Objective: To explore the value of contrast-enhanced computed tomography (CT) radiomics combined with machine learning algorithms in the preoperative prediction of perineural invasion (PNI) in oral squamous cell carcinoma (OSCC), aiming to provide evidence for assisting clinical treatment decision-making. Methods​: This study was approved by the Ethics Committee of the Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. A total of 250 OSCC patients confirmed by postoperative pathology were included, comprising 128 PNI-positive and 122 PNI-negative cases. The dataset was randomly divided into training (n=175), validation (n=38), and independent testing (n=37) sets in a ratio of 7:1.5:1.5. Regions of interest were delineated on preoperative images, and radiomic features were extracted. After dimensionality reduction and feature selection using methods like Least Absolute Shrinkage and Selection Operator (LASSO) regression, multiple machine learning models, including support vector machine (SVM), random forest, Light gradient boosting machine (LightGBM), and a Stacking ensemble model, were constructed. Model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, calibration curves, and decision curve analysis (DCA). Model interpretability was analyzed using Shapley additive explanations (SHAP) and grouped permutation feature importance analysis. Results ​: ​ Among the 250 samples analyzed, the LightGBM model based on radiomics demonstrated the best performance on the independent test set, with an AUC of 0.781, outperforming models like SVM (AUC = 0.730) and Random Forest (AUC = 0.691), as well as clinical models (AUCs ranging 0.549-0.711). The LightGBM model showed good calibration (Brier score 0.198), and DCA indicated high clinical net benefit over a wide threshold probability range. Paired DeLong tests revealed no statistically significant differences in AUC between the ensemble (Stacking) model and the corresponding best-performing radiomics-based model. SHAP analysis and grouped permutation feature importance analysis further indicated that the primary discriminative information for the model came from radiomic texture features. Conclusion ​ The LightGBM model based on contrast-enhanced CT radiomics demonstrated good discriminative ability for preoperative prediction of PNI in OSCC. In the independent test set, it achieved the highest AUC. This model holds promise as a non-invasive auxiliary tool for preoperative risk assessment. Given the limited sample size of the independent test set, these results require further validation in larger cohorts and external datasets.

ObjectiveTo explore the mechanism of Yishen Qubi Tongluo Formula （益肾祛痹通络方， YQTF） in the treatment of rheumatoid arthritis（RA）. MethodsNetwork pharmacology was employed to retrieve and screen the active components and potential targets of YQTF as well as RA-related targets using databases including TCMSP， BATMAN， ETCM and GEO. The intersection of targets related to active components and RA-related targets was identified， and a protein-protein interaction （PPI） network was constructed. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed， and a drug-active component-common target network of YQTF in the treatment of RA was established. The core components of YQTF were molecularly docked with key targets. Human rheumatoid arthritis synovial fibroblast cell line MH7A was divided into blank group， model group， methotrexate group and YQTF group. The blank group was cultured with 10% fetal bovine serum， while the other three groups were stimulated with 10 μg/L of recombinant human tumor necrosis factor-α （TNF-α） for 24 h to establish the RA cell model. On this basis， the methotrexate group was treated with methotrexate suspension at a concentration of 20 μmol/L， and the YQTF group was treated with 10% YQTF-medicated serum. After 48 h of intervention， the levels of TNF-α and interleukin-17A（IL-17A）contents in cell supernatants were detected by enzyme-linked immunosorbent assay （ELISA）， and mRNA expressions of phosphatidylinositol 3-kinase（PI3K）， protein kinase B（AKT） and mammalian target of rapamycin（mTOR） were detected by real-time quantitative polymerase chain reaction （RT-qPCR）. ResultsNetwork pharmacological analysis identified 209 active components and 583 potential target genes of YQTF， as well as 818 RA-related targets. A total of 29 common targets were obtained from the intersection of drug-related targets and RA-related targets. Quercetin，β-sitosterol， kaempferol， stigmasterol and luteolin were the core active components of YQTF for the treatment of RA， while matrix metalloproteinase-9 （MMP9）， prostaglandin-endoperoxide synthase 2 （PTGS2）， Toll-like receptor 4 （TLR4）， tumor protein p53 （TP53） and transcription factor AP-1 subunit JUN were the key targets. The GO and KEGG pathway enrichment analysis showed that the involved biological processes and pathways were mainly associated with antioxidant responses， PI3K-AKT signaling pathway and Toll-like receptor （TLR） signaling pathway. Molecular docking results showed that MMP9 and PTGS2 exhibited high binding affinities with quercetin， β-sitosterol， kaempferol， stigmasterol and luteolin； TLR4 exhibited high binding activities with β-sitosterol， stigmasterol and luteolin； and TP53 showed high binding affinity with luteolin. The results of cell experiments showed that compared with the control group， the contents of TNF-α and IL-17A as well as the mRNA expressions of AKT and mTOR in the model group significantly increased （P＜0.05 or P＜0.01）. Compared with the model group， all the above indicators significantly decreased in the YQTF group， while the contents of TNF-α and the mRNA expression of AKT significantly decreased in the methotrexate group （P＜0.05 or P＜0.01）. ConclusionThe mechanism of YQTF in the treatment of RA may be associated with reducing inflammatory cytokine secretion and inhibiting the activation of the PI3K-AKT-mTOR signaling pathway.

BackgroundEmotional disorders in adolescents have emerged as a prominent issue in recent years. Current mainstream clinical assessment approaches for such conditions predominantly rely on interviews and rating scales， which are limited by inherent drawbacks such as high subjectivity and recall bias. Accordingly， there exists an urgent clinical need for the development of objective， quantifiable auxiliary diagnostic tools.In previous studies， frontal electroencephalography （EEG） has demonstrated significant value in assessing depressive and anxiety. However， the lack of standardized quantitative metrics and intuitive visual analytical approaches has severely restricted clinical interpretability of EEG data and diminished patient engagement. To address these key limitations， the present study proposes an innovative analytical framework that converts frontal EEG signals into quantifiable visual metrics to enhance clinical comprehension and acceptance. ObjectiveTo explore the value of frontal EEG in assessing anxiety， depression， and sleep quality in adolescents with emotional disorders， with the aim of providing objective auxiliary tools for clinical diagnosis and assessment of adolescents with emotional disorders. MethodsThis cross-sectional study recruited 105 adolescents aged 12-18 years who visited the outpatient department of a specialized mental hospital in Beijing from April 2023 to April 2024. All participants met the diagnostic criteria for mood （affective） disorders or anxiety disorders in the International Classification of Diseases， tenth edition （ICD-10）. Frontal EEG signals were collected within a big data analytics-driven framework and further processed by EEG system to generate six quantitative cerebral function indices， namely brain load， tension and excitement， emotional stress， sleepiness index， cerebral vitality， and cerebral fatigue. In addition， validated standardized scales， including the Self-rating Anxiety Scale （SAS）， the Self-rating Depression Scale （SDS）， and the Pittsburgh Sleep Quality Index （PSQI）， were administered for anxiety， depressive symptoms， and sleep quality， respectively. ResultsIn adolescent patients with emotional disorders， the SAS score exhibited significant positive correlations with brain load （r_s=0.328， P<0.01）， emotional stress （r_s=0.341， P<0.01）， and cerebral fatigue （r_s=0.286， P<0.01）. The SDS score was positively correlated with brain load （r_s=0.275， P<0.01）， emotional stress （r_s=0.241， P<0.05）， and cerebral fatigue （r_s=0.311， P<0.01）， while showing a significant negative correlation with cerebral vitality （r_s=-0.212， P<0.05）. Additionally， the PSQI total score demonstrated positive correlations with brain load （r_s=0.340， P<0.01）， emotional stress （r_s=0.322， P<0.01）， and cerebral fatigue （r_s=0.229， P<0.05）. ConclusionFrontal EEG-derived indices， including brain load， emotional stress， cerebral fatigue and cerebral vitality， may serve as objective markers for reflecting anxiety， depression， and sleep quality in adolescents with emotional disorders. ［Funded by Beijing High level Innovation and Entrepreneurship Talent Support Program （number， 202504841041）； Horizontal Joint Project］

Objective To analyze the genetic characteristics and variations of influenza A (H3N2) viruses in Ma'anshan from 2022 to 2024, and to provide a scientific basis for local influenza prevention and control. Methods From April 2022 to March 2024, influenza-like illness (ILI) specimens were collected from three national influenza surveillance sentinel hospitals in Ma’anshan. Samples positive for influenza by real-time PCR were subjected to virus culture and identification. A total of 40 representative A/H3N2 strains with hemagglutination titers ≥8 were selected for whole-genome sequencing. Genetic evolution, homology, amino acid variations, and glycosylation sites were analyzed. Results All H3N2 representative strains from the 2022–2023 influenza season belonged to clade 3C.2a1b.2a.1a.1, while those from the 2023–2024 season fell into clade 3C.2a1b.2a.2a.3a.1. The nucleotide and amino acid sequence similarities of HA and NA between the 40 representative strains and the vaccine strain A/Darwin/6/2021 were all above 97.35%. Compared with the vaccine strain, amino acid mutations were identified in antigenic sites A, B, C, and E, as well as in receptor-binding sites of the HA protein. An I222V substitution was detected in the NA protein. The HA protein contained four additional glycosylation sites compared to the vaccine strain, while the glycosylation pattern of the NA protein remained consistent. Conclusion No antigenic drift was observed in the influenza A/H3N2 viruses in Ma'anshan City from 2022 to 2024, but genetic changes such as branching variations, key amino acid substitutions, and an increase in HA glycosylation sites were observed. These findings underscore the importance of sustained molecular surveillance of local influenza viruses.

Objective To analyze the varicella vaccination rate in the 2012-2023 birth cohort in Changsha. Methods The varicella vaccination information of all children born in Changsha from 2012 to 2023 was collected through the Hunan Provincial Immunization Planning Information System. The varicella epidemic information from 2012 to 2023 was collected through the China Information System for Disease Control and Prevention. The varicella vaccination rate and its characteristics of children in the birth cohort were descriptively analyzed. Results The first dose vaccination rate of varicella vaccine for children born from 2012 to 2023 was 64.82 %, and the full vaccination rate was 40.87 %. The full vaccination rate of the birth cohort in each year ranged from 18.11 % (2020 birth cohort) -73.07 % (2017 birth cohort), showing an overall increasing trend year by year. Conclusion The varicella vaccination rate has increased among children born between 2012 and 2023 in Changsha, but overall coverage is low. It is necessary to strengthen the publicity of varicella vaccine knowledge and promote timely vaccination of school-age children.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

Spermatogenesis is a highly ordered and spatiotemporally regulated developmental process in the male reproductive system, during which spermatogonial stem cells (SSCs), supported by the seminiferous tubule microenvironment, sequentially undergo mitosis, meiosis, and spermiogenesis to ultimately generate structurally intact spermatozoa. This complex process is accompanied by extensive transcriptional reprogramming, chromatin remodeling, and finely tuned post-transcriptional regulation. Precise control of RNA fate is therefore essential for maintaining the continuity and fidelity of spermatogenesis, and its disruption represents a major molecular basis of male infertility. N⁶-methyladenosine (m⁶A), the most abundant internal RNA modification in eukaryotes, has emerged as a critical regulator of post-transcriptional gene expression. m⁶A methyltransferases (“writers”) catalyze the addition of a methyl group to the N⁶ position of adenosine, m⁶A demethylases (“erasers”) remove the modification, and m⁶A-binding proteins (“readers”) recognize m⁶A-modified transcripts. Through the coordinated actions of these factors, m⁶A regulates transcript fate at multiple levels, including RNA splicing, nuclear export, stability, translation, and decay. Emerging evidence indicates that m⁶A-mediated regulation is essential across multiple stages of spermatogenesis, including SSC self-renewal and differentiation, meiotic progression, maintenance of chromosomal stability, and sperm morphogenesis. Beyond its intrinsic functions in germ cells, m⁶A also contributes to the regulation of the testicular microenvironment. In sertoli cells, m⁶A is involved in maintaining blood-testis barrier integrity, RNA processing, and paracrine signaling, thereby providing structural and metabolic support for germ cell development. In Leydig cells, m⁶A regulates steroidogenesis, particularly testosterone synthesis, and participates in cellular stress responses and metabolic homeostasis. Through these mechanisms, m⁶A indirectly influences spermatogenesis by modulating the functional state of testicular somatic cells, highlighting an integrated regulatory mode that combines cell-intrinsic and microenvironment-mediated effects. Notably, distinct classes of m⁶A regulators exhibit pronounced stage-specific functions and coordinated division of labor, collectively forming a multilayered and dynamic regulatory network. Writers often display dosage- and temporal window-dependent effects; erasers contribute to stage-specific demethylation and functional compensation; while readers function through a “switch-buffer” dual-layer architecture, and RNA-binding proteins (RBPs) participate in substrate selection and post-transcriptional regulation. Importantly, emerging evidence suggests that some m⁶A-related proteins can function through noncanonical mechanisms independent of m⁶A recognition, such as intrinsic RNA-binding activity, helicase function, or ribonucleoprotein complex assembly, thereby expanding the functional landscape of the m⁶A regulatory system. Dysregulation of m⁶A machinery can lead to multiple spermatogenic defects, including impaired SSC self-renewal, meiotic arrest, abnormal chromatin remodeling, and defective sperm formation, ultimately resulting in male infertility. Despite substantial advances, several critical questions remain unresolved, including the distinction between m⁶A-dependent and -independent mechanisms, the spatiotemporal dynamics of m⁶A modifications at single-cell resolution, and the coordination and antagonism among different regulatory factors. In this review, we systematically summarize the dual regulation of spermatogenesis by germ cell-intrinsic mechanisms and the testicular microenvironment, and delineate the molecular mechanisms and stage-specific functions of the dynamic m⁶A regulatory network. We further discuss the current limitations in the field and propose feasible experimental strategies for future investigation. Collectively, this work aims to provide a comprehensive framework for understanding the epitranscriptomic regulation of spermatogenesis and to offer theoretical insights into the pathogenesis and clinical management of male infertility.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

Spermatogenesis is a highly ordered and spatiotemporally regulated developmental process in the male reproductive system, during which spermatogonial stem cells (SSCs), supported by the seminiferous tubule microenvironment, sequentially undergo mitosis, meiosis, and spermiogenesis to ultimately generate structurally intact spermatozoa. This complex process is accompanied by extensive transcriptional reprogramming, chromatin remodeling, and finely tuned post-transcriptional regulation. Precise control of RNA fate is therefore essential for maintaining the continuity and fidelity of spermatogenesis, and its disruption represents a major molecular basis of male infertility. N⁶-methyladenosine (m⁶A), the most abundant internal RNA modification in eukaryotes, has emerged as a critical regulator of post-transcriptional gene expression. m⁶A methyltransferases (“writers”) catalyze the addition of a methyl group to the N⁶ position of adenosine, m⁶A demethylases (“erasers”) remove the modification, and m⁶A-binding proteins (“readers”) recognize m⁶A-modified transcripts. Through the coordinated actions of these factors, m⁶A regulates transcript fate at multiple levels, including RNA splicing, nuclear export, stability, translation, and decay. Emerging evidence indicates that m⁶A-mediated regulation is essential across multiple stages of spermatogenesis, including SSC self-renewal and differentiation, meiotic progression, maintenance of chromosomal stability, and sperm morphogenesis. Beyond its intrinsic functions in germ cells, m⁶A also contributes to the regulation of the testicular microenvironment. In sertoli cells, m⁶A is involved in maintaining blood-testis barrier integrity, RNA processing, and paracrine signaling, thereby providing structural and metabolic support for germ cell development. In Leydig cells, m⁶A regulates steroidogenesis, particularly testosterone synthesis, and participates in cellular stress responses and metabolic homeostasis. Through these mechanisms, m⁶A indirectly influences spermatogenesis by modulating the functional state of testicular somatic cells, highlighting an integrated regulatory mode that combines cell-intrinsic and microenvironment-mediated effects. Notably, distinct classes of m⁶A regulators exhibit pronounced stage-specific functions and coordinated division of labor, collectively forming a multilayered and dynamic regulatory network. Writers often display dosage- and temporal window-dependent effects; erasers contribute to stage-specific demethylation and functional compensation; while readers function through a “switch-buffer” dual-layer architecture, and RNA-binding proteins (RBPs) participate in substrate selection and post-transcriptional regulation. Importantly, emerging evidence suggests that some m⁶A-related proteins can function through noncanonical mechanisms independent of m⁶A recognition, such as intrinsic RNA-binding activity, helicase function, or ribonucleoprotein complex assembly, thereby expanding the functional landscape of the m⁶A regulatory system. Dysregulation of m⁶A machinery can lead to multiple spermatogenic defects, including impaired SSC self-renewal, meiotic arrest, abnormal chromatin remodeling, and defective sperm formation, ultimately resulting in male infertility. Despite substantial advances, several critical questions remain unresolved, including the distinction between m⁶A-dependent and -independent mechanisms, the spatiotemporal dynamics of m⁶A modifications at single-cell resolution, and the coordination and antagonism among different regulatory factors. In this review, we systematically summarize the dual regulation of spermatogenesis by germ cell-intrinsic mechanisms and the testicular microenvironment, and delineate the molecular mechanisms and stage-specific functions of the dynamic m⁶A regulatory network. We further discuss the current limitations in the field and propose feasible experimental strategies for future investigation. Collectively, this work aims to provide a comprehensive framework for understanding the epitranscriptomic regulation of spermatogenesis and to offer theoretical insights into the pathogenesis and clinical management of male infertility.

ObjectiveTo analyze the risk factors for long-term cardiovascular events in patients undergoing long-term peritoneal dialysis （PD）， and to construct and validate a visual nomogram prediction model based on multiple parameters. MethodsA prospective cohort study was conducted， consecutively enrolling 248 maintenance PD patients （dialysis duration ≥ 3 months）. Demographic characteristics， clinical indicators， laboratory parameters， and echocardiographic indices （including left ventricular ejection fraction ［LVEF］， ratio of early diastolic mitral inflow velocity to early diastolic mitral annular velocity （E/e’）， etc.） were collected. The composite endpoint was defined as the occurrence of cardiovascular events or cardiovascular death， with non-cardiovascular death as the competing risk and loss to follow-up or the end of follow-up as censoring events. Fine-Gray competing risks model was used to screen independent predictors， based on which a nomogram model was constructed. Internal validation was performed using the Bootstrap method （1 000 resamplings）， and the concordance index （C-index） and time-dependent receiver operating characteristic （time-dependent ROC） curve were calculated to evaluate the model performance. ResultsWith a median follow-up of 29 months （interquartile range： 24–35 months）， 88 patients （35.48%） reached the composite endpoint， including 80 cases of cardiovascular events and 8 cases of cardiovascular death， and 4 patients died of non-cardiovascular causes. Multivariate Fine-Gray analysis revealed that age， diabetes mellitus， hemoglobin （HGB） level and E/e' ratio were independent influencing factors of the composite endpoint. Specifically， each 1-year increase in age was associated with a 3.0% increase in the risk of the composite endpoint （HR=1.030， P=0.006）； patients with diabetes mellitus had a 167.9% higher risk compared with non-diabetic patients （HR=2.679， P=0.007）； each 1g/L increase in HGB level contributed to a 1.5% reduction in the risk （HR=0.985， P=0.003）； and each 0.1 increase in E/e' ratio led to a 7.2% increase in the risk （HR=1.072， P=0.045）. The nomogram model had a C-index of 0.76 （95% CI： 0.698–0.820）， and the AUC of the time-dependent ROC curve reached 0.849 at 23 months of follow-up. ConclusionIncreased age， complicated with diabetes mellitus， decreased HGB， and elevated E/e' ratio are independent risk factors of long-term occurrence of cardiovascular events and cardiovascular death in patients undergoing long-term PD. The nomogram model constructed based on the above variables has good predictive value and clinical applicability， which can provide a reference for cardiovascular risk stratification and individualized intervention in long-term PD patients.