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Cao J, Li J, Gu Z, Niu JJ, An GS, Jin QQ, Wang YY, Huang P, Sun JH. Combined metabolomics and machine learning algorithms to explore metabolic biomarkers for diagnosis of acute myocardial ischemia. Int J Legal Med 2023; 137:169-180. [PMID: 35348878 DOI: 10.1007/s00414-022-02816-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/15/2022] [Indexed: 01/10/2023]
Abstract
Acute myocardial ischemia (AMI) remains the leading cause of death worldwide, and the post-mortem diagnosis of AMI represents a current challenge for both clinical and forensic pathologists. In the present study, the untargeted metabolomics based on ultra-performance liquid chromatography combined with high-resolution mass spectrometry was applied to analyze serum metabolic signatures from AMI in a rat model (n = 10 per group). A total of 28 endogenous metabolites in serum were significantly altered in AMI group relative to control and sham groups. A set of machine learning algorithms, namely gradient tree boosting (GTB), support vector machine (SVM), random forest (RF), logistic regression (LR), and multilayer perceptron (MLP) models, was used to screen the more valuable metabolites from 28 metabolites to optimize the biomarker panel. The results showed that classification accuracy and performance of MLP model were better than other algorithms when the metabolites consisting of L-threonic acid, N-acetyl-L-cysteine, CMPF, glycocholic acid, L-tyrosine, cholic acid, and glycoursodeoxycholic acid. Finally, 17 blood samples from autopsy cases were applied to validate the classification model's value in human samples. The MLP model constructed based on rat dataset achieved accuracy of 88.23%, and ROC of 0.89 for predicting AMI type II in autopsy cases of sudden cardiac death. The results demonstrated that MLP model based on 7 molecular biomarkers had a good diagnostic performance for both AMI rats and autopsy-based blood samples. Thus, the combination of metabolomics and machine learning algorithms provides a novel strategy for AMI diagnosis.
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Affiliation(s)
- Jie Cao
- Shanghai Key Laboratory of Forensic Medicine (Academy of Forensic Science), 200063, Shanghai, People's Republic of China.,School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong, Shanxi Province, 030604, People's Republic of China
| | - Jian Li
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong, Shanxi Province, 030604, People's Republic of China
| | - Zhen Gu
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong, Shanxi Province, 030604, People's Republic of China
| | - Jia-Jia Niu
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong, Shanxi Province, 030604, People's Republic of China
| | - Guo-Shuai An
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong, Shanxi Province, 030604, People's Republic of China
| | - Qian-Qian Jin
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong, Shanxi Province, 030604, People's Republic of China
| | - Ying-Yuan Wang
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong, Shanxi Province, 030604, People's Republic of China
| | - Ping Huang
- Shanghai Key Laboratory of Forensic Medicine (Academy of Forensic Science), 200063, Shanghai, People's Republic of China
| | - Jun-Hong Sun
- Shanghai Key Laboratory of Forensic Medicine (Academy of Forensic Science), 200063, Shanghai, People's Republic of China. .,School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong, Shanxi Province, 030604, People's Republic of China.
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Assessment of traditional Chinese medicine pattern in a bleomycin-induced pulmonary fibrosis mouse model: A pilot study. JOURNAL OF TRADITIONAL CHINESE MEDICAL SCIENCES 2022. [DOI: 10.1016/j.jtcms.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Du QX, Wang L, Li D, Niu JJ, Zhang XD, Sun JH. Estimating the time of skeletal muscle contusion based on the spatial distribution of neutrophils: a practical approach to forensic problems. Int J Legal Med 2022; 136:149-158. [PMID: 34515836 DOI: 10.1007/s00414-021-02690-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 08/16/2021] [Indexed: 10/20/2022]
Abstract
The study aimed to explore the neutrophil's spatial distributions used to estimate the histological age of contused skeletal muscle, and assessed the accuracy of various indicators, such as the proportion of neutrophils, "neutrophil mean distance," and distribution of neutrophils in areas of "contiguous contour lines." Fifty-five Sprague-Dawley rats were divided randomly into a control group and contusion groups at 1, 1.5, 2, 3, 4, and 6 h, as well as 1, 3, 5, and 15 days, post-injury (n = 5 per group). Nuclei and neutrophils were detected by hematoxylin and eosin (HE) staining and immunohistochemical (IHC) staining. At 0-24 h after injury, the distribution of neutrophils at distances of 100, 200, 300, 400, 500, and 600 µm from adjacent blood vessels was determined, and the best samples were screened to estimate wound age. To estimate wound age as accurately as possible, Fisher discriminant analysis (FDA) of the proportion of neutrophils, neutrophil mean distance, and distribution of neutrophils was performed, and 100.0% and 95.0% of the original and cross-validated cases were correctly classified, respectively. The spatial distribution of neutrophils at different distances from adjacent blood vessels showed a strong correlation with the histological age of contusion skeletal muscle, and the combination of the proportion of neutrophils, neutrophil mean distance, and distribution of neutrophils could be used to accurately estimate wound age.
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Affiliation(s)
- Qiu-Xiang Du
- School of Forensic Medicine, Shanxi Province, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, 030604, People's Republic of China
| | - Liang Wang
- School of Forensic Medicine, Shanxi Province, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, 030604, People's Republic of China
| | - Dan Li
- School of Forensic Medicine, Shanxi Province, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, 030604, People's Republic of China
| | - Jia-Jia Niu
- School of Forensic Medicine, Shanxi Province, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, 030604, People's Republic of China
| | - Xu-Dong Zhang
- School of Forensic Medicine, Shanxi Province, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, 030604, People's Republic of China
| | - Jun-Hong Sun
- School of Forensic Medicine, Shanxi Province, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, 030604, People's Republic of China.
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Majam M, Phatsoane M, Hanna K, Faul C, Arora L, Makthal S, Kumar A, Jois K, Lalla-Edward ST. Utility of a Machine-Guided Tool for Assessing Risk Behavior Associated With Contracting HIV in Three Sites in South Africa: Protocol for an In-Field Evaluation. JMIR Res Protoc 2021; 10:e30304. [PMID: 34860679 PMCID: PMC8686409 DOI: 10.2196/30304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/25/2021] [Accepted: 09/10/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Mobile technology has helped to advance health programs, and studies have shown that an automated risk prediction model can successfully be used to identify patients who exhibit a high probable risk of contracting human immunodeficiency virus (HIV). A machine-guided tool is an algorithm that takes a set of subjective and objective answers from a simple questionnaire and computes an HIV risk assessment score. OBJECTIVE The primary objective of this study is to establish that machine learning can be used to develop machine-guided tools and give us a deeper statistical understanding of the correlation between certain behavioral patterns and HIV. METHODS In total, 200 HIV-negative adult individuals across three South African study sites each (two semirural and one urban) will be recruited. Study processes will include (1) completing a series of questions (demographic, sexual behavior and history, personal, lifestyle, and symptoms) on an application system, unaided (assistance will only be provided upon user request); (2) two HIV tests (one per study visit) being performed by a nurse/counselor according to South African national guidelines (to evaluate the prediction accuracy of the tool); and (3) communicating test results and completing a user experience survey questionnaire. The output metrics for this study will be computed by using the participants' risk assessment scores as "predictions" and the test results as the "ground truth." Analyses will be completed after visit 1 and then again after visit 2. All risk assessment scores will be used to calculate the reliability of the machine-guided tool. RESULTS Ethical approval was received from the University of Witwatersrand Human Research Ethics Committee (HREC; ethics reference no. 200312) on August 20, 2020. This study is ongoing. Data collection has commenced and is expected to be completed in the second half of 2021. We will report on the machine-guided tool's performance and usability, together with user satisfaction and recommendations for improvement. CONCLUSIONS Machine-guided risk assessment tools can provide a cost-effective alternative to large-scale HIV screening and help in providing targeted counseling and testing to prevent the spread of HIV. TRIAL REGISTRATION South African National Clinical Trial Registry DOH-27-042021-679; https://sanctr.samrc.ac.za/TrialDisplay.aspx?TrialID=5545. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/30304.
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Affiliation(s)
- Mohammed Majam
- Ezintsha, Faculty of Health Sciences, University of Witswatersrand, Johannesburg, South Africa
| | - Mothepane Phatsoane
- Ezintsha, Faculty of Health Sciences, University of Witswatersrand, Johannesburg, South Africa
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Li N, Bai RF, Li C, Dang LH, Du QX, Jin QQ, Cao J, Wang YY, Sun JH. Insight into molecular profile changes after skeletal muscle contusion using microarray and bioinformatics analyses. Biosci Rep 2021; 41:BSR20203699. [PMID: 33398324 PMCID: PMC7816072 DOI: 10.1042/bsr20203699] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/08/2020] [Accepted: 01/04/2021] [Indexed: 12/18/2022] Open
Abstract
Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. The present study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process. A total of 33 rats were divided randomly into control (n=3), mild contusion (n=15), and severe contusion (n=15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n=3 per subgroup). A total of 2844 and 2298 differentially expressed genes (DEGs) were identified using microarray analyses in the mild and severe contusions, respectively. From the analysis of the 1620 coexpressed genes in mildly and severely contused muscle, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. The functional analyses of genes in the functional modules and temporal clusters were performed, and the hub genes in each module-cluster pair were identified. Interestingly, we found that genes down-regulated at 24-48 h were largely associated with metabolic processes, especially of the oxidative phosphorylation (OXPHOS), which has been rarely reported. These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.
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Affiliation(s)
- Na Li
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030604, Shanxi, China
| | - Ru-feng Bai
- Key Laboratory of Evidence Science, China University of Political Science and law, Beijing, China
- Collaborative Innovation Center of Judicial Civilization, Beijing, China
| | - Chun Li
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030604, Shanxi, China
| | - Li-hong Dang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030604, Shanxi, China
| | - Qiu-xiang Du
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030604, Shanxi, China
| | - Qian-qian Jin
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030604, Shanxi, China
| | - Jie Cao
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030604, Shanxi, China
| | - Ying-yuan Wang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030604, Shanxi, China
| | - Jun-hong Sun
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030604, Shanxi, China
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Multilayer perceptron based deep neural network for early detection of coronary heart disease. HEALTH AND TECHNOLOGY 2020. [DOI: 10.1007/s12553-020-00509-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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