are closely associated with long-term clinical latency and late-stage relapse. Due to their quiescent nature, dormant cells are intrinsically resistant to conventional therapies—such as chemotherapy and radiotherapy—that preferentially target rapidly dividing cells. In addition, they display enhanced anti-apoptotic capacity and immune evasion, rendering them particularly difficult to eradicate. More critically, in response to microenvironmental changes or activation of specific signaling pathways, dormant cells can re-enter the cell cycle and initiate metastatic outgrowth or tumor recurrence. This ability to escape dormancy underscores their clinical threat and positions their effective detection and elimination as a major challenge in contemporary cancer treatment. Hypoxia, a hallmark of the solid tumor microenvironment, has been widely recognized as a potent inducer of tumor cell dormancy. However, the molecular mechanisms by which tumor cells sense and respond to hypoxic stress—initiating the transition into dormancy—remain poorly defined. In particular, the lack of a systems-level understanding of the dynamic and multifactorial regulatory landscape has impeded the identification of actionable targets and constrained the development of effective therapeutic strategies. Accumulating evidence indicates that hypoxia-induced dormancy tumor cells are accompanied by a suite of adaptive phenotypes, including cell cycle arrest, global suppression of protein synthesis, metabolic reprogramming, autophagy activation, resistance to apoptosis, immune evasion, and therapy tolerance. These changes are orchestrated by multiple converging signaling pathways—such as PI3K-AKT-mTOR, Ras-Raf-MEK-ERK, and AMPK—that together constitute a highly dynamic and interconnected regulatory network. While individual pathways have been studied in depth, most investigations remain reductionist and fail to capture the temporal progression and network-level coordination underlying dormancy transitions. Systems biology offers a powerful framework to address this complexity. By integrating high-throughput multi-omics data—such as transcriptomics and proteomics—researchers can reconstruct global regulatory networks encompassing the key signaling axes involved in dormancy regulation. These networks facilitate the identification of core regulatory modules and elucidate functional interactions among key effectors. When combined with dynamic modeling approaches—such as ordinary differential equations—these frameworks enable the simulation of temporal behaviors of critical signaling nodes, including phosphorylated AMPK (p-AMPK), phosphorylated S6 (p-S6), and the p38/ERK activity ratio, providing insights into how their dynamic changes govern transitions between proliferation and dormancy. Beyond mapping trajectories from proliferation to dormancy and from shallow to deep dormancy, such dynamic regulatory models support topological analyses to identify central hubs and molecular switches. Key factors—such as NR2F1, mTORC1, ULK1, HIF-1α, and DYRK1A—have emerged as pivotal nodes within these networks and represent promising therapeutic targets. Constructing an integrative, systems-level regulatory framework—anchored in multi-pathway coordination, omics-layer integration, and dynamic modeling—is thus essential for decoding the architecture and progression of tumor dormancy. Such a framework not only advances mechanistic understanding but also lays the foundation for precision therapies targeting dormant tumor cells during the MRD phase, addressing a critical unmet need in cancer management.

Objective This study proposes a fatigue detection method for police extreme training based on electrical impedance imaging technology to prevent muscle damage caused by overstrain during intense physical training.Methods First, based on the mechanism of human anaerobic exercise, lactic acid was identified as a key indicator of muscle fatigue, demonstrating that measuring muscle lactic acid effectively reflects localized fatigue. Second, a numerical simulation model of the human calf was established, and the internal tissue structure of the calf was analyzed to determine the stages of lactic acid diffusion and change. Then, the reconstruction performance of electrical impedance tomography (EIT) in visualizing lactic acid diffusion was compared under three different regularization algorithms, and the most suitable regularization method for subsequent experiments was selected. Finally, a controlled experiment simulating lactate diffusion was conducted to verify the imaging capability of the TK-Noser regularization algorithm in complex imaging fields.Results Simulation results indicate that both the TK-Noser and TV regularization algorithms achieve superior imaging performance, effectively suppressing artifacts in the visualization of lactic acid diffusion inside muscle tissue. The average ICC/RMSE values reached 0.754/0.303 and 0.772/0.320, respectively, while the average SSIM/PSNR values were 0.677/61 dB and 0.488/60 dB, respectively. In the lactate diffusion experiment, the average ICC/SSIM of the EIT reconstruction results based on the TK-Noser regularization algorithm reached 0.701 and 0.572, respectively. Additionally, compared with the TV regularization algorithm, the TK-Noser algorithm better preserved the shape and structural integrity of the imaging target, with an SSIM value 21.2% higher than that of the TV regularization results. This enhancement ensures the stability of the experimental results and significantly improves the capability of electrical impedance imaging technology in monitoring lactate diffusion within complex fields.Conclusion The proposed method offers real-time convenience and non-invasiveness, making it a promising approach for dynamic monitoring of muscle lactate levels in police officers during extreme physical training.