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.

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.

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 THBS4 functions 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 THBS4research (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.

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 protein (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.

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 high BRF1 expression cases better than that of the low BRF1 expression cases. 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.

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.

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.

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 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.

Parkinson"s disease (PD), the second most common neurodegenerative disorder worldwide, presents significant heterogeneity in clinical manifestations, genetic background, and response to interventions. While conventional exercise therapies demonstrate benefits in alleviating motor and non-motor symptoms through mechanisms such as modulating α-synuclein aggregation, enhancing mitophagy, and reducing neuroinflammation, their efficacy varies considerably among individuals. This variability may stem from endogenous factors such as genetic background, clinical phenotypes, stages of pathological progression, as well as exogenous factors like the type, intensity, and frequency of movement. Thus, this review first discusses the necessity of precise exercise interventions for PD patients, focusing on the epidemiological burden, heterogeneity in disease mechanisms, and differences in intervention response (Why). Next, we systematically explain how to develop precise exercise intervention strategies by stratifying interventions based on genetic background, clinical phenotype, and disease stage, combined with technological aids (How). Genetically, mutations in genes such as GBA1, PRKN, PINK1, and SNCA dictate distinct molecular pathologies—including lysosomal dysfunction, impaired mitophagy, and α-synuclein aggregation—which necessitate tailored exercise regimens. For instance, patients with PRKN/PINK1 mutations may benefit from moderate-intensity endurance training to support mitochondrial biogenesis without exacerbating oxidative stress, whereas carriers of GBA1 mutations might require exercises focusing on enhancing lysosomal function and managing oxidative damage. Clinically, patients are stratified into tremor-dominant (TD) and postural instability/gait difficulty (PIGD) subtypes, which demand divergent exercise priorities: coordinative, rhythm-based activities like dance or Tai Chi for TD-PD to engage cerebellar circuits, versus targeted balance and strength training, potentially aided by virtual reality, for PIGD-PD to mitigate axial symptoms and fall risk. Furthermore, intervention strategies must evolve with disease progression: high-intensity exercise is prioritized in early stages to leverage neuroplasticity and potential disease modification, while mid- and late-stage management focuses on functional maintenance, fall prevention, and compensatory strategies, respectively. Critical to implementing this framework is the adoption of digital biomarkers via wearable technology (e.g., inertial sensors, smartwatches), which enables continuous, objective monitoring of gait, tremor, and physiological responses. This facilitates a closed-loop feedback system, allowing for the remote adjustment of exercise parameters (intensity, frequency, duration) in real-time, thus optimizing efficacy and ensuring safety. Finally, we detail how to configure exercise parameters through personalized adaptation (What), including exercise type, intensity, frequency and dose. Higher volumes of physical activity are associated with reduced PD risk and slower progression, though optimal thresholds remain incompletely defined. Aerobic exercise improves cardiovascular fitness and may aid clearance of pathogenic proteins; resistance training counters sarcopenia and bradykinesia; balance training reduces falls; and mind-body exercises (e.g., Tai Chi) integrate motor and cognitive components. Multimodal regimens are often most beneficial. High-intensity aerobic exercise appears particularly effective in early PD, enhancing neural connectivity and mitigating disease progression in randomized trials. Most evidence supports supervised sessions occurring 3–5 times per week, lasting 30–60 min, adapted to individual tolerance and disease stage. In conclusion, this narrative review outlines a comprehensive precision medicine framework for exercise intervention in PD, moving beyond symptomatic management towards targeting underlying pathophysiology. By stratifying patients based on genetic, phenotypic, and staging characteristics, and by leveraging digital technology for dynamic personalization, exercise therapy can be transformed into a more potent, individualized, and disease-modifying strategy. Future research must validate these biomarker-driven approaches in large-scale trials and establish definitive guidelines for translating precision exercise into clinical practice.

Microorganisms, as one of the Earth"s most abundant genetic resources, demonstrate tremendous application potential in fields such as medicine, energy, and environmental protection. However, natural microorganisms often suffer from poor stability and low catalytic efficiency. The emergence of microorganism-nanomaterial hybrid systems offers novel strategies to overcome these limitations. These systems integrate nanomaterials with microorganisms or their components (e.g., cell membranes, metabolites, or biomacromolecules) through methods such as biomineralization, electrostatic assembly, surface modification, and genetic engineering. This enables programmable design from the nanoscale to the macroscale, demonstrating broad application prospects and attracting extensive research interest. First, microbial-nanomaterial hybrid systems are classified based on the types of nanomaterials (organic, inorganic, organic-inorganic) and microorganisms (bacteria, fungi, viruses, algae, probiotics). Both types of systems leverage the unique catalytic selectivity of microorganisms and the diverse physicochemical properties of nanomaterials to achieve multidimensional synergy. Their synergistic mechanisms involve both the biochemical processes of microorganisms and the surface/interface reactions of nanomaterials, representing a multidisciplinary achievement spanning microbial interface engineering, biomimetic catalysis, controllable nanomaterial fabrication, and interfacial transport and reaction processes. Next, the application progress in biomedical fields (such as anti-infection, intestinal diseases, and cancer therapy) and energy conversion (e.g., light-driven hybrid systems for proton reduction to hydrogen, CO₂ reduction and conversion, and nitrogen fixation) is elaborated in detail, highlighting their significant advantages in functional integration and synergistic performance. Microorganism–nanomaterial hybrid systems combine the specific recognition and precise metabolic capabilities of microorganisms with the catalytic, drug-delivery, and optoelectronic functions of nanomaterials, enabling the construction of various multifunctional synergistic platforms for catalysis, diagnosis, and therapy. These advances have greatly promoted development in nanomedicine, energy, and environmental applications. In medical contexts, such systems utilize the natural chemotaxis of microorganisms for precise targeting, achieve controlled drug release through environmentally responsive delivery and metabolic regulation, and enhance therapeutic efficacy via combined chemical-biological treatments and immune modulation. Improved biosafety can be achieved through attenuated microbial designs and nanomaterial coatings, offering diverse strategies for the precise treatment of various diseases. In the energy sector, the excellent light-harvesting properties of semiconductor materials and the precise catalytic capabilities of biological systems have been integrated to successfully construct light-driven biocatalytic systems, significantly improving light utilization efficiency. Finally, this review discusses the key challenges facing the practical application of these systems. Nanomaterials may exert toxic effects on microorganisms, impairing their activity and raising environmental safety concerns. The potential release of engineered nanomaterials into ecosystems necessitates careful risk assessment and long-term monitoring. In real-world environments, microbial functions are easily compromised, nanostructures are prone to damage, and reactive oxygen species (ROS) tend to accumulate, resulting in insufficient system stability. Stringent culture conditions, costly raw materials, and significant batch-to-batch variability hinder large-scale production and commercialization. The synergistic mechanisms between microorganisms and nanomaterials are not yet fully understood, particularly regarding molecular-level interactions and long-term compatibility. In medical applications, off-target risks persist due to unpredictable microbial colonization and immune responses, while environmental applications lack sufficient selective recognition capabilities, indicating a need for improved targeting and specificity. Furthermore, interdisciplinary barriers between biology, materials science, and engineering complicate collaborative innovation, and the absence of well-established standards for evaluation, regulation, and scalability also constrains further development. Future efforts should focus on enhancing biocompatibility, optimizing fabrication processes, and establishing comprehensive safety and performance standards to accelerate the transition of these promising systems from laboratory research to real-world applications.

Nucleic acid aptamers represent a class of single-stranded oligonucleotides capable of high-affinity and specific binding to diverse targets, including proteins, small molecules, cells, and metal ions. Their advantages over antibodies—such as simpler synthesis, lower immunogenicity, superior stability, and easier modification—have positioned them as powerful tools in therapeutics, diagnostics, and biosensing. This review systematically surveys the integral role of bioinformatics and artificial intelligence (AI) in modern aptamer development, spanning from in silico selection and structural prediction to the generative design of novel aptamer sequences. The application of high-throughput SELEX (HT-SELEX) has greatly accelerated the discovery of aptamers, but also introduced computational challenges in processing large-scale sequencing data. Bioinformatics pipelines now routinely include tools like AptaPLEX and AptaSuite for preprocessing raw reads, including demultiplexing, adapter trimming, and quality filtering. Subsequent analytical steps involve clustering-based tools (e.g., FASTAptamer, AptaCLUSTER) to identify enriched sequences, and motif discovery algorithms (such as AptaTRACE and MPBind) that uncover conserved sequence-structure patterns associated with binding functionality. These approaches allow researchers to move beyond manual curation and extract meaningful candidates from complex selection rounds. Accurate prediction of secondary and tertiary structures is essential for understanding aptamer function and interaction mechanisms. Conventional tools, including RNAfold and Mfold, employ thermodynamics-based models to predict RNA folding, yet often struggle with pseudoknots and non-canonical pairs. Recent advances in deep learning—exemplified by SPOT-RNA, E2Efold, and UFold—have significantly improved prediction accuracy by leveraging neural networks trained on large structural datasets. For tertiary structure, methods range from fragment assembly (Rosetta FARFAR2) and homology modeling (RNAComposer) to deep learning-aided approaches such as AlphaFold-RNA and RoseTTAFoldNA. While these tools offer new insights, predicting structures for short, flexible aptamers remains non-trivial. Predicting aptamer–target interactions draws on both physics-based and data-driven approaches. Molecular docking programs—AutoDock Vina, ZDOCK, and MDockPP—provide initial binding poses, which can be refined using molecular dynamics simulations (with GROMACS, AMBER, or NAMD) and free energy perturbation techniques to estimate binding affinity. Complementarily, machine learning models are increasingly employed to predict interactions from sequence and structural features. Early efforts used hand-engineered features with classifiers like SVM and random forest, while contemporary deep learning models (AptaNet, AptaBERT, PAIR) utilize pre-trained language models to capture intricate sequence-binding relationships with superior generalization. Perhaps the most transformative development is the use of generative AI for de novo aptamer design. Conditional variational autoencoders (e.g., RaptGen), generative adversarial networks (e.g., AptaDesigner), and diffusion models (e.g., AptaDiff) can generate novel aptamer sequences conditioned on target properties or desired binding affinities. Reinforcement learning and evolutionary algorithms, including Monte Carlo tree search (Apta-MCTS) and NSGA-II, support multi-objective optimization toward high specificity, stability, and low immunogenicity. These approaches mark a paradigm shift from selective discovery to intentional design, greatly expanding the functional sequence space. Aptamers designed via these computational strategies are increasingly applied in biomedical and environmental fields, including as targeted therapeutics, diagnostic biosensors, and agents in food safety monitoring. Nonetheless, key challenges persist: data scarcity and heterogeneity, model interpretability, and experimental validation bottlenecks. Future progress will depend on standardized data sharing, improved explainable AI, and the integration of computational design with high-throughput experimental screening—ultimately enabling robust, clinically viable aptamer technologies.

Objective Early detection of crop diseases is crucial for effective agricultural management and yield protection. While visible light imaging has been widely applied for disease detection due to its accessibility and non-destructive nature, most existing methods primarily focus on identifying diseases during the symptomatic phase, when visual symptoms are already prominent. However, detecting plant diseases during the incubation period—when symptoms are still subtle or invisible—remains a major challenge due to the lack of distinctive visual cues and limited research methodologies. This study aims to address this gap by proposing a novel three-channel recognition model to accurately identify early blight symptoms during the incubation stage in Solanaceae crops, particularly in chili and tomato, using only visible light images.Methods We established a controlled experimental setup in which healthy leaves and leaves inoculated with early blight pathogens were photographed continuously over time. A total of 1 258 visible light images were collected, capturing various stages of disease progression. From these images, lesion regions were manually annotated. To quantitatively characterize early and subtle color changes within the lesion areas, we extracted color moments—first-order (mean), second-order (standard deviation), and third-order (skewness)—from multiple color spaces, including Lab and HSV. By analyzing the temporal variation of these color moments across disease progression stages, we identified the first-order moment of the Saturation (S) channel in the HSV color space as the most sensitive indicator of lesion development on inoculated leaves. Using this insight, we defined four disease categories: healthy, incubation stage, early stage, and late stage. Subsequently, a three-channel classification model was constructed by integrating features from three color channels that provided complementary information. Three-channel models were constructed based on R-G-B, L-a-b, and H-S-V color spaces, respectively, to evaluate performance across different crops and to determine which color representation provides the most discriminative power for identifying disease symptoms during the incubation period.Results The proposed models demonstrated strong classification performance. The three-channel model built using the Lab color space achieved a 94.44% accuracy in recognizing the incubation stage of early blight in pepper, effectively distinguishing subtle pre-symptomatic features from healthy tissue. The model based on the HSV color space achieved 100% accuracy in detecting incubation-stage symptoms in tomato, underscoring the discriminative power of S-channel variations in this context. These results confirm the model"s capability to identify early blight before visible lesions become pronounced, which is essential for timely disease intervention.Conclusion This study presents a new technical pathway for early-stage disease detection using visible light images by focusing on subtle color feature changes during the incubation period. The proposed three-channel recognition model effectively identifies early blight in both chili and tomato, offering a non-destructive, low-cost, and easily deployable solution for early warning and precision agriculture. Furthermore, this framework can be generalized to other crops and diseases where early detection plays a critical role in minimizing yield losses and ensuring sustainable production. The method lays a solid foundation for future research in pre-symptomatic plant disease recognition and provides valuable tools for intelligent crop monitoring and precision management systems.

Irisin, a myokine discovered in recent years, has been widely confirmed to exert cardioprotective effects. This review comprehensively elaborates on the molecular mechanisms of Irisin in diabetic cardiomyocytes and its close associations with pathophysiological processes such as disordered glycolipid metabolism, oxidative stress, and autophagy. In terms of regulating glycolipid metabolism, Irisin significantly improves energy metabolism in cardiomyocytes by activating the AMPK signaling pathway, thereby reversing diabetes-induced metabolic abnormalities. It promotes the browning of white adipose tissue (WAT), a process in which subcutaneous fat demonstrates a greater propensity to brown compared to visceral fat, thereby enhancing energy expenditure and exerting anti-inflammatory effects. These browned adipocytes secrete bioactive substances such as FGF and adiponectin, which further contribute to metabolic balance. Meanwhile, Irisin reduces the glucolipotoxic burden on pancreatic β-cells: by modulating signaling pathways including PI3K/AKT and AMPK, it not only inhibits β-cell apoptosis but also improves their function and morphology. It enhances insulin secretion by regulating key proteins including Glut2, Glk, and Pdx1 through the AMPK pathway. Additionally, Irisin accelerates the oxidation of free fatty acids (FFA) via activation of pathways such as PPARα, ameliorates insulin resistance, and thus optimizes the metabolic environment of cardiomyocytes. In the context of cellular stress regulation, Irisin exhibits potent antioxidant properties. It not only directly counteracts the accumulation of reactive oxygen species (ROS) to alleviate oxidative damage but also inhibits ferroptosis by upregulating the MITOL/MARCH5 signaling axis, thereby helping to maintain mitochondrial homeostasis. Regarding endoplasmic reticulum stress (ERS), Irisin downregulates key proteins including GRP78 and PERK, thus mitigating ERS-induced cardiomyocyte apoptosis and fibrosis—a protective mechanism that has also been validated in other diseases such as pancreatitis and osteoporosis. In maintaining the balance between autophagy and cell death, Irisin sustains cellular homeostasis by coordinating both mitochondrial-targeted autophagy and non-selective autophagy. It promotes FUNDC1-mediated mitophagy to support mitochondrial turnover and ensure proper organelle function. At the same time, it suppresses excessive autophagy-induced cell damage through pathways such as PI3K/AKT/mTOR. In terms of apoptosis regulation, Irisin downregulates pro-inflammatory factors (e.g., TNF-α, IL-6) and apoptosis-related proteins such as Caspase-3, while upregulating the anti-apoptotic protein Bcl-2. It inhibits cardiomyocyte apoptosis through multiple signaling pathways, including AMPK/mTOR and miR-19b/PTEN. In summary, Irisin plays a crucial protective role in improving metabolic disorders, reducing cellular stress damage, and regulating cell death in diabetic cardiomyopathy (DCM) through multi-target and multi-pathway synergistic mechanisms. Its diverse actions provide an important theoretical basis and potential therapeutic targets for the clinical prevention and treatment of DCM. However, further research is needed to clarify its systemic effects, the safety of clinical interventions, and optimal treatment strategies to fully realize its therapeutic potential.

Cell Biology is one of the most rapidly developing branches of modern life sciences, characterized by distinct interdisciplinary integration. It provides theoretical foundations, experimental skills, and cutting-edge perspectives for undergraduate and graduate students in bioscience and related majors. Against the backdrop of higher education reform in the new era, the Cell Biology teaching team at the University of Chinese Academy of Sciences (UCAS) has restructured the curriculum. The course focuses on the fundamental structures and life processes of cells while incorporating ideological and political elements to foster students" scientific mindset, patriotic sentiment, and social responsibility. By optimizing teaching design, enhancing practical components, and innovating assessment methods, the course integrates knowledge transfer, skill development, and value education. This paper summarizes preliminary experiences from the teaching development and educational practice of the undergraduate Cell Biology course at UCAS, serving as a reference for collaborative research- and teaching-oriented courses in science and engineering.

Objective This study aims to develop a microwave-induced thermoacoustic and ultrasound dual-modality microscopy system that integrates the advantages of both imaging techniques to investigate the dielectric properties of biological tissues at a microscopic level.Methods This paper first discusses a method to enhance system resolution by combining short-pulse microwave excitation with high-frequency point-focused ultrasonic transducer detection. A three-dimensional microwave-induced thermoacoustic microscopic imaging system was constructed based on this approach and further developed into a dual-modality system capable of both thermoacoustic and ultrasonic imaging. The image reconstruction and dual-modality image fusion strategies are also described. Subsequently, experiments were conducted in the following sequence: imaging of copper wires to evaluate the system"s spatial resolution along the X/Y/Z axes; imaging of tubes containing 3% and 6% saline solutions and tubes filled with coupling agent/vegetable oil to demonstrate the complementary information provided by the two modalities; imaging of brain tissue and bone-cartilage samples to assess the applicability of the technology; and osteoporosis detection to validate the disease diagnostic capability of the dual-modality system. The microwave-induced thermoacoustic and ultrasound microscopic images of these samples were verified against corresponding photographs or micro-CT images.Results The thermoacoustic and ultrasonic images of the copper wire closely matched the physical photograph. The three-dimensional resolutions of the microwave-induced thermoacoustic and ultrasound imaging systems, as estimated from the copper wire experiment, were 178 μm × 178 μm × 88 μm and 177 μm × 177 μm × 42 μm, respectively. These measured values align well with theoretical predictions. The dual-modality imaging system successfully combines dielectric property differences captured by thermoacoustic imaging and acoustic impedance variations captured by ultrasound imaging, thereby providing both functional and structural information of the samples. Specifically, the system distinguished between tubes containing saline solutions of different concentrations and those containing vegetable oil, demonstrating strong spatial consistency with physical photographs. The thermoacoustic image contrast among saline solutions corresponded to theoretical dielectric properties, while the ultrasonic contrast between saline and oil reflected their difference in acoustic impedance. The system identified multiple brain tissue structures, including the cortex, hippocampus, superior colliculus, corpus callosum, cingulate cortex, and striatum. The bimodal imaging approach exhibited superior performance, visualizing tissue structures with greater clarity and detail than either modality alone. The brain tissue images were consistent with physical photographs, tissue dielectric properties, and publicly available anatomical atlases. The bimodal system clearly delineated cartilage and epiphyseal lines via thermoacoustic imaging, while ultrasonic imaging revealed bone structures. Thermoacoustic imaging alone differentiated bone sections between normal and osteoporotic groups; however, incorporating prior skeletal contour information from ultrasound significantly enhanced discriminatory power, resulting in intergroup differences with higher statistical significance. The imaging results of bone samples corresponded well with physical photographs, micro-CT images, and theoretical analyses of dielectric properties for cartilage, normal bone, and osteoporotic bone.Conclusion The microwave-induced thermoacoustic and ultrasound dual-modality microscopy system developed in this study demonstrates potential for microscopic detection of complex biological tissues based on dielectric properties. It is expected to provide a new imaging tool for functional assessment of brain tissue and the skeletal system, as well as for studies on disease pathogenesis.

Driven by the construction of "New Medical Sciences" and the educational digitalization strategy, there is an increasingly urgent demand in medical education for compound talents who possess a solid professional foundation, scientific research literacy, and clinical innovation capabilities. To address the problems existing in traditional physiology courses—including insufficient training of high-order thinking, delayed scientific research initiation, and a single evaluation mechanism—this study, with the concept of outcome-based education (OBE) as the guide and supported by constructivist and inquiry-based learning theories, has constructed and implemented a new "Teaching-Learning-Research Integration" blended online-offline curriculum model for physiology. The curriculum promotes reforms systematically from four dimensions. First, in the online dimension, it upgrades resources such as micro-courses and virtual simulation experiments, and optimizes self-directed learning paths. Second, in the offline dimension, it reconstructs flipped classrooms to strengthen the discussion of scientific research cases and interactive inquiry. Third, it expands in-depth scientific research guidance and builds a stepped scientific research training system through SRIP projects and discipline competitions. Fourth, it reforms the multi-dimensional evaluation mechanism by integrating process-oriented assessment and scientific research literacy evaluation. The practical results show that students" mastery of basic physiology knowledge has been significantly improved; the effectiveness of cultivating their scientific research literacy and professional literacy, as well as their overall course satisfaction, have all been enhanced. Meanwhile, the teaching and research capabilities of the teacher team have been synchronously strengthened, achieving the goal of "mutual promotion between teaching and research". This study confirms the effectiveness and promotion value of the in-depth integration of "Teaching-Learning-Research" in physiology courses. It provides a replicable and transferable model reference for the reform of basic medical courses under the background of "New Medical Sciences" and holds important practical significance for systematically improving the scientific research literacy and innovation capabilities of medical talents.

Objective This study aimed to comprehensively investigate the potential protective effects and underlying mechanisms of taurine against dihydrotestosterone (DHT)-induced androgenetic alopecia (AGA) in male C57BL/6 mice, with a focus on hair follicle cycle modulation, cellular proliferation/apoptosis, and key related signaling pathways.Methods Six-week-old female C57BL/6 mice were initially used to assess the hair growth-promoting potential of taurine. After acclimatization, they were randomly assigned to three groups (n=8 per group): control (regular drinking water), taurine (drinking water containing 1% taurine), and minoxidil (topical 2% minoxidil, positive control). For the AGA study, male C57BL/6 mice were randomly divided into five groups (n=8 per group): control (physiological saline), DHT (model group, 1 mg/d DHT ), DHT+low-dose taurine (1 mg/d DHT + 2 mg/d taurine), DHT+high-dose taurine (1 mg/d DHT + 10 mg/d taurine), and DHT+minoxidil (positive control, 1 mg/d DHT + topical 2% minoxidil). One day before treatment initiation, dorsal hair was shaved with scissors, and residual hair was removed using a depilatory cream. DHT and taurine were administered via daily intraperitoneal injection. Hair regrowth was assessed by photographing the depilated area at regular intervals and quantified using a four-point grading system (0–3). Dorsal skin samples were collected on day 14 for histological analysis (H&E staining), immunofluorescence staining (Ki67 for proliferation, TUNEL for apoptosis), ELISA (DHT quantification), RT-qPCR, and Western blot analysis to evaluate the expression of key genes and proteins (androgen receptor (AR), transforming growth factor (TGF)-β1, TGF-β2, Dickkopf-1 (DKK1)).Results In female mice, taurine supplementation significantly accelerated hair growth, with effects comparable to minoxidil. This was evidenced by an earlier transition from pink (telogen) to black (anagen) skin and increased hair growth scores. Histological analysis showed that taurine increased hair follicle count and dermal thickness. Immunofluorescence confirmed enhanced keratinocyte proliferation in the hair matrix. In the DHT-induced AGA model, DHT significantly extended the telogen phase, inhibited hair growth, increased skin DHT content, and induced hair follicle miniaturization. Taurine treatment, particularly at the high dose, effectively counteracted these effects: it promoted the telogen-to-anagen transition and improved hair growth scores. Histomorphometric analysis showed that taurine significantly restored DHT-induced reductions in dermal thickness, hair follicle count, hair bulb depth, and follicle size. Taurine treatment also reduced apoptosis and promoted the proliferation of hair follicle cells, as demonstrated by Ki67 and TUNEL assays. Crucially, RT-qPCR and Western blot analyses revealed that DHT significantly up-regulated the expression of AR, TGF-β1, TGF-β2, and DKK1 at both mRNA and protein levels in dorsal skin. Taurine administration markedly down-regulated the expression of these pathogenic factors, bringing them closer to the levels observed in the control group.Conclusion Taurine demonstrates significant efficacy in alleviating DHT-induced AGA in male C57BL/6 mice. Its protective effects are mediated through multi-faceted mechanisms: (1) Promoting hair follicle cycle progression: It accelerates the transition from telogen to anagen, counteracting DHT-induced prolongation of the telogen phase. (2) Modulating cellular dynamics: It stimulates the proliferation of hair matrix keratinocytes and reduces DHT-induced apoptosis within hair follicle cells. (3) Suppressing androgen-driven pathogenic pathways: It downregulates the expression of critical molecules in the AGA pathway, including AR, the cytokines TGF-β1 and TGF-β2, and the Wnt pathway inhibitor DKK1. Given its favorable safety profile and multi-targeted action, taurine emerges as a promising novel therapeutic candidate or adjunct for treating AGA. Further investigation into its clinical potential and precise molecular mechanisms is warranted. This study provides a robust preclinical foundation for considering taurine supplementation or topical application in hair loss management strategies.

Mendel established the laws and laid the foundation of modern genetics through his famous hybridization experiments on seven pairs of classic traits in the garden pea (Pisum sativum). However, the molecular bases underlying these traits have only come into sharp focus in recent years. Leveraging advances in traditional map-based cloning, TILLING, long-read resequencing, population genetics, and GWAS, this article synthesizes current knowledge of ten genes governing seven traits—plant height, seed shape, flower color, seed color, pod color, pod morphology, and flower position—by summarizing each gene’s identity, chromosomal localization, and functional pathway. For plant height, the classical Le locus corresponds to PsGA3ox1, which encodes a gibberellin 3β-hydroxylase. Mutations at Le impede the biosynthesis of the bioactive hormone GA₁, and the resulting deficiency leads to a dwarf or reduced-stature phenotype. Seed shape is determined by R, identified as PsSBEI (starch-branching enzyme I). Insertion of a transposable element into R restricts amylopectin synthesis, perturbing endosperm starch architecture and resulting in the wrinkled seeds noted by Mendel. Flower color is specified by the coordinated action of A (a bHLH transcription factor) and A2 (a WD40 scaffold). Together, they assemble the canonical MYB-bHLH-WD40 (MBW) regulatory complex, which co-activates structural genes in the anthocyanin pathway to determine pigment accumulation and floral hue. Seed color is governed by I, which encodes PsSGR (STAY-GREEN), a magnesium dechelatase that catalyzes a key step in chlorophyll catabolism. Loss-of-function alleles at I block chlorophyll degradation, yielding “stay-green” seeds in which chlorophyll persists beyond normal developmental stages. Pod coloration maps to Gp, corresponding to ChlG (chlorophyll synthase). Either direct loss of ChlG function or readthrough-fusion transcriptional interference caused by a large upstream deletion suppresses chlorophyll biosynthesis in developing pods, resulting in the yellow-pod phenotype. Pod morphology depends on two convergent regulatory pathways. The P gene, PsCLE41, signals through the P-PXY-WOX/NAC axis to promote vascular differentiation and secondary-wall programs, while V encodes PsMYB26, a transcription factor that drives secondary wall thickening in fiber cells. Acting in concert, these modules ensure robust secondary-wall deposition in the fiber layer lining the inner pod wall; disruption of either component compromises wall thickening and leads to pleated or wrinkled pods. Flower position (inflorescence determinacy at the shoot apex) is controlled by FA, identified as PsCIK, which participates in the CLAVATA-WUSCHEL (CLV-WUS) feedback circuit that maintains shoot apical meristem homeostasis. Mutations in FA destabilize this self-regulatory loop and promote terminal flowers at the apex. The expressivity of this determinacy phenotype is further modulated by a recessive modifier, Mfa, which fine-tunes the outcome in the fa background. Across these loci, convergent evidence highlights the central role of structural variation in generating the classical Mendelian phenotypes. Building on this clarified molecular landscape, we outline practical implications for quality improvement and the deliberate “design” of traits. Looking ahead, we envisage a next generation of legume genetic improvement anchored on three mutually reinforcing pillars: high-quality reference genomes to deliver contiguous, structurally faithful assemblies; comprehensive pan-genomes to capture presence/absence variation and structural polymorphism across germplasm; and precise gene editing to target coding, regulatory, and structural features alike. Together, these tools chart a path toward mechanism-based breeding, enabling purposeful, design-driven trait improvement in peas and, by extension, other legumes.

Mitochondria are the most crucial energy-generating organelles in eukaryotic cells and serve as signaling hubs that orchestrate metabolism, redox balance, cell-fate decision and multiple forms of cell death. Mitochondria possess their own DNA (mtDNA), which is independent of the nuclear genome, yet encodes only 13 polypeptides, 22 tRNAs, and 2 rRNAs. The remaining >1 150 mitochondrial proteins are encoded by nuclear genes (nDNA), and the two genomes cooperate to preserve cellular homeostasis and proper function. Mitochondrial proteins are localized to the outer mitochondrial membrane (OMM), intermembrane space (IMS), inner mitochondrial membrane (IMM) or matrix, participating in oxidative phosphorylation (OXPHOS), the tricarboxylic acid cycle (TCA), fission-fusion dynamics, and other processes indispensable for mitochondrial integrity. Mitochondrial quality control (MQC) is exerted largely by mitochondrial proteases, which selectively modulate protein activity and degrade misfolded or superfluous proteins. Among them, a group of mitochondrial AAA+ proteases (ATPases associated with diverse cellular activities) couple ATP binding and hydrolysis to protein unfolding and proteolysis, thereby regulating fusion protein maturation, respiratory-chain assembly, and mtDNA replication/transcription. Mutations or aberrant expression of these mitochondrial AAA+ proteases cripple mitochondrial architecture and function, precipitating a spectrum of severe neurological disorders. This review summarizes current knowledge on three paradigmatic mitochondrial AAA+ proteases, LONP1, YME1L1, and AFG3L2. We highlight their conserved Walker A/B motifs in the ATPase domain and hexameric architecture, yet emphasize divergent sub-mitochondrial topologies: LONP1 is soluble in the matrix, whereas YME1L1 and AFG3L2 are embedded in the IMM with catalytic domains facing IMS and matrix, respectively. These positional differences translate into distinct substrates and proteolytic strategies, enabling a division of labor and mutual complementation that cooperatively safeguards mitochondrial proteostasis. Pathogenic mutations linked to common and rare neurological disorders are mapped predominantly to the ATPase and the hydrolase/peptidase domains. Substitutions of the amino acid within these core domains can directly abolish ATP hydrolysis, substrate engagement or peptide cleavage, thereby crippling local MQC networks. Additional variants may disturb transcriptional, translational or post-translational regulation, altering protease stoichiometry and impairing compartmental balance. The subsequent cascade, mtDNA instability, respiratory-chain dysfunction, and aberrant mitochondrial dynamics, propagates stress signals that culminate in neuronal dysfunction and/or neurodegeneration. The mutational and clinical heterogeneity observed across cell types, developmental stages, and genetic backgrounds underscores the context-dependent fine-tuning of these AAA+ proteases. Deciphering how disease-associated variants rewire domain structure, catalytic cycle, and network-level crosstalk will therefore illuminate pathophysiologic mechanisms and guide precision therapeutic strategies.

In recent years, immunotherapy has become an excellent option for cancer patients, but most patients still face problems such as low response or drug resistance. Therefore, researchers conducted extensive studies on the reasons for the poor efficacy of immunotherapy. Eventually, it was found that the regulatory effect of abnormal expression of oncogenes and tumor suppressor genes on the tumor immune microenvironment is one of the important factors leading to the failure of immunotherapy to achieve the expected efficacy. It is well known that cancer is a kind of disease caused by the interaction between environmental and genetic factors, and the occurrence of cancer is mainly related to genetic alteration. Physiologically, the balance between oncogenes and tumor suppressor genes is crucial for DNA replication and proliferation regulation. However, under certain conditions, such as viral infection, chemical carcinogens or radiation, these genes may be mutated and eventually induce cancer. In addition, the combination of different gene mutations can also lead to significant differences among patients. For example, certain gene mutations are associated with the metastasis of cancer cells, while some are associated with the resistance of cancer cells to the attack of immune cells. Therefore, exploring the effects of different genetic alterations on the tumor microenvironment can help us better solve the problems in the process of clinical treatment and provide a theoretical basis for designing gene-targeted and personalized therapies. This review mainly summarizes the effects of common oncogenes and tumor suppressor gene mutations on immunosuppressive cells, anti-tumor immune effector cells and tumor-associated fibroblasts in the tumor microenvironment. Firstly, when the oncogene KRAS, cMyc and EGFR are abnormally activated, cancer cell will secrete various cytokines and chemokines, thereby recruiting various immunosuppressive cells to the TME and causing exhaustion of CD8⁺ T and NK cells. It can also reprogram CAFs and eventually promote the development of cancer. Furthermore, similar phenomena occur after the inactivation of tumor suppressor genes. For example, cancer cells with inactivated PTEN genes will secrete large amounts of IL-33 and LOX to recruit macrophages and induce TAMs. Cancer cells can secrete a variety of microRNAs into the tumor microenvironment after p53 dysregulation. These mircoRNAs can reprogram CAFs and lead to epithelial-mesenchymal transition. Finally, we summarize the reversing effects of therapeutic interventions targeting mutant oncogenes or tumor suppressor genes (such as KRAS inhibitors, overexpression of p53 by mRNA, PI3Kβ inhibitors) on the immunosuppressive tumor microenvironment. Some of the results of their synergistic effects in combination with immunotherapy are also listed. Compared with monotherapy, the combination of either KRAS inhibitor or p53 mRNA nanomedicine with αPD-1 therapy resulted in more durable and potent anti-tumor effects. In summary, this review elucidates the regulatory and remodeling effects of genetic alterations in tumor cells on the tumor immune microenvironment, and analyzes the great potential of gene alteration intervention combined with immunotherapy. We hope it can provide theoretical basis and development strategy for precise cancer immunotherapy.

Neurodegenerative diseases (NDs) are a group of disorders characterized by the progressive loss of neuronal structure and function, leading to clinical manifestations such as cognitive decline, motor dysfunction, and neuropsychiatric abnormalities. NDs encompass a range of conditions, including Alzheimer"s disease (AD), Parkinson"s disease (PD), and amyotrophic lateral sclerosis (ALS), etc. With the intensifying trends of global population growth and aging, the incidence of NDs continues to rise, yet no curative treatments are currently available. The blood-brain barrier (BBB) plays a crucial role in maintaining central nervous system (CNS) homeostasis by blocking harmful substances in the bloodstream from entering brain tissue. More than 98% of small-molecule drugs and nearly 100% of large-molecule therapeutics fail to cross the BBB and reach brain parenchyma. Ultrasound-targeted microbubble destruction (UTMD) is an emerging interdisciplinary technology integrating materials science and bioengineering, which combines the advantages of microbubble carriers with the physical properties of ultrasound. This innovative approach enables transient and reversible opening of the BBB, and enhancing drug delivery efficiency. Microbubbles (MB) are the core component of the UTMD system, consisting of two fundamental structural elements: a gaseous core and a biocompatible outer shell. The drug-loading capacity of MB has been significantly expanded, evolving from traditional chemotherapeutic agents to encompass nucleic acid drugs, macromolecular antibodies, and even traditional Chinese medicines. Concurrently, their drug-loading strategies have advanced from initial passive physical adsorption to active targeted delivery. UTMD possesses the following 4 biological advantages. (1) UTMD can transiently and reversibly enhance the permeability of cell membranes and blood vessels. The biocompatible shells commonly used in microbubbles can be metabolized by the body, posing no risk of long-term accumulation. (2) UTMD not only significantly improves drug delivery efficiency but also simultaneously serves as an ultrasound contrast agent and therapeutic carrier, achieving the integration of diagnosis and treatment. (3) UTMD technology offers dual advantages of spatial targeting and molecular targeting, allowing for precise drug delivery. (4) UTMD only requires conventional ultrasound equipment, and the raw materials for microbubble preparation are readily available with simple synthesis processes. Whether applied in diagnostics or treatment, the cost remains relatively low. The mechanism by which UTMD opens the BBB is primarily associated with cavitation effect and sonoporation effect. The cavitation effect induces mechanical stretching of both cellular membranes and capillary walls, creating transient, reversible channels that facilitate macromolecular drug passage, to enhance BBB permeability. Meanwhile, the sonoporation effect promotes drug penetration through dual mechanisms: (1) augmenting passive diffusion across biological barriers; (2) potentiating active transport processes. This synergistic action significantly elevates both local drug concentrations and therapeutic efficacy at target sites. The permeability of the BBB is predominantly influenced by both microbubble characteristics and ultrasound parameters. Microbubble characteristics and ultrasound parameters are key factors affecting BBB permeability. By adjusting the composition of microbubbles and optimizing ultrasound parameters, effective BBB opening can be achieved while minimizing tissue damage, to regulate the dosage of drugs delivered to the brain parenchyma. Both preclinical investigations and clinical trials have consistently shown that UTMD holds significant therapeutic promise for NDs. This article outlines the fundamental properties of microbubbles and elucidates the potential mechanisms underlying UTMD mediated BBB opening. Furthermore, it systematically reviews recent advances in UTMD technology for the treatment of treating various NDs, aiming to provide a theoretical foundation and future directions for developing novel therapeutic strategies and drugs for NDs.

Objective In nature, objects cast shadows due to illumination, forming the basis for stereoscopic perception. Birds need to adapt to changes in lighting (meaning they can recognize stereoscopic shapes even when shadows look different) to accurately perceive different three-dimensional forms. However, how neurons in the key visual brain area in birds handle these lighting changes remains largely unreported. In this study, pigeons (Columba livia) were used as subjects to investigate how neurons in pigeon’s ventrolateral mesopallium (MVL) represent stereoscopic shapes consistently, regardless of changes in lighting.Methods Visual cognitive training combined with neuronal recording was employed. Pigeons were first trained to discriminate different stereoscopic shapes (concave/convex). We then tested whether and how light luminance angle and surface appearance of the stereoscopic shapes affect their recognition accuracy, and further verify whether the results rely on specify luminance color. Simultaneously, neuronal firing activity of neurons was recorded with multiple electrode array implanted from the MVL during the presentation of difference shapes. The response was finally analyzed how selectively they responded to different stereoscopic shapes and whether their selectivity was affected by the changes of luminance condition (like lighting angle) or surface look. Support vector machine (SVM) models were trained on neuronal population responses recorded under one condition (light luminance angle of 45°) and used to decode responses under other conditions (light luminance angle of 135°, 225°, 315°) to verify the invariance of responses to different luminance conditions.Results Behavioral results from 6 pigeons consistently showed that the pigeons could reliably identify the core 3D shape (over 80% accuracy), and this ability wasn’t affected by changes in light angle or surface appearance. Statistical analysis of 88 recorded neurons from 6 pigeons revealed that 83% (73/88) showed strong selectivity for specific 3D shapes (selectivity index>0.3), and responses to convex shapes were consistently stronger than to concave shapes. These shape-selective responses remained stable across changes in light angle and surface appearance. Neural patterns were consistent under both blue and orange lighting. The decoding accuracy achieves above 70%, suggesting stable responses under different conditions (e.g., different lighting angles or surface appearance).Conclusion Neurons in the pigeon MVL maintain a consistent neural encoding pattern for different stereoscopic shapes, unaffected by illumination or surface appearance. This ensures stable object recognition by pigeons in changing visual environments. Our findings provide new physiological evidence for understanding how birds achieve stable perception (“invariant neural representations”) while coping with variations in the visual field.

Objective In the clinical diagnosis and grading of brain glioma from histopathological slides, whole-slide cell nucleus density estimation is a critical task. This metric is a key biomarker directly correlated with tumor malignancy, proliferative activity, and patient prognosis, as defined by the World Health Organization (WHO) classification system. Glioma density estimation typically relies heavily on the performance of underlying nucleus segmentation. However, segmentation accuracy is challenged by substantial heterogeneity in nucleus morphology and significant staining variations both across slides and within individual specimens. This variability often causes standard semantic segmentation models to overfit the training data, leading to considerable errors in density estimation. Such inaccuracies can compromise downstream pathological assessments, particularly the subjective and time-consuming manual selection of regions of interest (ROI) for grading. To address these limitations, this study aims to develop a precise and robust whole-slide nucleus density estimation method that enhances model generalization and mitigates overfitting, thereby providing an objective, automated tool for glioma analysis.Methods We propose a systematic three-stage pipeline. (1) Preprocessing: whole-slide images (WSIs) of glioma undergo comprehensive preprocessing, including automated data cleaning to discard blurry or artifact-contaminated patches, data augmentation through geometric transformations (e.g., rotation, flipping) to increase dataset diversity, and color normalization. The latter, based on RGB channel ratios, remaps the color space of all patches to a standardized target, reducing domain shifts caused by staining inconsistencies and improving model robustness. A rigorous semi-automated ground-truth annotation protocol is also implemented, where initial binarization assists annotators in accurately labeling even faint or blurry nuclei, ensuring high-quality training data. (2) Segmentation: using the preprocessed patches, we construct a U-net-based segmentation model that incorporates the DropBlock regularization module—here termed U-net+DropBlock. Unlike standard Dropout, which removes individual neurons, DropBlock eliminates contiguous, spatially correlated regions within feature maps. This structural regularization disrupts undesirable spatial dependencies, forcing the network to learn a more distributed and robust feature representation, thereby reducing overfitting. (3) Quantitative analysis: for each segmented patch, density is computed as the ratio of the total nucleus area to the total patch area—a more robust approach than simple nucleus counting, as it accounts for variations in nucleus size. Patch-wise density values are then assembled into a whole-slide density heatmap, offering an intuitive, global overview of tumor cellularity.Results The U-net+DropBlock model was evaluated both quantitatively and qualitatively against state-of-the-art nucleus segmentation methods, including standard U-net and Hover-net. Quantitatively, our model achieved an F1 score of 90.1%, outperforming U-net and Hover-net, which both scored 87.6%. Qualitative analysis confirmed that our method effectively balances precision and recall, substantially reducing the over-segmentation artifacts common with U-net and the under-segmentation issues observed with Hover-net. This enhanced segmentation quality directly improved the accuracy and reliability of the proposed density estimation approach.Conclusion The proposed whole-slide nucleus density estimation method provides a powerful tool for improving the precision and efficiency of glioma diagnosis. By enabling automated, rapid, and objective analysis of cellular density, it overcomes key limitations of manual pathological review. The generated heatmaps allow pathologists to rapidly identify high-density “hotspots” critical for accurate grading and prognostic evaluation, supporting a more standardized and reproducible ROI selection process. This work lays a solid foundation for developing advanced AI-assisted diagnostic systems, paving the way for more precise, efficient, and reproducible glioma assessments in clinical practice.

Spores and pollen, as ubiquitous organisms found in nature, possess a remarkable core-shell structure and intricate surface morphology. These tiny particles are notable for their dimensional uniformity, sustainable utilization, environmental friendliness, porosity, amphiphilicity, and strong adhesive properties. In addition, they display excellent biocompatibility and biodegradability, which significantly enhances the stability and targeting of drugs within the body. Spores and pollen can be extracted using methods such as acidic solutions, alkaline solutions, or enzyme treatments to obtain sporopollenin, which is an extremely resilient and chemically inert complex biopolymer. The sporopollenin extracted through this process removes the original bioactive substances, such as cell nuclei, enzymes, and DNA, providing greater drug loading capacity and containing no potential allergens or immunogens, thus further enhancing its drug loading capacity and improving safety in therapeutic applications. Due to these beneficial attributes, spores, pollen and sporopollenin have gained widespread use in a variety of drug delivery systems, such as targeted delivery, sustained drug delivery, toxicity mitigation, flavor masking, vaccine delivery, delivery of labile substances, and other applications. This review introduces the types of natural spores and pollen commonly used in drug delivery systems, including their main components, common effects, and uses in drug delivery systems, and so on. It subsequently summarizes novel optimization methods in their processing, such as physical treatment, surface modification, and chemical modification, which enable higher drug loading efficiency, stability, and targeting, among other benefits. Additionally, this paper reviews the research progress and applications of natural spores, pollen, and sporopollenin in drug delivery systems, while also touching on some innovative research content, such as novel nanomotor microcarriers developed based on pollen. Based on these research findings, we further elaborate on the advantages of spores, pollen, and sporopollenin in drug delivery systems. For example, they have high stability and drug loading capacity, good adhesion, excellent targeting, and are easy to modify functionally. Currently, they show promising prospects in the fields of targeted drug delivery, sustained-release drug delivery, as well as the delivery of drugs that are effective but slightly toxic, and are often used in research on the treatment of diseases such as cancer and inflammation. We have also highlighted the challenges they face in various applications and identified some issues that need to be addressed, including difficulties in large-scale production, the need to improve extraction and purification processes, and the existence of a low but still noteworthy risk of allergies, in order to fully leverage their potential in drug delivery applications. According to current research, although spores, pollen, and sporopollenin face some unresolved issues in clinical drug delivery, they still have great potential overall and are expected to become a new generation of green drug delivery platforms. In the future, further research into their unique physical and chemical properties and structural characteristics will help develop more efficient and stable drug delivery systems to meet diverse treatment needs. We believe that continued exploration of natural spores, pollen, and sporopollenin will drive this emerging field to achieve continuous breakthroughs and progress, ultimately making an important contribution to the cause of human health.

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 (K_D 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 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 (frequency, intensity, time, and type) principle, 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.

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.

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.

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.

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.