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Jianmin Gong,1,2,* Bo Shi,1,3,* Ping Yang,1 Adeel Khan,4 Tao Xiong,2 Zhiyang Li1 1Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210000, People’s Republic of China; 2College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China; 3Department of Clinical Laboratory, Nanjing Jiangning Hospital of Chinese Medicine (CM), Nanjing, 211100, People’s Republic of China; 4Department of Biotechnology, University of Science and Technology Bannu, Bannu, 28100, Islamic Republic of Pakistan*These authors contributed equally to this workCorrespondence: Tao Xiong, College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China, Tel +8613872387410, Email xiongtao@yangtzeu.edu.cn Zhiyang Li, Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, People’s Republic of China, Tel +8618951703765, Email lizhiyang@nju.edu.cnBackground: Hypertrophic cardiomyopathy (HCM) is a dominantly inherited disease associated with sudden immune cell associations that remain unclear. The aim of this study was to comprehensively screen candidate markers associated with HCM and immune cells and explore potential pathogenic pathways.Methods: First, download the GSE32453 dataset to identify differentially expressed genes (DEGs) and perform Gene Ontology and pathway enrichment analysis using DAVID and GSEA. Next, construct protein-protein interaction (PPI) networks using String and Cytoscape to identify hub genes. Afterward, use CIBERSORT to determine the proportion of immune cells attributed to key genes in HCM and conduct ROC analysis based on the external dataset GSE36961 to evaluate their diagnostic value. Finally, validate the expression of key genes in the hypertrophic cardiomyocyte model through qRT-PCR using data from the HPA database.Results: Comprehensive analysis revealed that there were 254 upregulated genes and 181 downregulated genes in HCM. The enrichment study underscored pathways of inflammatory signaling, including MAPK and PI3K-Akt pathways. Pathways abundant in genes associated with HCM encompassed myocardial contraction and NADH dehydrogenase activity. Additionally, the analysis of immune infiltration revealed a notable increase in macrophages, NK cells, and monocytes in the HCM group, showing statistically significant variances in CD4 memory resting T cell infiltration when compared to the healthy control group. Within the validation dataset GSE36961, the Area Under the Curve (AUC) scores for eight crucial genes (FOS, CD86, CD68, BDNF, PIK3R1, PLEK, RAC2, CCL2) each exceeded 0.8. The HPA database revealed the positioning traits and paths of these eight crucial genes in smooth muscle cells, myocardial cells, and fibroblasts. The outcomes of the qRT-PCR were aligned with the sequencing findings.Conclusion: Bioinformatics analysis unveiled pivotal genes, pathways, and immune involvement, illuminating the molecular underpinnings of HCM. These findings suggest promising therapeutic targets for clinical applications.Keywords: hypertrophic cardiomyopathy, bioinformatic, immune cells infiltration, biomarker

Chenwei Zhang,1,2,* Lu Yu,1,2,* Tao Xiong,2 Yukai Zhang,2 Juan Liu,1 Jingfen Zhang,3 Peiyun He,3 Yujia Xi,4 Yi Jiang1,5 1Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 2The First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 3Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 4Department of Urology, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 5NHC Key Laboratory of Pneumoconiosis, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yujia Xi, Department of Urology, The Second Hospital of Shanxi Medical University, No. 382, Wuyi Road, Taiyuan, 030000, People’s Republic of China, Email xyj20001030@126.com Yi Jiang, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, No. 85, South Jiefang Road, Taiyuan, 030000, People’s Republic of China, Email 8jiangyi@163.comBackground: Chronic Obstructive Pulmonary Disease (COPD), the most prevalent chronic respiratory condition, significantly impairs patients’ quality of life. The pivotal element in disease management lies in prevention, underscoring the paramount importance of employing a scientific approach to investigate early prevention strategies for COPD.Methods: This study delved into the causal link between 28 dietary intakes and COPD employing two-sample Mendelian randomization. We primarily utilized the Inverse Variance Weighted (IVW) method as the main outcome, complemented by Weighted Median (WM), MR-Egger method, along with several sensitivity analysis techniques, all accompanied by visual representations.Results: We identified higher odds of COPD following exposure to green beans (OR=1.381, 95% CI=1.119– 1.704, P=0.003) and pork intake (OR=2.657, 95% CI=1.203– 5.868, P=0.016). In contrast, the odds of developing COPD were lower following exposure to dried fruit (OR=0.481, 95% CI=0.283– 0.819, P=0.007), cereal (OR=0.560, 95% CI=0.356– 0.880, P=0.012), and whole egg consumption (OR=0.700, 95% CI=0.504– 0.972, P=0.033).Conclusion: In light of our study’s findings, we anticipate that strategically modifying dietary choices may offer an avenue for early COPD prevention in the future.Keywords: Mendelian randomization, dietary intake, chronic obstructive pulmonary disease, causal relationship, early prevention

Ting Xiong,1 Xinyue Bai,1 Xingyi Wei,1 Lezheng Wang,1 Fei Li,2 Hui Shi,3 Yue Shi2 1School of Exercise and Health, Shanghai University of Sport, Shanghai, 200438, People’s Republic of China; 2School of Athletic Performance, Shanghai University of Sport, Shanghai, 200438, People’s Republic of China; 3Department of Rheumatology and Immunology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of ChinaCorrespondence: Yue Shi, School of Athletic Performance, Shanghai University of Sport, No. 399, Changhai Road, Shanghai, 200438, People’s Republic of China, Tel +86-21- 18521361033, Fax +86-21-65508050, Email shiyue910@sina.comAbstract: Chronic respiratory diseases (CRD), is a group of disorders, primarily chronic obstructive pulmonary disease and asthma, which are characterized by high prevalence and disability, recurrent acute exacerbations, and multiple comorbidities, resulting in exercise limitations and reduced health-related quality of life. Exercise training, an important tool in pulmonary rehabilitation, reduces adverse symptoms in patients by relieving respiratory limitations, increasing gas exchange, increasing central and peripheral hemodynamic forces, and enhancing skeletal muscle function. Aerobic, resistance, and high-intensity intermittent exercises, and other emerging forms such as aquatic exercise and Tai Chi effectively improve exercise capacity, physical fitness, and pulmonary function in patients with CRD. The underlying mechanisms include enhancement of the body’s immune response, better control of the inflammatory response, and acceleration of the interaction between the vagus and sympathetic nerves to improve gas exchange. Here, we reviewed the new evidence of benefits and mechanisms of exercise intervention in the pulmonary rehabilitation of patients with chronic obstructive pulmonary disease, bronchial asthma, bronchiectasis, interstitial lung disease, and lung cancer.Keywors: chronic respiratory disease, exercise training, chronic obstructive pulmonary disease, asthma, interstitial lung disease, bronchiectasis, lung cancer

Yuheng Jia1 &ast;, Gaden Luosang2,3 &ast;, Yiming Li,1 Jianyong Wang,2 Pengyu Li,4 Tianyuan Xiong,1 Yijian Li,1 Yanbiao Liao,1 Zhengang Zhao,1 Yong Peng,1 Yuan Feng,1 Weili Jiang,2 Wenjian Li,2 Xinpei Zhang,2 Zhang Yi,2 Mao Chen1 1Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 2Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 3Department of Information Science and Technology, Tibet University, Lhasa City, People’s Republic of China; 4West China Medical School, Sichuan University, Chengdu, Sichuan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Mao ChenDepartment of Cardiology, West China Hospital, Sichuan University, No. 37 Guoxue Street, Chengdu, 610041, People’s Republic of ChinaTel +86-18980602046Fax +86-28-85423169Email hmaochen@vip.sina.comZhang YiMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of ChinaTel +86-13882217717Fax +86-28-85466062Email zhangyi@scu.edu.cnPurpose: Late major bleeding is one of the main complications after transcatheter aortic valve replacement (TAVR). We aimed to develop a risk prediction model based on deep learning to predict major or life-threatening bleeding complications (MLBCs) after TAVR.Patients and Methods: This was a retrospective study including TAVR patients from West China Hospital of Sichuan University Transcatheter Aortic Valve Replacement Registry (ChiCTR2000033419) between April 17, 2012 and May 27, 2020. A deep learning-based model named BLeNet was developed with 56 features covering baseline, procedural, and post-procedural characteristics. The model was validated with the bootstrap method and evaluated using Harrell’s concordance index (c-index), receiver operating characteristics (ROC) curve, calibration curve, and Kaplan–Meier estimate. Captum interpretation library was applied to identify feature importance. The BLeNet model was compared with the traditional Cox proportional hazard (Cox-PH) model and the random survival forest model in the metrics mentioned above.Results: The BLeNet model outperformed the Cox-PH and random survival forest models significantly in discrimination [optimism-corrected c-index of BLeNet vs Cox-PH vs random survival forest: 0.81 (95% CI: 0.79– 0.92) vs 0.72 (95% CI: 0.63– 0.77) vs 0.70 (95% CI: 0.61– 0.74)] and calibration (integrated calibration index of BLeNet vs Cox-PH vs random survival forest: 0.007 vs 0.015 vs 0.019). In Kaplan–Meier analysis, BLeNet model had great performance in stratifying high- and low-bleeding risk patients (p < 0.0001).Conclusion: Deep learning is a feasible way to build prediction models concerning TAVR prognosis. A dedicated bleeding risk prediction model was developed for TAVR patients to facilitate well-informed clinical decisions.Keywords: deep learning, transcatheter aortic valve replacement, major or life-threatening bleeding complications, prediction model

Meigan cai and zao cai are two important traditional fermented vegetables in Fujian Province, China. However, there is little information on the bacterial community of these two fermented vegetables. In the present work, we evaluated and compared the physicochemical characteristics and bacterial communities of meigan cai and zao cai. Results revealed that Firmicutes and Proteobacteria were the main dominant phyla, and Lactobacillus and Serratia were the main genera. Principal coordinate and significance analysis showed that the bacterial communities were similar between meigan cai and zao cai. Except for glucose, there was no significant difference in the physicochemical characteristics between meigan cai and zao cai. Our study provided information on the bacterial communities of meigan cai and zao cai, and a guide for the production of these two fermented vegetables. [ABSTRACT FROM AUTHOR]

To study the genetic diversity of Chinese indigenous pig breeds, a total of 403 pigs from 10 local populations and 1 exotic Duroc breed were genotyped for 20 microsatellite markers. Heterozygosity and Wright's F-statistics ([F.sub.IS], [F.sub.ST], and [F.sub.IT]) were calculated to determine the genetic variation in those populations. The observed heterozygosities were in the range of 0.31 (Duroc) to 0.66 (Shengxian). The [F.sub.IS] value was in a range of-0.07 to 0.48. The mean [F.sub.ST] showed that approximately 78% of the genetic variation was within-population and 22% was across the populations. The 10 Chinese local breeds were classified into two major groups according to the phylogenetic tree, which was based on standard genetic distance. Four pig populations, Jianli, Ganxi Two Ends Black, Shaziling, and Dongshan were grouped into one branch. Before the study, these four populations were all classified as Central China Two Ends Black according to coat color, shape of the head, and shape of the ear. The Jinhua pig, which also has the two-ends-black coat color, was also grouped to the same branch but was not traditionally classified into this type. The five populations were located in various provinces in central China. The other five populations, Nanyang Black, Hainan Spotted, Huainan Black, Jiaxing Black, and Shengxian Spotted (black body, white feet), were grouped into another branch. The two groups of pig breeds had the same FST value (0.14) when calculated separately. This value was similar to that of Iberian pigs (0.13) but smaller than that of the European pigs (0.27) as reported by other researchers. Our study showed that large genetic differentiation exists in Chinese pig breeds. The grouping of the five two-ends-black populations into one branch of the phylogenetic tree may indicate that the number of conservation farms can be decreased for this type of pig. Key Words: Chinese Indigenous Population, Genetic Diversity, Microsatellite, Pig