Abstract:Objective Alcohol use disorder (AUD) is a chronic, relapsing brain disorder characterized by compulsive alcohol drinking and impaired control over alcohol use. Its pathogenesis involves functional abnormalities in neural circuits underlying reward, cognitive control, and emotion regulation. Current clinical assessments primarily rely on psychometric scales, which rely on subjective experience, are susceptible to recall bias and concealment, and provide limited insight into the aberrant neural responses underlying AUD. Addiction involves state-dependent, non-stationary neural dynamics, necessitating analytical approaches capable of capturing the time-varying features of brain states. Microstate analysis characterizes the spatiotemporal dynamics of brain activity by identifying brief, recurrent, quasi-stable spatial patterns in electroencephalography (EEG), and thus provides a useful tool for investigating neural mechanisms in resting-state and task states. Previous studies have shown that microstate parameters can reflect addiction-related pathological craving and functional impairment. However, the alcohol cue-induced temporal reorganization of microstates in patients with AUD remains to be investigated. In this study, we applied microstate analysis to investigate aberrant microstate patterns in patients with AUD in response to alcohol cue exposure.Methods Twenty-one patients with AUD admitted to Shandong Daizhuang Hospital from October 2023 to January 2024 were enrolled. All participants met the ICD-11 diagnostic criteria. A visual cue exposure paradigm containing alcohol cues was used. EEG data were recorded sequentially during eyes-open resting state, neutral cue exposure, and alcohol cue exposure, with eyes-closed resting-state data collected after each condition for microstate analysis. Subjective craving intensity was assessed using a visual analogue scale (VAS) before and after cue exposure.Results The microstate topographies across conditions were largely consistent with the four topographies of microstate classes A–D. Compared with the resting state, after exposure to alcohol cues, AUD patients showed a significant increase in the mean duration (t=4.341, P<0.001), coverage (t=12.645, P<0.001), and occurrence (t=2.740, P=0.030) of microstate B, while the mean duration (t=–5.196, P<0.001) and coverage (t=–7.623, P<0.001) of microstate A significantly decreased. Under neutral cues, the occurrence (t=5.806, P<0.001), mean duration (t=6.541, P<0.001), and coverage (t=9.496, P<0.001) of microstate C significantly increased. In addition, under alcohol cues, the transition probabilities between microstates A and B and between microstates C and D were significantly increased (A→B: t=12.401, P<0.001; B→A: t=2.637, P=0.024; C→D: t=3.390, P=0.007; D→C: t=3.690, P=0.004), whereas the bidirectional transition probabilities between microstates A and C were significantly decreased (A→C: t=–7.861, P<0.001; C→A: t=–3.253, P=0.008).Conclusion Alcohol cues can induce the retrieval of past drinking experiences and pathological craving in patients with AUD. These cue-elicited responses are reflected in EEG microstate parameters, which could serve as potential electrophysiological markers of cue reactivity in AUD and provide a basis for understanding the neural mechanisms underlying alcohol addiction.

Abstract:无

Abstract:Bacterial biofilms are major causes of chronic infections and medical-device-associated infections. Their persistence and resistance to antibiotics and host immunity impose substantial clinical and economic burdens. Non-invasive, dynamic, and quantitative characterization of biofilms is therefore essential for anti-infective research, drug development, and clinical risk assessment. Electrochemical impedance spectroscopy (EIS) has been widely used for biofilm monitoring because it is real-time, in situ, and non-destructive. However, EIS interpretation remains dominated by equivalent circuit models, which simplify biofilms into ideal electrical elements. Although equivalent circuit models are intuitive and convenient, they have fundamental limitations: fitted parameters often lack clear physical meaning; different circuit topologies can fit the same spectrum; model selection is subjective; and the models have limited predictive power and cannot directly retrieve intrinsic biophysical properties. These limitations restrict EIS from advancing from status monitoring to mechanistic interpretation. Transmission-line models, dielectric-spectrum theory, and machine-learning approaches have been attempted, but a unified theoretical framework that quantitatively links the multi-dimensional physical properties of biofilms to broadband impedance responses is still lacking. This review systematically examines the progress and bottlenecks of biofilm impedance analysis and proposes a physics-driven paradigm. The central hypothesis is that, within an appropriate frequency window, an interfacial biofilm can be approximated as a quasi-homogeneous dielectric membrane. This approximation does not deny microscopic heterogeneity; rather, it assumes that when the characteristic spatial scale of the applied field is much larger than that of local cellular aggregates, pores, and extracellular polymeric substances, the biofilm behaves as a continuous dielectric medium governed mainly by dielectric polarization and ionic migration rather than by faradaic electrochemical reactions. On this basis, a minimal operable physical model is constructed using Maxwell""s equations, dielectric relaxation theory, and thermodynamic constraints. The model adopts an electrode-biofilm-electrolyte geometry with appropriate boundary conditions and a frequency-dependent complex permittivity. Combined with physical priors and Bayesian inversion, it aims to retrieve a minimal set of intrinsic quantities from EIS data: biofilm thickness, biomass, conductivity, dielectric constant, and interfacial adhesion-related equivalent electromagnetic characteristics. These parameters can be independently cross-validated by confocal laser scanning microscopy, quartz crystal microbalance, conductivity measurements, and microfluidic mechanical assays, and can be correlated with biological features such as metabolic activity, three-dimensional structure, mechanical properties, interfacial stability, and stress resistance. Compared with conventional equivalent circuit models, the proposed framework offers clearer physical definitions, improved inversion uniqueness through physical constraints such as Kramers-Kronig consistency, predictive capability through the evolution of physical parameters, and compatibility with independent non-electrochemical validation. Because multi-parameter inversion from limited-band EIS data is inherently ill-posed, the framework requires physical priors, multi-frequency measurements, orthogonal experimental calibration, and multimodal constraints to reduce parameter coupling and non-uniqueness. The validity of the quasi-homogeneous approximation must be further defined for highly heterogeneous, disrupted, or non-continuous biofilms, and the retrieved parameters must be cross-checked across strains and culture conditions. Nevertheless, this physics-based paradigm may enable EIS to provide quantitative physical portraits of biofilms rather than merely indicating whether biofilms are present or increasing. It may also be extended to cell monolayers, stem-cell layers, organoid sheets, and other two-dimensional interfacial living systems, offering a new theoretical route for spectroscopic characterization of biological interfaces.

Abstract:Parvoviruses are small, non-enveloped, single-stranded DNA viruses. Unlike most oncolytic platforms, several members inherently prefer rapidly proliferating tumor cells, not through genetic engineering but because their life cycle depends on mitotic host cells. Their non-structural protein 1 (NS1) is the principal effector of this natural oncolytic activity. Integrating current knowledge of its structure, tumor-killing mechanism and translational development, we argue that NS1 constitutes a distinct class of oncolytic agents: a structurally definable and engineerable cytotoxic effector whose antitumor value does not depend on viral replication. Structurally, NS1 comprises three modules acting in concert: an N-terminal HUH endonuclease domain, a central helicase/ATPase domain, and C-terminal transactivation domain. These endow NS1 with site-specific DNA endonuclease and helicase activities as well as pro-apoptotic activity, forming the molecular basis of the intrinsic oncolytic effect of the genus parvovirus. Mechanistically, the N-terminal endonuclease introduces single-strand breaks into host genomic DNA; separately, the C-terminal transactivation domain (TAD2) activates ataxia-telangiectasia mutated and Rad3-related kinase (ATR), which phosphorylates CDC25C at Ser216 and thereby inactivates the cyclin B/CDK1 complex, imposing a p53-independent G2 arrest. Because p53 is the most frequently mutated gene in the majority of human malignancies, this p53 independence is central to the broad applicability of NS1, and p53-deficient tumor cells are not less sensitive to NS1 than their wild-type counterparts. In parallel, mitochondrial damage induced by NS1 promotes sustained opening of the mitochondrial permeability transition pore (mPTP), collapse of the mitochondrial membrane potential, release of cytochrome c, an increased Bax/Bcl-2 ratio and ultimately caspase-9/3/7-dependent apoptosis. When apoptotic cells are not promptly cleared, secondary necrosis releases damage-associated molecular patterns (DAMPs) including HMGB1, extracellular ATP, HSP70 and tumor-associated antigens; notably, H-1PV infection elevates extracellular ATP release in a dose-dependent manner. These DAMPs, together with cGAS-STING activation triggered by mitochondrial DNA leakage, convert local killing into systemic immune surveillance: macrophages polarize from M2 towards an inflammatory M1 phenotype through TLR4 and P2X7 signaling and secrete TNF-α, IL-12 and IL-1β together with the chemokines CXCL9/CXCL10 that recruit natural killer (NK) cells and CD8⁺ T cells, while modulation of natural cytotoxicity receptor (NCR) ligands sensitizes NS1-expressing tumor cells to NK-mediated killing. The translational evidence forms a four-tier chain: broad cytotoxicity across human tumor cell lines; in vivo efficacy in rodent tumor models; clinical activity and safety in canine spontaneous tumors, which owing to their genetic heterogeneity and intact immune system predict human outcomes better than rodent models; and, for H-1PV, two ParvOryx trials (a phase I/IIa study in glioblastoma and a phase II study in unresectable metastatic pancreatic ductal adenocarcinoma) demonstrating a favorable safety profile with evidence of immune activation. Four interlocking bottlenecks still separate NS1 from routine clinical use: host immune clearance of delivery vectors, insufficient tumor-targeted delivery, the manufacturing paradox arising from NS1 cytotoxicity to producer cells, and tumor resistance and immune evasion. We therefore propose 3 viewpoints. First, the field is moving from oncolytic whole viruses towards a defined NS1 effector drug: non-viral delivery of NS1-encoding plasmids by lipoplexes or engineered exosomes would decouple NS1 cytotoxicity from viral replication and alleviate the constraints of immune clearance, biosafety and manufacturing. Second, companion-dog spontaneous tumor trials constitute the most leveraged translational bridge and should serve as the mandatory pre-registration platform for NS1-based agents. Third, the intrinsic immunogenicity of NS1-induced cell death makes NS1 a natural partner for immune checkpoint inhibitors, and cold-to-hot conversion of the tumor microenvironment should guide rational combination design. This integrated picture should inform the development of NS1-based oncolytic drugs over the next five to ten years.

Abstract:Ferroptosis is a form of regulated cell death driven by iron-dependent lipid peroxidation, characterized by morphological and biochemical features and is mechanistically distinct from apoptosis, necroptosis, and classical necrosis. It is widely involved in tumor initiation and progression, metabolic reprogramming, therapeutic responses, and both primary and acquired drug resistance. The tumor microenvironment frequently imposes multiple stresses, including oxidative stress, nutrient deprivation, hypoxia, and chemotherapeutic insults. In response, tumor cells employ post-translational modifications to rapidly alter the abundance, catalytic activity, and subcellular localization of target proteins, thereby acquiring adaptive survival advantages. The ubiquitin-proteasome system (UPS) is a central system for maintaining protein homeostasis in eukaryotic cells. Through the coordinated action of the ubiquitin-activating enzyme (E1), ubiquitin-conjugating enzyme (E2), and ubiquitin-protein ligase (E3) cascade and deubiquitinase (DUB), it enables substrate-specific and reversible regulation of protein function. In the tumor microenvironment, E3 ubiquitin ligases and DUBs jointly regulate the protein stability and functions of core ferroptosis effectors, including solute carrier family 7 member 11 (SLC7A11), glutathione peroxidase 4 (GPX4), ferroptosis suppressor protein 1 (FSP1), and acyl-CoA synthetase long-chain family member 4 (ACSL4). These regulators influence three major pathways: iron homeostasis, lipid peroxidation, and cellular antioxidant defense. This regulation reshapes the capacity of tumor cells to withstand oxidative damage and pharmacological stress. Consequently, the UPS has emerged as a critical post-translational regulatory node that determines ferroptosis sensitivity in tumor cells. Although previous reviews have discussed ferroptosis in cancer and ubiquitination in tumor biology, an integrated understanding of how the dynamic balance between E3 ligases and DUBs orchestrates tumor ferroptosis, and how these mechanisms may be therapeutically exploited, remains needed. This review focuses on the UPS-mediated regulatory network of tumor ferroptosis. We summarize the molecular mechanisms by which E3 ligases and DUBs regulate iron homeostasis, lipid metabolic reprogramming, and antioxidant defense, and emphasize that this regulation is markedly context-dependent and bidirectional. We also systematically evaluate the translational potential of various proteostasis-targeting tools to modulate ferroptosis. In addition to proteolysis-targeting chimeras (PROTAC) and deubiquitinase-targeting chimeras (DUBTAC), we discuss emerging technologies such as molecular glue, autophagy-targeting chimeras (AUTAC), and lysosome-targeting chimeras (LYTAC) in the context of tumor ferroptosis. These modalities offer distinct strategies for either degrading or stabilizing ferroptosis-related proteins. Furthermore, we explore the sensitizing value of targeting the ubiquitination-ferroptosis axis in combination with chemotherapy, radiotherapy, and immunotherapy, and analyze the theoretical rationale for and translational barriers to such combination regimens. Currently, clinical translation in this field remains constrained by multiple bottlenecks: the ubiquitination sites and chain types of ferroptosis-related substrates have not been fully characterized; the impact of tumor heterogeneity on UPS-ferroptosis regulatory circuits lacks systematic investigation; clinically validated predictive biomarkers for identifying patients likely to benefit are still scarce; and the in vivo safety evaluation framework for UPS-targeting small-molecule agents remains to be established. To address these limitations, we propose integrating ubiquitinomics, lipidomics, and single-cell multi-omics to construct panoramic regulatory maps, developing tumor microenvironment-responsive delivery systems to optimize the therapeutic window, and outlining directions for translating basic mechanistic findings into precision cancer therapy. Addressing these challenges will require collaborative efforts across chemistry, biology, and clinical oncology. In summary, this review aims to deepen the understanding of proteostasis-mediated ferroptosis regulation in tumors and to provide theoretical references for the development of novel anticancer interventions.

Abstract:Social interaction is a continuously unfolding process in which partners adjust their coordination in response to changing goals, task phases, roles, strategies, and ongoing feedback. Such coordination is also shaped by prior interaction experience. Inter-brain synchrony (IBS), defined as the temporal coordination of neural activity across individuals during social interaction, provides an important window into the neural mechanisms underlying interpersonal coordination. Existing research shows that IBS varies across interaction contexts and changes over the course of an interaction. Prior interactions, repeated training, and external interventions may also influence subsequent patterns of IBS. This review synthesizes evidence for these changes and their relationships with behavioral coordination and interaction outcomes. It conceptualizes IBS flexibility along two interrelated dimensions —adaptability and experientiality—distinguished primarily by the factors on which IBS changes depend. Adaptability refers to dynamic adjustments in IBS in response to current interaction goals, task phases, role allocation, strategies, and feedback. These adjustments may manifest as increases or decreases in synchrony, transitions between distinct IBS states, or reconfiguration of inter-brain networks. Experientiality refers to the influence of prior interaction experience or external interventions on subsequent IBS. Such effects may manifest as post-task residual synchrony, changes across repeated interactions or training sessions, and alterations induced by noninvasive brain stimulation or multi-brain neurofeedback. Changes within an ongoing interaction may reflect both current interaction demands and accumulated experience. Identifying experience-dependent effects therefore requires distinguishing the phase in which experience is acquired from a subsequent assessment phase, while accounting for current interaction demands. Much of the available evidence comes from cross-sectional comparisons or assessments at only two time points; direct evidence of persistence, recurrence in later interactions, or generalization across partners and contexts remains limited. At the mechanistic level, several processes may contribute to these two dimensions of flexibility. Metastability may serve as an overarching dynamical principle, allowing interactive systems to shift between integration and segregation rather than remain in a fixed state of high synchrony. Within this framework, adaptability may be driven by mutual prediction and prediction-error updating, which support real-time changes in coupling strength and inter-brain state transitions. Experientiality may reflect history-dependent reconfiguration of this metastable system through accumulated co-occurrence, relational learning, reactivation, offline consolidation, and selective retention of functionally relevant coupling patterns, thereby reshaping subsequent coupling tendencies, state-transition patterns, and recurrent coordination configurations. It should be noted that these proposed mechanisms draw primarily on theoretical frameworks derived from research on learning and plasticity within individual brains and require validation in future hyperscanning studies. This review also summarizes quantitative measures relevant to the two dimensions of IBS flexibility. Adaptability can be captured by temporal-dynamic indices, such as sliding-window trajectories, variability, state transitions, directionality, time-lagged coupling, and dynamic network organization. Experientiality can be quantified using measures of post-task residual coupling, pre-post differences in IBS, delayed retention or reinstatement, and longitudinal trajectories of growth or maintenance. These measures provide statistical descriptions of IBS patterns but are insufficient, on their own, to support inferences about adaptability or experientiality. Such inferences require linking observed patterns of IBS change to specific interaction events and behavioral changes. Guided by this framework, future research should combine dynamic analyses, tracking at multiple time points, and experimental manipulations to examine how IBS flexibility relates to interaction demands, behavioral adjustments, and interaction outcomes, as well as whether experience-related effects are retained or transferred across different interaction contexts. Such work may deepen our understanding of the neural mechanisms underlying dynamic interpersonal coordination and provide a theoretical basis for research and applications in educational and clinical settings.

Abstract:Objective Cysteine S-carboxyethylation is a recently identified post-translational modification with potential biological relevance, but its experimental identification is costly and time-consuming. Existing computational approaches mainly rely on sequence information and may not fully exploit positional differences around the central cysteine or the complementary representation capabilities of heterogeneous neural networks. This study aimed to develop an effective sequence-based model for prioritizing potential cysteine S-carboxyethylation sites.Methods We developed Cys-DHE, a dual-branch heterogeneous deep ensemble model for cysteine S-carboxyethylation site prediction. The benchmark dataset contained 1 204 training samples, including 602 positive and 602 negative samples, and an independent test set containing 913 samples, including 83 positive and 830 negative samples. Each sample was represented by a 41-aa sequence window centered on the target Cys. Binary-weight encoding (BWE) was used to combine amino-acid identity with a distance-dependent weighting mechanism, thereby emphasizing residues closer to the central site. The resulting 41×20 feature matrix was simultaneously input into two complementary branches: CNN-BiLSTM-Attention for extracting local patterns and bidirectional contextual dependencies, and CNN-Transformer for modeling cross-position interactions and long-range dependencies. The prediction probabilities of the two branches were integrated using fixed weights of 0.75 and 0.25, respectively, determined according to five-fold cross-validation results on the training set. Model performance was evaluated using sensitivity (Sn), specificity (Sp), accuracy (Acc), Matthews correlation coefficient (MCC), AUROC, and AUPR, and was further compared with individual branches and conventional machine-learning methods.Results In five-fold cross-validation, Cys-DHE achieved Sn, Sp, Acc, MCC, AUROC, and AUPR values of 0.680 3, 0.842 7, 0.751 9, 0.526 2, 0.825 7, and 0.856 0, respectively. Compared with the individual branches, the fixed-weight ensemble improved Sn, Acc, AUROC, and AUPR, indicating complementary information between the two network architectures. Cys-DHE also outperformed SVM, RF, XGBoost, and LightGBM across the evaluated metrics under the same cross-validation setting. On the independent test set, Cys-DHE achieved an Acc of 89.60%, Sn of 69.88%, Sp of 91.57%, MCC of 0.508 8, AUROC of 0.865 4, and AUPR of 0.485 2. Compared with DLBWE-Cys, Cys-DHE showed higher Sp, Acc, and MCC, although Sn, AUROC, and AUPR were slightly lower. Additional local Cys analysis showed that positive sites were more frequently accompanied by additional Cys residues in their local sequence neighborhoods. Among samples containing an additional Cys within the 41-aa window, the median distance from the central Cys to the nearest additional Cys was approximately 5 residues in positive samples and 10 residues in negative samples, with a significant difference (P=1.35×10??).Conclusion Cys-DHE effectively integrates position-aware sequence encoding with complementary heterogeneous deep-learning architectures and provides balanced performance for cysteine S-carboxyethylation site prediction. The model is intended as a sequence-based candidate prioritization tool for narrowing potential modification sites before subsequent structural analysis and experimental validation. Sequence proximity should not be interpreted as direct evidence of three-dimensional spatial proximity or disulfide-bond formation. Future work will incorporate structural descriptors, solvent accessibility, spatial neighborhood information, and reliable disulfide-bond annotations, while expanding experimentally validated datasets to further improve model robustness, interpretability, and generalization across proteins. An online prediction server is available athttp://www.lzzzlab.top/Cys-DHE/.

Abstract:Systemic chronic inflammation associated with advancing age is closely linked to the onset and progression of multiple age-related diseases. This persistent inflammatory state, commonly termed inflammaging, reflects complex interactions between accumulated cellular damage and dysregulated immune responses. Inflammasomes are multiprotein complexes of the innate immune system that respond to cellular stress and damage and amplify inflammatory signaling. Their sustained or recurrent activation may contribute to the establishment and maintenance of inflammaging. This review focuses on the NLRP3 inflammasome and summarizes its activation mechanisms, regulatory pathways, and pathological significance within the aging microenvironment. Particular attention is given to neurodegenerative diseases, osteoarthritis, atherosclerosis, and type 2 diabetes, emphasizing shared activation mechanisms and tissue-specific effects. Although initiating stimuli differ among these conditions, several molecular events can contribute to NLRP3 inflammasome activation. Priming mediated by nuclear factor-κB (NF-κB) promotes the expression of NLRP3 and pro-interleukin-1β, whereas lysosomal damage, mitochondrial dysfunction, reactive oxygen species accumulation, mitochondrial DNA release, and potassium efflux can participate in activation pathways. These events are interconnected rather than obligatory sequential steps in every setting. Inflammasome assembly promotes caspase-1 activation, which facilitates interleukin-1β and interleukin-18 maturation and gasdermin D cleavage, potentially leading to pyroptosis. The relative contributions of these processes depend on cell type, tissue context, and disease stage. Within aging tissues, persistent damage signals and impaired cellular homeostasis may favor repeated inflammasome activation, establishing a cycle of inflammatory amplification and further tissue injury. However, the pathological consequences vary across organs. In neurodegenerative diseases, inflammasome signaling is associated with neuroinflammation and neuronal vulnerability; in osteoarthritis, it may contribute to joint inflammation and cartilage degeneration. In atherosclerosis, it is implicated in vascular inflammation and plaque progression, whereas in type 2 diabetes, it is linked to metabolic inflammation and impaired glucose homeostasis. These distinctions underscore the importance of interpreting shared molecular mechanisms within specific tissue environments. The review further discusses the therapeutic potential of targeting inflammasomes to mitigate inflammaging and associated disease processes. Strategies include inhibiting NLRP3 activation, modulating upstream stress and priming pathways, and limiting downstream inflammatory signaling or pyroptotic cell death. By interrupting shared mechanisms of inflammatory amplification, these approaches may offer opportunities for coordinated intervention across multiple age-related disorders. Nevertheless, mechanistic overlap does not imply uniform therapeutic responses, and benefits observed in experimental models should not be equated with established clinical efficacy or evidence of delayed human aging.Successful clinical translation will require careful consideration of disease stage, tissue specificity, treatment timing, and the consequences of prolonged interference with innate immune defenses. Long-term safety is particularly important for older populations and preventive applications. The limited availability of validated biomarkers of inflammasome activity also complicates patient stratification, assessment of target engagement, and monitoring of therapeutic responses. Future research should clarify causal relationships, identify contexts in which inflammasome inhibition is most beneficial, and establish reliable translational endpoints. Overall, inflammasomes represent an important link between aging-associated damage and chronic inflammatory amplification, supporting their investigation as shared targets for early intervention in multiple age-related diseases, while highlighting the need for disease-specific evaluation and rigorous assessment of benefits and risks.

Abstract:Bloodstream infection (BSI) is a severe systemic infectious disease with rapid disease progression and high in-hospital mortality. The widespread and irrational use of antibiotics in recent decades has continuously driven the emergence of drug-resistant strains, resulting in a year-by-year increase in the detection rate of multidrug-resistant pathogens in BSI patients. In this context, early and accurate diagnosis is particularly critical for guiding rational antimicrobial therapy, preventing disease deterioration and optimizing patient long-term prognosis. As the recognized gold standard for clinical BSI diagnosis, conventional blood culture remains the most reliable testing method, yet it has prominent inherent technical limitations, including long incubation cycles and slow turnaround times. Generally, the positive detection time of routine blood culture exceeds 12 h after venous blood collection. Each hour of delayed antimicrobial intervention significantly increases the risk of adverse clinical outcomes and patient mortality. Moreover, the lag of laboratory test results easily leads to blind empirical antibiotic abuse, which further promotes the spread of drug-resistant bacteria and substantially increases the difficulty of nosocomial infection prevention and control in clinical institutions. The primary report of blood culture, covering Gram staining results and positive alert time, can effectively distinguish Gram-positive cocci, Gram-negative bacilli and yeast-like fungi, enabling timely and targeted early empirical anti-infection treatment before the acquisition of definitive species identification and antimicrobial susceptibility data. This review systematically summarizes effective strategies to accelerate blood culture detection efficiency from two core dimensions: clinical process optimization and blood culture bottle product improvement. At the clinical process level, standardized blood collection procedures, strict specimen delivery within 2 h, satellite blood culture laboratory mode and hierarchical reporting system can significantly shorten specimen transportation and pre-processing time, thereby effectively advancing the release of primary detection reports. These optimized clinical strategies require almost no additional equipment investment and are highly feasible and promotable for grassroots and community hospitals with limited laboratory resources. In terms of product modification, the optimization of medium formula, the addition of high-efficiency antibiotic adsorption resins, and the improvement of CO₂ sensing membranes and gas-isolated bottle structures can effectively promote the resuscitation and proliferation of dormant or inhibited pathogenic microorganisms. Such consumable improvements can shorten the turnaround time of primary reports by 1–4 h and are fully compatible with mainstream automatic blood culture detection platforms widely used in current clinical practice. This review also comprehensively sorts out a variety of emerging BSI detection technologies, including FISH (fluorescence in situ hybridization), PCR (polymerase chain reaction), MALDI-TOF-MS(matrix-assisted laser desorption/ionization time-of-flight mass spectrometry), direct whole-blood pathogen detection, next-generation sequencing, T2MR(T2 magnetic resonance), biomarker detection and machine learning-assisted identification. The study deeply analyzes the unique technical advantages and practical clinical limitations of each detection technology, and clarifies that the large-scale clinical promotion and popularization of these novel detection methods are restricted by high operating costs, incomplete pathogen coverage, nucleic acid contamination risk, blood matrix interference and the lack of stable routine phenotypic drug susceptibility testing capacity. Although emerging detection techniques possess prominent rapid-detection advantages, traditional blood culture is still clinically irreplaceable because it isolates viable pathogens, which can then be subjected to Gram staining and phenotypic antimicrobial susceptibility testing to provide accurate results for precise antibiotic selection. Overall, blood cultures remain irreplaceable. So speeding up blood cultures is still an important way to address the pain points in BSI detection.

Abstract:Skin tissue regeneration requires coordinated responses from immune cells, keratinocytes, fibroblasts, vascular endothelial cells, and other cell populations. Wound closure restores tissue continuity, but it does not necessarily indicate recovery of the epidermal barrier, dermal architecture, skin appendages, or tissue function. Chronic wounds and pathological scars are distinct manifestations of dysregulated repair: the former are commonly associated with persistent inflammation and impaired tissue formation, whereas the latter involve excessive extracellular matrix deposition and fibrosis. Their distinct pathological settings need to be considered when interpreting mechanisms and evaluating interventions. Network pharmacology integrates information on drug constituents, candidate targets, signaling pathways, and disease phenotypes to generate testable hypotheses about multicomponent and multitarget drug actions. It can narrow the range of candidates for subsequent studies, but associations derived from databases, network topology, functional enrichment, or molecular docking do not establish direct target engagement or a causal contribution to tissue regeneration. This review summarizes the biological basis and representative regulatory networks of skin wound repair and tissue regeneration, outlines the main steps and limitations of network pharmacology, and examines how it can be integrated with other techniques. Transcriptomics, proteomics, phosphoproteomics, and metabolomics can compare predicted networks with molecular responses measured in treated samples. Single-cell sequencing and spatial omics can further locate candidate targets in specific cell populations and wound regions, while sampling across repair stages helps assess their temporal context. Machine-learning methods can rank candidate targets for experimental follow-up, provided that model performance is evaluated using appropriate validation procedures. Biochemical and biophysical approaches, including direct binding assays and measurements of protein activity, can test whether a predicted drug-target relationship is supported at the molecular level. Target inhibition or genetic perturbation, together with rescue experiments where feasible, can then assess whether the target contributes to an observed drug response. Cell-based assays and animal studies can relate these molecular findings to processes such as re-epithelialization, angiogenesis, extracellular matrix remodeling, and restoration of skin structure. Together, these approaches can prioritize candidate targets and relate predicted drug actions to molecular, cellular, and tissue-level observations. Agreement between network predictions and experimental observations can support a proposed mechanism, but it does not, by itself, establish a causal role for the target. Database coverage and target-prediction algorithms can bias candidate selection; commonly used oral bioavailability and drug-likeness thresholds may be poorly suited to topical treatment; and the composition and tissue exposure of multicomponent preparations are not always known. In addition, target effects may vary among wound types, cell populations, anatomical regions, and repair stages. Two-dimensional cell cultures and animal models cannot fully reproduce human chronic wounds, and promising experimental results do not by themselves demonstrate clinical benefit. Together, these limitations may affect the reliability and applicability of network predictions. Future work should standardize data sources and reporting, account for pathological and spatiotemporal context, and use direct target-engagement and causal-intervention experiments to test key hypotheses. Drug dose, route of administration, local exposure, formulation stability, and safety should be evaluated alongside healing outcomes. Beyond wound area and inflammatory changes, endpoints should include barrier recovery, tissue architecture, relevant skin appendages, and functional properties when the injury model permits. A connected evidence pathway from network prediction through molecular and biological validation to clinically relevant evaluation may provide a more reliable basis for studying drugs intended to improve skin wound repair and tissue regeneration.