ObjectiveTo explore the expression levels of long non-coding RNA （lncRNA） H19 and microRNA-22-3p （miR-22-3p） in the serum of patients with acute myocardial infarction （AMI） and their diagnostic value for AMI. MethodsA total of 176 AMI patients were selected as the experimental group and were divided into ST-segment elevation myocardial infarction （STEMI） group （n=95） and non-ST-segment elevation myocardial infarction （NSTEMI） group （n=81） based on their medical history and electrocardiogram. Meanwhile， 156 patients with negative angiography during the same period were selected as the control group （CON group）. The relative expression levels of lncRNA H19 and miR-22-3p in the serum of the two groups were detected by real-time fluorescence quantitative polymerase chain reaction. The diagnostic value of miR-22-3p and lncRNA H19 in AMI was evaluated by receiver operating characteristic curve （ROC） analysis. The levels of serum cardiac troponin I （cTnI）， creatine kinase （CK）， creatine kinase MB isoenzyme （CK-MB）， and high-sensitivity C-reactive protein （CRP） were detected in all study subjects. Spearman correlation analyses were used to evaluate the correlations between lncRNA H19 and miR-22-3p and cTnI， CK， CK-MB and CRP. ResultsCompared with the control group， the expression level of lncRNA H19 was upregulated and the expression level of miR-22-3p was downregulated in the AMI group （all P<0.01）； compared with the NSTEMI group， the expression level of lncRNA H19 was upregulated and the expression level of miR-22-3p was downregulated in the STEMI group （P<0.01）. The areas under the ROC curves （AUC） for the diagnosis of AMI by miR-22-3p， lncRNA H19 and their combination were 0.555， 0.977， and 0.983， respectively. lncRNA H19 was positively correlated with cTnI， CK， CK-MB and CRP （P<0.05）， and negatively correlated with left ventricular ejection fraction （LVEF） （P<0.001）； miR-22-3p was negatively correlated with cTnI， CK and CRP， and positively correlated with LVEF （P<0.05）. ConclusionThe expression level of lncRNA H19 is elevated and the expression level of miR-22-3p decreased in AMI patients， and their levels may have potential value as an auxiliary biomarker for AMI diagnosis and cardiac function assessment.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Background/Aims: Lenvatinib resistance remains a critical barrier in advanced hepatocellular carcinoma (HCC) therapy. However, the underlying mechanisms and strategies for reversing resistance remain incompletely understood. Methods: Integrated transcriptomics of lenvatinib-resistant patient tumors and an acquired-resistance murine model identified a novel macrophage subpopulation. Functional validation employed CRISPR-SAM screening, conditioned medium (CM) assays, subcutaneous/orthotopic xenografts, patient-derived organoids (PDOs), and patient-derived xenografts (PDXs). Mechanistic studies included ChIP-qPCR, co-immunoprecipitation, and pharmacologic targeting. Clinical relevance was assessed in a retrospective cohort. Results: Resistant HCC exhibited significant enrichment of a COLEC12high TAM subset , which correlated with poor survival and treatment response. These TAMs secreted neuregulin-1 (NRG1) , activating HER2/HER3-AKT signaling in tumor cells to drive cancer stemness and lenvatinib resistance. Mechanistically, in TAMs COLEC12 sequestered STAT1 in the cytoplasm, preventing its phosphorylation, and thereby derepressing STAT3-mediated NRG1 transcription. Depletion of NRG1 reversed the stemness phenotypes and resensitized tumors to lenvatinib both in vitro and in vivo. Clinically, high NRG1 expression predicted an inferior lenvatinib response and shorter survival. Crucially, the bispecific anti-HER2/HER3 antibody zenocutuzumab restored lenvatinib efficacy in PDOs, PDXs, and murine models. Conclusions Our work establishes the COLEC12high TAM/NRG1 axis as a master regulator of therapeutic resistance and identifies NRG1 as a predictive biomarker, providing a clinically actionable strategy to overcome lenvatinib resistance in HCC.

ObjectiveTo identify and validate the optimal compatibility dosage of astragaloside Ⅰ （ASⅠ） and calycosin （CY） in the treatment of cholestatic liver fibrosis. MethodsA 3，5-diethoxycarbonyl-1，4-dihydrocollidine （DDC） diet was used to establish a mouse model of liver fibrosis， and the uniform design method was used to identify the optimal combination ratio of the saponin component ASⅠ and the flavonoid component CY in Astragalus membranaceus. In the uniform design experiment， 80 male C57/BL6J mice were divided into normal group， model group， total astragalosides （TAS） group， groups A — F with a uniform design， and obeticholic acid （OCA） group using a random number table， with 8 mice in each group. The multiple regression analysis was used to establish the optimal regression equation and obtain the potential optimal combination ratio. The in vivo efficacy of the empirically optimal combination identified in the uniform design and the optimal dose combination predicted by the regression equation were compared for validation. A one-way analysis of variance was used for comparison of continuous data between multiple groups； the Levene test was used to determine the homogeneity of variance， and the least significant difference t-test was used for comparison of data with homogeneity of variance between two groups， while the Dunnett T3 test was used for comparison of data with heterogeneity of variance. ResultsIn the uniform design regimen， the JYB combination （3.125 mg/kg ASI+50 mg/kg CY） significantly reduced the serum levels of alanine aminotransferase （ALT）， aspartate transaminase （AST）， alkaline phosphatase （ALP）， total bile acid （TBA）， total bilirubin （TBil）， and indirect bilirubin （IBil） in mice with DDC-induced cholestatic liver fibrosis （all P<0.05）， and it also reduced hepatic Hyp content （P<0.01）， semi-quantified collagen deposition area （P<0.001）， and the mRNA expression levels of Acta2， Col1a1， Ck7， Ck19， Adgre1， TLR4， TNF-α， and CCL5 in liver tissue （all P<0.05）. The regression equation showed that 50 mg/kg ASⅠ+50 mg/kg CY was the potential optimal combination， which was named as P1 combination. However， subsequent validation experiments showed that P1 combination only significantly improved the serum levels of AST and IBil and hepatic Hyp content in DDC mice （all P<0.05）， with no significant impact on hepatic collagen deposition and the mRNA expression levels of Acta2， Ck7， Ck19， Adgre1， and CCL5 （all P>0.05）. In contrast， the JYB combination significantly improved the serum levels of ALP， ALT， AST， TBA， TBil， and IBil， hepatic collagen deposition， hepatic Hyp content， and the mRNA expression levels of Acta2， Col1a1， Ck7， Ck19， Adgre1， TLR4， TNF-α， and CCL5 （all P<0.05）. ConclusionThis study shows that the JYB combination （3.125 mg/kg ASⅠ+50 mg/kg CY） can significantly alleviate DDC-induced liver fibrosis， with comparable efficacy to total saponins from Astragalus membranaceus， and compared with ASⅠ or CY administered alone， the JYB combination has a significantly better regulatory effect on the serum levels of ALP and TBA.

OBJECTIVE To explore the efficacy and safety of toludesvenlafaxine in the treatment of patients with major depressive disorder （MDD） with anhedonia. METHODS This retrospective study included 160 patients with MDD with anhedonia who were treated at Hebei Mental Health Center from May 2023 to August 2025. These patients were divided into a control group （ n ＝81） and an observation group （ n ＝79） according to treatment regimens. All patients received transcranial magnetic stimulation therapy. On this basis，the control group was treated with Sertraline Hydrochloride Tablets， while the observation group received Toludesvenlafaxine Hydrochloride Sustained-Release Tablets，with an 8-week continuous treatment course.To compare t he clinical total effective rate， anhedonia remission rate，the severity of depressive symptoms，degree of anhedonia，cognitive function，social function and quality of life between the two groups before and after treatment， the incidence of adverse reactions during treatment was recorded simultaneously. RESULTS After treatment，the total effective rate and anhedonia remission rate of the observation group were significantly higher than the control group （ P ＜0.05）. At the 4th and 8th weeks of treatment， the scores of Snaith-Hamilton Pleasure Scale， Hamilton Depression Rating Scale-17 Items， and Sheehan Disability Scale of both groups were significantly lower than those before treatment in the same group， and the observation group was significantly lower than the control group （ P ＜0.05）； the scores of Montreal Cognitive Assessment and Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form were significantly higher than those before treatment in the same group， and the observation group was significantly higher than the control group （ P ＜0.05）. There was no statistically significant difference in the overall incidence of adverse reactions between the two groups （ P ＞0.05）. CONCLUSIONS Toludesvenlafaxine can significantly improve depressive symptoms， cognitive function， and social function in MDD patients with anhedonia，with good safety.

ObjectiveTo clarify whether METTL14 mediates the core role of acupuncture at Neiguan (PC6) in promoting myelination and improving behavior in young autistic rats through gene intervention technology. MethodsThe ASD model was established by intraperitoneal injection of valproic acid (VPA) in pregnant rats. Male offspring were intracerebroventricularly injected with adenovirus-packaged METTL14 shRNA (sh-METTL14) or its control (sh-NC) on postnatal day 1, with a model group set as well. Subsequently, the juvenile rats were divided into model group, acupuncture group, acupuncture+sh-NC group, and acupuncture+sh-METTL14 group. The acupuncture group received acupuncture at Neiguan (PC6) from postnatal day 7, once daily for 21 consecutive days. Neurobehavioral changes were evaluated by behavioral tests; METTL14 knockdown efficiency and the expression of METTL14, METTL3, and PTEN were detected by quantitative real-time PCR (qRT-PCR) and Western blot (WB); PTEN m⁶A levels were measured by RNA immunoprecipitation-qPCR (RIP-qPCR); myelin ultrastructure, expression of myelin basic protein (MBP) and neurofascin 155 (NF155), and dendritic spine density were observed using transmission electron microscopy (TEM), enzyme-linked immunosorbent assay (ELISA), immunofluorescence, qRT-PCR, and primary neuron culture. ResultsBehaviorally, knockdown of METTL14 significantly counteracted the beneficial effects of acupuncture in improving self-grooming, open field exploration, three-chamber social interaction, and Morris water maze learning and memory (P<0.05, P<0.01). Compared with the acupuncture+sh-NC group, the acupuncture+sh-METTL14 group showed significantly decreased mRNA and protein expression of hippocampal METTL14 (P<0.01), and the upregulating effects of acupuncture on METTL3 and PTEN expression were reversed (P<0.01). Meanwhile, knockdown of METTL14 significantly inhibited the acupuncture-induced increase in PTEN m⁶A levels (P<0.01). Morphologically, knockdown of METTL14 attenuated the improvement of myelin structure by acupuncture, reversed the downregulation of MBP and upregulation of NF155 induced by acupuncture, and blocked the increase in dendritic spine density (P<0.05, P<0.01). ConclusionMETTL14 is a key molecule mediating the therapeutic effect of acupuncture at Neiguan. Acupuncture at Neiguan upregulates METTL14, thereby enhancing m⁶A methylation modification of PTEN mRNA to stabilize its expression, ultimately promoting myelin development and improving behavioral symptoms in ASD juvenile rats. This preliminarily reveals the modern biological connotation of “opening Xuanfu and dredging myelin”.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

BACKGROUND:Peripheral nerve axon rupture seriously affects patients' physical function and mental health.Microsurgery,nerve autograft,nerve allograft,fibrin glue and catheter technology are the main treatments for peripheral nerve injury,each of which has its own advantages and disadvantages,but the overall treatment effect is not satisfactory.Despite the clinical success of Schwann cells in promoting axonal regeneration,there are still many challenges in the treatment with Schwann cells,such as slow expansion of Schwann cells,immune rejection,and low survival rate of transplanted cells.OBJECTIVE:To summarize the role and mechanism of Schwann cells in promoting the regeneration of peripheral nerve axons,and the difficulties and challenges of Schwann cells in the process of nerve regeneration treatment.METHODS:PubMed,Medline,WanFang,VIP,and CNKI were searched by computer using the search terms of"Schwann cells,synaptic Schwann cell,macrophage,peripheral nerve axon rupture,Wallerian degeneration,Peripheral nerve axon regeneration,Central nervous system repair"in English and Chinese.Literature related to Schwann cell proliferation and differentiation,promotion of peripheral nerve regeneration,and clinical applications was retrieved from database inception to October 2024,and a total of 95 articles were finally included for review.RESULTS AND CONCLUSION:Schwann cells interact with macrophages,T cells and other cells,to initiate the regeneration process through signaling pathways,including Krox20/C-Jun,NRG-1/ErbB,Notch,MAPK,and PI3K/Akt/mTOR,synthesize and release nerve growth factors,and thus promote regeneration of the peripheral nervous system.Schwann cells have been experimentally demonstrated to have great potential in peripheral nerve repair and are expected to become the key target of therapeutic intervention.However,there are still problems such as difficulties in cell harvest and culture,as well as the occurrence of other diseases during the treatment process.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.