ObjectiveTo investigate the role of 4-week high-intensity interval training (HIIT) in modulating the metabolic homeostasis of the pyruvate-lactate axis in the hippocampus of rats with chronic unpredictable mild stress (CUMS) to improve their depressive-like behavior. MethodsForty-eight SPF-grade 8-week-old male SD rats were randomly divided into 4 groups: the normal quiet group (C), the CUMS quiet group (M), the normal exercise group (HC), and the CUMS exercise group (HM). The M and HM groups received 8 weeks of CUMS modeling, while the HC and HM groups were exposed to 4 weeks of HIIT starting from the 5th week (3 min (85%-90%) S_max+1 min (50%-55%) S_max, 3-5 cycles, S_max is the maximum movement speed). A lactate analyzer was used to detect the blood lactate concentration in the quiet state of rats in the HC and HM groups at week 4 and in the 0, 2, 4, 8, 12, and 24 h after exercise, as well as in the quiet state of rats in each group at week 8. Behavioral indexes such as sucrose preference rate, number of times of uprightness and number of traversing frames in the absenteeism experiment, and other behavioral indexes were used to assess the depressive-like behavior of the rats at week 4 and week 8. The rats were anesthetized on the next day after the behavioral test in week 8, and hippocampal tissues were taken for assay. LC-MS non-targeted metabolomics, target quantification, ELISA and Western blot were used to detect the changes in metabolite content, lactate and pyruvate concentration, the content of key metabolic enzymes in the pyruvate-lactate axis, and the protein expression levels of monocarboxylate transporters (MCTs). Results4-week HIIT intervention significantly increased the sucrose preference rate, the number of uprights and the number of traversed frames in the absent field experiment in CUMS rats; non-targeted metabolomics assay found that 21 metabolites were significantly changed in group M compared to group C, and 14 and 11 differential metabolites were significantly dialed back in the HC and HM groups, respectively, after the 4-week HIIT intervention; the quantitative results of the targeting showed that, compared to group C, lactate concentration in the hippocampal tissues of M group, compared with group C, lactate concentration in hippocampal tissue was significantly reduced and pyruvate concentration was significantly increased, and 4-week HIIT intervention significantly increased the concentration of lactate and pyruvate in hippocampal tissue of HM group; the trend of changes in blood lactate concentration was consistent with the change in lactate concentration in hippocampal tissue; compared with group C, the LDHB content of group M was significantly increased, the content of PKM2 and PDH, as well as the protein expression level of MCT2 and MCT4 were significantly reduced. The 4-week HIIT intervention upregulated the PKM2 and PDH content as well as the protein expression levels of MCT2 and MCT4 in the HM group. ConclusionThe 4-week HIIT intervention upregulated blood lactate concentration and PKM2 and PDH metabolizing enzymes in hippocampal tissues of CUMS rats, and upregulated the expression of MCT2 and MCT4 transport carrier proteins to promote central lactate uptake and utilization, which regulated metabolic homeostasis of the pyruvate-lactate axis and improved depressive-like behaviors.

ObjectiveThis study aimed to investigate the effects of 4-week high-intensity interval training (HIIT) on synaptic plasticity in the prefrontal cortex (PFC) of rats exposed to chronic unpredictable mild stress (CUMS), and to explore its potential mechanisms. MethodsA total of 48 male Sprague-Dawley rats were randomly divided into 4 groups: control (C), model (M), control plus HIIT (HC), and model plus HIIT (HM). Rats in groups M and HM underwent 8 weeks of CUMS to establish depression-like behaviors, while groups HC and HM received HIIT intervention beginning from the 5th week for 4 consecutive weeks. The HIIT protocol consisted of repeated intervals of 3 min at high speed (85%-90% maximal training speed, S_max) alternated with one minute at low speed (50%-55% S_max), with 3 to 5 sets per session, conducted 5 d per week. Behavioral assessments and tail-vein blood lactate levels were measured at the end of the 4th and 8th weeks. After the intervention, rat PFC tissues were collected for Golgi staining to analyze synaptic morphology. Enzyme-linked immunosorbent assays (ELISA) were employed to detect brain-derived neurotrophic factor (BDNF), monocarboxylate transporter 1 (MCT1), lactate, and glutamate levels in the PFC, as well as serotonin (5-HT) levels in serum. Additionally, Western blot analysis was conducted to quantify the expression of synaptic plasticity-related proteins, including c-Fos, activity-regulated cytoskeleton-associated protein (Arc), and N-methyl-D-aspartate receptor 1 (NMDAR1). ResultsCompared to the control group (C), the CUMS-exposed rats (group M) exhibited significant reductions in sucrose preference rates, number of grid crossings, frequency of upright postures, and entries into and duration spent in open arms of the elevated plus maze, indicating marked depressive-like behaviors. Additionally, the group M showed significantly reduced dendritic spine density in the PFC, along with elevated levels of c-Fos, Arc, NMDAR1 protein expression, and increased concentrations of lactate and glutamate. Conversely, BDNF and MCT1 contents in the PFC and 5-HT levels in serum were significantly decreased. Following HIIT intervention, rats in the group HM displayed considerable improvement in behavioral indicators compared with the group M, accompanied by significant elevations in PFC MCT1 and lactate concentrations. Furthermore, HIIT notably normalized the expression levels of c-Fos, Arc, NMDAR1, as well as glutamate and BDNF contents in the PFC. Synaptic spine density also exhibited significant recovery. ConclusionFour weeks of HIIT intervention may alleviate depressive-like behaviors in CUMS rats by increasing lactate levels and reducing glutamate concentration in the PFC, thereby downregulating the overexpression of NMDAR, attenuating excitotoxicity, and enhancing synaptic plasticity.

[摘 要] 目的：分析胃癌骨转移患者的临床病理特征及影响预后的因素，探讨不同治疗方案对同时与异时骨转移患者生存的影响。方法：纳入2015年至2023年间南京大学医学院附属鼓楼医院胃癌骨转移患者120例，其中同时骨转移36例，异时骨转移84例。采用χ2检验比较胃癌同时与异时骨转移患者临床病理特征，采用Cox比例风险回归模型分析影响胃癌骨转移患者骨转移后总生存期（OS-BM）的风险因素，使用Kaplan-Meier法分析不同治疗方式对同时与异时骨转移OS-BM的影响。结果：120例胃癌骨转移患者中，有104例（86.6%）合并全身其他器官转移灶。在同时与异时骨转移患者的病理特征比较中，同时骨转移患者血C-反应蛋白（CRP）升高、血浆白蛋白减少；而异时骨转移患者外周血白细胞以及中性粒细胞低于正常值（均P < 0.05）。异时骨转移[HR = 2.35, 95% CI（1.47, 3.74），P < 0.01]、血清CA125 ≥ 30.2U/mL [HR = 1.6，95% CI（1.03, 2.48），P = 0.036]、血白细胞 ≥ 9.5 × 109/L [HR = 2.15，95% CI（1.17, 3.92），P = 0.013]，以及未接受免疫治疗[HR = 2.26，95% CI（1.5, 3.39），P < 0.01]是影响患者预后的独立危险因素。免疫治疗的联合使用相较于未使用免疫治疗，可明显延长胃癌骨转移患者的OS-BM（9.63 vs 4.53个月，P = 0.002）。异时骨转移患者比同时骨转移患者对免疫治疗响应更佳（中位OS-BM：10.8 vs 7.3个月，P = 0.004）。结论：免疫治疗是胃癌骨转移患者生存的独立保护因素，建议此类患者在化疗基础上尽早采用以免疫治疗为主的联合治疗，以延长患者的生存期。

Objective To analyze the differences in distribution of traditional Chinese medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without, and to explore the potential correlation between the distribution of TCM syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of TCM syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between TCM syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary TCM syndrome elements related to disease location were the lung and liver, and the primary TCM syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary TCM syndrome elements related to disease location were the lung and spleen, and the primary TCM syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of TCM syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there was significant difference in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, the results showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of TCM syndrome elements and the species abundance and composition of salivary microbiota between the patients with pulmonary nodules and the healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.

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

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

ObjectiveTo investigate the differences in the risk factors for postoperative pancreatic fistula （POPF） after pancreaticoduodenectomy （PD） between the 2005 and 2016 editions of the definition and classification standards for pancreatic fistula， and to establish a risk prediction model for pancreatic fistula based on the 2016 edition. MethodsA retrospective analysis was performed for the clinical data of 303 patients who were admitted to Tianjin Third Central Hospital and underwent PD from January 2016 to May 2022， and the patients with POPF were identified based on the new and old editions. The independent-samples t test or the non-parametric Mann-Whitney U test was used for comparison of continuous data between groups， and the chi-square test was used for comparison of categorical data between groups. The univariate and multivariate logistic regression analyses were used to investigate the differences in the risk factors for pancreatic fistula after PD between the two editions； a risk prediction model was established for POPF based on the 2016 edition， and the receiver operating characteristic curve was used to invesitgate the accuracy of this model in predicting POPF and perform model validation. ResultsAccording to the 2005 edition， the univariate analysis showed that the diameter of the main pancreatic duct （χ²=31.641， P<0.001）， main pancreatic duct index （χ²=52.777， P<0.001）， portal vein invasion （χ²=6.259， P=0.012）， intra-abdominal fat thickness （χ²=7.665， P=0.006）， preoperative biliary drainage （χ²=5.999， P=0.014）， pancreatic cancer （χ²=5.544， P=0.019）， marginal pancreatic thickness （t=2.055， P=0.032）， pancreatic CT value （t=-3.224， P=0.002）， and preoperative blood amylase level （Z=-2.099， P=0.036） were closely associated with POPF， and the multivariate logistic regression analysis showed that main pancreatic duct index （odds ratio ［OR］=0.000， 95% confidence interval ［CI］： 0.000‍ ‍—‍ ‍0.011， P<0.05）， pancreatic cancer （OR=4.843， 95%CI： 1.285‍ ‍—‍ ‍18.254， P<0.05）， and pancreatic CT value （OR=0.869， 95%CI： 0.806‍ ‍—‍ ‍0.937， P<0.05） were independent risk factors； based on the 2016 edition， the univariate analysis showed the diameter of the main pancreatic duct （χ²=5.391， P=0.020）， main pancreatic duct index （χ²=11.394， P=0.001）， intra-abdominal fat thickness （χ²=8.899， P=0.003）， marginal pancreatic thickness （t=2.665， P=0.009）， pancreatic CT value （t=-2.835， P=0.004） were closely associated with POPF， and the multivariate logistic regression analysis showed that main pancreatic duct index （OR=0.001， 95%CI： 0.000‍ ‍—‍ ‍0.050， P<0.05） and pancreatic CT value （OR=0.943， 95%CI： 0.894‍ ‍—‍ ‍0.994， P<0.05） were independent risk factors. A risk prediction model was established for POPF after PD， and the ROC curve analysis showed that this model had an area under the ROC curve of 0.788 （95%CI： 0.707‍ ‍—‍ ‍0.870） in the modeling group and 0.804 （95%CI： 0.675‍ ‍—‍ ‍0.932） in the validation group. ConclusionMain pancreatic duct index and pancreatic CT value are closely associated with POPF after PD， and the risk prediction model for pancreatic fistula based on the 2016 edition has a good prediction accuracy.

ObjectiveTo explore the establishment and evaluation methods of the rat model of acute myocardial infarction （AMI） in coronary heart disease with the syndrome of Qi and Yin deficiency by sleep deprivation （SD） combined with isoproterenol （ISO） and preliminarily explore its biological basis. MethodForty SD rats were assigned into normal （no treatment）， SD （treatment in modified multi-platform water environment for 96 h）， ISO （subcutaneous injection of ISO at 100 mg·kg^-1 once every other day for a total of 2 times）， and SD+ISO （injection of 100 mg·kg^-1 ISO after SD for 72 h and 96 h） groups. The cardiac function was detected by small animal echocardiography. The serum levels of creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， and cardiac troponin T （cTnT） were measured by biochemical methods. The pathological changes of the myocardial tissue were observed by hematoxylin-eosin staining. The general state， body weight， grip strength， body temperature， behaviors in open field test， serum levels of cyclic adenosine monophosphate （cAMP）， cyclic guanosine monophosphate （cGMP）， cAMP/cGMP ratio， red （R）， green （G）， blue （B） values of the tongue surface， and pulse amplitude were observed and measured to evaluate the modeling results. Enzyme-linked immunosorbent assay was employed to determine the serum levels of interleukin-18 （IL-18）， tumor necrosis factor-α （TNF-α）， superoxide dismutase （SOD）， malondialdehyde （MDA）， corticotropin-releasing factor （CRF）， adrenocorticotropic hormone （ACTH）， triiodothyronine （T3）， tetraiodothyronine （T4）， cluster of differentiation 4 （CD4）， and cluster of differentiation 8 （CD8）. ResultIn terms of disease indicators， the ISO and SD+ISO groups had lower cardiac function indicators than the normal group （P<0.01）. The levels of CK， CM-MB， LDH and cTnT elevated in each model group compared with the normal group （P<0.01）. The pathological changes of myocardial tissue were obvious in the ISO and SD+ISO groups. In terms of syndrome indicators， compared with the normal group， the SD and SD+ISO groups showed decreased body weight at each time point （P<0.01）， and the ISO group showed decreased body weight at the time points of 48 h and 72 h （P<0.05， P<0.01）. The paw temperature and rectal temperature increased in the SD group （P<0.01）. The model groups showed weakened grasp strength， lowered R， G， and B values of the tongue surface （P<0.01）， prolonged immobility time （P<0.01）， reduced total distance and number of entering the central area （P<0.01）， decreased average speed （P<0.05， P<0.01）， and increased cAMP and cGMP （P<0.05， P<0.01）. The cAMP/cGMP ratio was increased in the SD+ISO group （P<0.01）， and the pulse amplitude was decreased in the SD and SD+ISO groups （P<0.01）. In terms of serological indicators，compared with the normal group， the levels of IL-18， TNF-α， SOD and MDA were significantly increased in the ISO and SD+ISO groups （P<0.01）， the CRF， ACTH， CORT， T3， T4， CD4 and CD8 in the model groups were increased （P<0.05， P<0.01）. ConclusionSleep deprivation for 96 h combined with high-dose ISO can successfully establish a rat model of acute myocardial infarction in coronary heart disease with the syndrome of Qi and Yin deficiency. The model evaluation system can be built with disease indicators of western medicine， histopathological indicators， macroscopic indicators of traditional Chinese medicine， and serological indicators.

Objective:To investigate the clinical features and prognosis of male with anti-melanoma differentiation-associated gene 5 (MDA5) autoantibody.Methods:The clinical data of 246 patients with DM and anti-MDA5 autoantibodies hospitalized by Jiangsu Myositis Cooperation Group from 2017 to 2020 were collected and retrospectively analyzed. Chi-square test was performed to compared between counting data groups; Quantitative data were expressed by M ( Q1, Q3), and rank sum test was used for comparison between groups; Single factor survival analysis was performed by Kaplan-Meier method and Log rank test; Cox regression analysis were used for multivariate survival analysis. Results:①The male group had a higher proportion of rash at the sun exposure area [67.1%(47/70) vs 52.8%(93/176), χ2=4.18, P=0.041] and V-sign [50.0%(35/70) vs 30.7%(54/176), χ2=8.09, P=0.004] than the female group. The male group had higher levels of creatine kinase [112(18, 981)U/L vs 57 (13.6, 1 433)U/L, Z=-3.50, P<0.001] and ferritin [1 500 (166, 32 716)ng/ml vs 569 (18, 14 839)ng/ml, Z=-5.85, P<0.001] than the female group. The proportion of ILD [40.0%(28/70) vs 59.7%(105/176), χ2=7.82, P=0.020] patients and the red blood cell sedimentation rate[31.0(4.0, 101.5)mm/1 h vs 43.4(5.0, 126.5)mm/1 h, Z=-2.22, P=0.026] in the male group was lower than that of the female group, but the proportion of rapidly progressive interstitial lung disease (PR-ILD) [47.1%(33/70) vs 31.3%(55/176), χ2=5.51, P=0.019] was higher than that of the female group. ②In male patients with positive anti-MDA5 antibodies,the death group had a shorter course of disease[1.0(1.0, 3.0) month vs 2.5(0.5,84) month, Z=-3.07, P=0.002], the incidence of arthritis [16.7%(4/24) vs 42.2%(19/45), χ2=4.60, P=0.032] were low than those in survival group,while aspartate aminotransferase (AST)[64(22.1, 565)U/L vs 51(14,601)U/L, Z=-2.42, P=0.016], lactate dehydrogenase (LDH) [485(24,1 464)U/L vs 352(170, 1 213)U/L, Z=-3.38, P=0.001], C-reactive protein (CRP) [11.6(2.9, 61.7) mg/L vs 4.95(0.6, 86.4) mg/L, Z=-1.96, P=0.050], and ferritin levels [2 000(681, 7 676) vs 1 125 (166, 32 716)ng/ml, Z=-3.18, P=0.001] were higher than those in the survival group, and RP-ILD [95.8%(23/24) vs 22.2%(10/45), χ2=33.99, P<0.001] occurred at a significantly higher rate. ③Cox regression analysis indicated that the course of disease LDH level, and RP-ILD were related factors for the prognosis of male anti-MDA5 antibodies [ HR (95% CI)=0.203(0.077, 0.534), P=0.001; HR (95% CI)=1.002(1.001, 1.004), P=0.003; HR (95% CI)=95.674 (10.872, 841.904), P<0.001]. Conclusion:The clinical manifestations of male anti-MDA5 antibody-positive patients are different from those of female. The incidence of ILD is low, but the proportion of PR-ILD is high. The course of disease, serum LDH level, and RP-ILD are prognostic factors of male anti-MDA5 antibody-positive patients.