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Zhou J, Tian M, Zhang X, Xiong L, Huang J, Xu M, Xu H, Yin Z, Wu F, Hu J, Liang X, Wei S. Suicide among lymphoma patients. J Affect Disord 2024; 360:97-107. [PMID: 38821367 DOI: 10.1016/j.jad.2024.05.158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUND Higher suicide rates were observed in patients diagnosed with lymphoma. In this study, we accurately identified patients with high-risk lymphoma for suicide by constructing a nomogram with a view to effective interventions and reducing the risk of suicide. METHODS 235,806 patients diagnosed with lymphoma between 2000 and 2020 were picked from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (N = 165,064) and validation set (N = 70,742). A combination of the Least absolute shrinkage and selection operator (LASSO) and Cox proportional hazards regression identified the predictors that constructed the nomogram. To assess the discrimination, calibration, clinical applicability, and generalization of this nomogram, we implemented receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), and internal validation. The robustness of the results was assessed by the competing risks regression model. RESULTS Age at diagnosis, gender, ethnicity, marital status, stage, surgery, radiotherapy, and annual household income were key predictors of suicide in lymphoma patients. A nomogram was created to visualize the risk of suicide after a lymphoma diagnosis. The c-index for the training set was 0.773, and the validation set was 0.777. The calibration curve for the nomogram fitted well with the diagonal and the clinical decision curve indicated its clinical benefit. LIMITATION The effects of unmeasured and unnoticed biases and confounders were difficult to eliminate due to retrospective studies. CONCLUSION A convenient and reliable model has been constructed that will help to individualize and accurately quantify the risk of suicide in patients diagnosed with lymphoma.
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Affiliation(s)
- Jie Zhou
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China
| | - Mengjie Tian
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Xiangchen Zhang
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China
| | - Lingyi Xiong
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Jinlong Huang
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China
| | - Mengfan Xu
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Hongli Xu
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Zhucheng Yin
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Fengyang Wu
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Junjie Hu
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China
| | - Xinjun Liang
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China.
| | - Shaozhong Wei
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China.
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Fu X, Ma W, Zuo Q, Qi Y, Zhang S, Zhao Y. Application of machine learning for high-throughput tumor marker screening. Life Sci 2024; 348:122634. [PMID: 38685558 DOI: 10.1016/j.lfs.2024.122634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/26/2024] [Accepted: 04/10/2024] [Indexed: 05/02/2024]
Abstract
High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.
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Affiliation(s)
- Xingxing Fu
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
| | - Wanting Ma
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
| | - Qi Zuo
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
| | - Yanfei Qi
- Centenary Institute, The University of Sydney, Sydney, NSW 2050, Australia
| | - Shubiao Zhang
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China.
| | - Yinan Zhao
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
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Yan L, Chen C, Wang L, Hong H, Wu C, Huang J, Jiang J, Chen J, Xu G, Cui Z. Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms. Exp Ther Med 2024; 28:292. [PMID: 38827468 PMCID: PMC11140288 DOI: 10.3892/etm.2024.12581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 04/17/2024] [Indexed: 06/04/2024] Open
Abstract
Spinal cord injury (SCI) is a severe neurological complication following spinal fracture, which has long posed a challenge for clinicians. Microglia play a dual role in the pathophysiological process after SCI, both beneficial and detrimental. The underlying mechanisms of microglial actions following SCI require further exploration. The present study combined three different machine learning algorithms, namely weighted gene co-expression network analysis, random forest analysis and least absolute shrinkage and selection operator analysis, to screen for differentially expressed genes in the GSE96055 microglia dataset after SCI. It then used protein-protein interaction networks and gene set enrichment analysis with single genes to investigate the key genes and signaling pathways involved in microglial function following SCI. The results indicated that microglia not only participate in neuroinflammation but also serve a significant role in the clearance mechanism of apoptotic cells following SCI. Notably, bioinformatics analysis and lipopolysaccharide + UNC569 (a MerTK-specific inhibitor) stimulation of BV2 cell experiments showed that the expression levels of Anxa2, Myo1e and Spp1 in microglia were significantly upregulated following SCI, thus potentially involved in regulating the clearance mechanism of apoptotic cells. The present study suggested that Anxa2, Myo1e and Spp1 may serve as potential targets for the future treatment of SCI and provided a theoretical basis for the development of new methods and drugs for treating SCI.
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Affiliation(s)
- Lei Yan
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Chu Chen
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Lingling Wang
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Hongxiang Hong
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Chunshuai Wu
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Jiayi Huang
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Jiawei Jiang
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Jiajia Chen
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Guanhua Xu
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Zhiming Cui
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
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Tang BH, Li QY, Liu HX, Zheng Y, Wu YE, van den Anker J, Hao GX, Zhao W. Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making. Paediatr Drugs 2024; 26:355-363. [PMID: 38880837 DOI: 10.1007/s40272-024-00638-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 06/18/2024]
Abstract
Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.
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Affiliation(s)
- Bo-Hao Tang
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui-Xin Liu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Department of Pediatrics, Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Departments of Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Wei Zhao
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
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Zou X, Cui N, Ma Q, Lin Z, Zhang J, Li X. Development of a machine learning model for predicting pneumothorax risk in coaxial core needle biopsy (≤3 cm). Eur J Radiol 2024; 176:111508. [PMID: 38759543 DOI: 10.1016/j.ejrad.2024.111508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/31/2024] [Accepted: 05/13/2024] [Indexed: 05/19/2024]
Abstract
PURPOSE The aim is to devise a machine learning algorithm exploiting preoperative clinical data to forecast the hazard of pneumothorax post-coaxial needle lung biopsy (CCNB), thereby informing clinical decision-making and enhancing perioperative care. METHOD This retrospective analysis aggregated clinical and imaging data from patients with lung nodules (≤3 cm) biopsies. Variable selection was done using univariate analysis and LASSO regression, with the dataset subsequently divided into training (80 %) and validation (20 %) subsets. Various machine learning (ML) classifiers were employed in a consolidated approach to ascertain the paramount model, which was followed by individualized risk profiling showcased through Shapley Additive eXplanations (SHAP). RESULTS Out of the 325 patients included in the study, 19.6% (64/325) experienced postoperative pneumothorax. High-risk factors determined were Cancer, Lesion_type, GOLD, Size, and Depth. The Gaussian Naive Bayes (GNB) classifier demonstrated superior prediction with an Area Under the Curve (AUC) of 0.82 (95% CI 0.71-0.94), complemented by an accuracy rate of 0.8, sensitivity of 0.71, specificity of 0.84, and an F1 score of 0.61 in the test cohort. CONCLUSION The formulated prognostic algorithm exhibited commendable efficacy in preoperatively prognosticating CCNB-induced pneumothorax, harboring the potential to refine personalized risk appraisals, steer clinical judgment, and ameliorate perioperative patient stewardship.
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Affiliation(s)
- Xugong Zou
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China
| | - Ning Cui
- Medical Imaging Center, Taihe Hospital, Shiyan City, Hubei Province, China
| | - Qiang Ma
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China
| | - Zhipeng Lin
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China
| | - Jian Zhang
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China
| | - Xiaoqun Li
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China.
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Han T, Bai Y, Liu Y, Dong Y, Liang C, Gao L, Zhou J, Guo J, Wu J, Hu D. Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma. Funct Integr Genomics 2024; 24:118. [PMID: 38935217 DOI: 10.1007/s10142-024-01388-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 04/01/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024]
Abstract
Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.
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Affiliation(s)
- Tao Han
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Ying Bai
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China.
| | - Yafeng Liu
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Yunjia Dong
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Chao Liang
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Lu Gao
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Jiawei Zhou
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Jianqiang Guo
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Jing Wu
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China.
- Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institute, Huainan, Anhui, China.
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, Anhui, China.
| | - Dong Hu
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China.
- Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institute, Huainan, Anhui, China.
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, Anhui, China.
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Wali R, Xu H, Cheruiyot C, Saleem HN, Janshoff A, Habeck M, Ebert A. Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy. Biol Chem 2024; 405:427-439. [PMID: 38651266 DOI: 10.1515/hsz-2024-0023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 03/27/2024] [Indexed: 04/25/2024]
Abstract
Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs. We established a multimodal data fusion (MDF)-based analysis to integrate source datasets for Ca2+ transients, force measurements, and contractility recordings. Data were acquired for three additional layer types, single cells, cell monolayers, and 3D spheroid iPSC-CM models. For data analysis, numerical conversion as well as fusion of data from Ca2+ transients, force measurements, and contractility recordings, a non-negative blind deconvolution (NNBD)-based method was applied. Using an XGBoost algorithm, we found a high prediction accuracy for fused single cell, monolayer, and 3D spheroid iPSC-CM models (≥92 ± 0.08 %), as well as for fused Ca2+ transient, beating force, and contractility models (>96 ± 0.04 %). Integrating MDF and XGBoost provides a highly effective analysis tool for prediction of patho-phenotypic features in complex human disease models such as DCM iPSC-CMs.
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Affiliation(s)
- Ruheen Wali
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
| | - Hang Xu
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
| | - Cleophas Cheruiyot
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
| | - Hafiza Nosheen Saleem
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
| | - Andreas Janshoff
- Institute for Physical Chemistry, Göttingen University, Tammannstraße 6, D-37077 Göttingen, Germany
| | - Michael Habeck
- Microscopic Image Analysis, 39065 Jena University Hospital , Kollegiengasse 10, D-07743 Jena, Germany
| | - Antje Ebert
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
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Sun M, Sun J, Li M. Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis. Sci Rep 2024; 14:14490. [PMID: 38914641 PMCID: PMC11196279 DOI: 10.1038/s41598-024-65367-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 06/19/2024] [Indexed: 06/26/2024] Open
Abstract
Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SEER database and randomly divided the dataset into training and testing datasets in a 7:3 ratio. We chose three models to build, one based on neural networks (DeepSurv), one based on ensemble learning that Random Survival Forest (RSF), and a typical Cox Proportional-hazards (CoxPH) model. The DeepSurv model outperformed the RSF and classic CoxPH models with C-indexes of 0.751 and 0.763 for the training and test datasets. Additionally, the DeepSurv model showed better accuracy in predicting 1-, 3-, and 5-year survival rates (AUC: 0.767-0.793). Therefore, our prediction model based on deep learning algorithms can more accurately predict the survival rate and survival period of medulloblastoma compared to other models.
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Affiliation(s)
- Meng Sun
- Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014, Shandong, China
| | - Jikui Sun
- Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014, Shandong, China.
| | - Meng Li
- Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014, Shandong, China.
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He Y, Huang R, Zhang R, He F, Han L, Han W. PredCoffee: A binary classification approach specifically for coffee odor. iScience 2024; 27:110041. [PMID: 38868178 PMCID: PMC11167484 DOI: 10.1016/j.isci.2024.110041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/26/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
Compared to traditional methods, using machine learning to assess or predict the odor of molecules can save costs in various aspects. Our research aims to collect molecules with coffee odor and summarize the regularity of these molecules, ultimately creating a binary classifier that can determine whether a molecule has a coffee odor. In this study, a total of 371 coffee-odor molecules and 9,700 non-coffee-odor molecules were collected. The Knowledge-guided Pre-training of Graph Transformer (KPGT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and message-passing neural networks (MPNN) were used to train the data. The model with the best performance was selected as the basis of the predictor. The prediction accuracy value of the KPGT model exceeded 0.84 and the predictor has been deployed as a webserver PredCoffee.
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Affiliation(s)
- Yi He
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Ruirui Huang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Ruoyu Zhang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Fei He
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Lu Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
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10
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Yurchenko A, Özkul G, van Riel NAW, van Hest JCM, de Greef TFA. Mechanism-based and data-driven modeling in cell-free synthetic biology. Chem Commun (Camb) 2024; 60:6466-6475. [PMID: 38847387 DOI: 10.1039/d4cc01289e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.
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Affiliation(s)
- Angelina Yurchenko
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Gökçe Özkul
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Natal A W van Riel
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Eindhoven MedTech Innovation Center, 5612 AX Eindhoven, The Netherlands
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jan C M van Hest
- Bio-Organic Chemistry, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
- Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
| | - Tom F A de Greef
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, The Netherlands
- Center for Living Technologies, Eindhoven-Wageningen-Utrecht Alliance, 3584 CB Utrecht, The Netherlands
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11
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Ye C, Zhu S, Yuan J, Yuan X. FPR1, as a Potential Biomarker of Diagnosis and Infliximab Therapy Responses for Crohn's Disease, is Related to Disease Activity, Inflammation and Macrophage Polarization. J Inflamm Res 2024; 17:3949-3966. [PMID: 38911989 PMCID: PMC11193993 DOI: 10.2147/jir.s459819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/12/2024] [Indexed: 06/25/2024] Open
Abstract
Purpose Crohn's disease (CD) represents a multifaceted inflammatory gastrointestinal condition, with a profound significance placed on unraveling its molecular pathways to enhance both diagnostic capabilities and therapeutic interventions. This study focused on identifying a robust macrophage-related signatures (MacroSig) for diagnosing CD, emphasizing the role of FPR1 in macrophage polarization and its implications in CD. Patients and Methods Expression profiles from intestinal biopsies and macrophages of 1804 CD patients were retrieved from the Gene Expression Omnibus (GEO). Utilizing CIBERSORTx, differential expression analysis, and weighted correlation network analysis to to identify macrophage-related genes (MRGs). By unsupervised clustering, distinct clusters of CD were identified. Potential biomarkers were identified via using four machine learning algorithms, leading to the establishment of MacroSig which combines insights from 12 machine learning algorithms. Furthermore, the expression of FPR1 was verified in intestinal biopsies of CD patients and two murine experimental colitis models. Finally, we further explored the role of FPR1 in macrophage polarization through single-cell analysis as well as through the study of RAW264.7 cells and peritoneal macrophages. Results Two distinct clusters with differential levels of macrophage infiltration and inflammation were identified. The MacroSig, which included FPR1 and LILRB2, exhibited high diagnostic accuracy and outperformed existing biomarkers and signatures. Clinical analysis demonstrated a strong correlation of FPR1 with disease activity, endoscopic inflammation status, and response to infliximab treatment. The expression levels of FPR1 were validated in our CD cohort by immunohistochemistry and confirmed in two colitis mouse models. Single-cell analysis indicated that FPR1 is predominantly expressed in macrophages and monocytes. In vitro studies demonstrated that FPR1 was upregulated in M1 macrophages, and its activation promoted M1 polarization. Conclusion We developed a promising diagnostic signature for CD, and targeting FPR1 to modulate macrophage polarization may represent a novel therapeutic strategy.
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Affiliation(s)
- Chenglin Ye
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Sizhe Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, Hubei, People’s Republic of China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Xiuxue Yuan
- Medical College of Wuhan University of Science and Technology, Wuhan, Hubei, People’s Republic of China
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12
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Zheng Y, Zhang C, Sun X, Kang K, Luo R, Zhao A, Wu Y. Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning. Clin Exp Med 2024; 24:132. [PMID: 38890203 PMCID: PMC11189314 DOI: 10.1007/s10238-024-01402-3] [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: 04/24/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024]
Abstract
Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet there is a lack of survival analysis tailored to pediatric lymphoma. We analyzed lymphoma data from 1975 to 2018, comparing survival trends between 7,871 pediatric and 226,211 adult patients, identified key risk factors for pediatric lymphoma survival, developed a predictive nomogram, and utilized machine learning to predict long-term lymphoma-specific mortality risk. Between 1975 and 2018, we observed substantial increases in 1-year (19.3%), 5-year (41.9%), and 10-year (48.8%) overall survival rates in pediatric patients with lymphoma. Prognostic factors such as age, sex, race, Ann Arbor stage, lymphoma subtypes, and radiotherapy were incorporated into the nomogram. The nomogram exhibited excellent predictive performance with area under the curve (AUC) values of 0.766, 0.724, and 0.703 for one-year, five-year, and ten-year survival, respectively, in the training cohort, and AUC values of 0.776, 0.712, and 0.696 in the validation cohort. Importantly, the nomogram outperformed the Ann Arbor staging system in survival prediction. Machine learning models achieved AUC values of approximately 0.75, surpassing the conventional method (AUC = ~ 0.70) in predicting the risk of lymphoma-specific death. We also observed that pediatric lymphoma survivors had a substantially reduced risk of lymphoma after ten years b,ut faced an increasing risk of non-lymphoma diseases. The study highlights substantial improvements in pediatric lymphoma survival, offers reliable predictive tools, and underscores the importance of long-term monitoring for non-lymphoma health issues in pediatric patients.
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Grants
- No. 82303773, No. 82303772, No. 82204490, No. 82303694 National Natural Science Foundation of China
- No. 82303773, No. 82303772, No. 82204490, No. 82303694 National Natural Science Foundation of China
- No. 82303773, No. 82303772, No. 82204490, No. 82303694 National Natural Science Foundation of China
- No. 82303773, No. 82303772, No. 82204490, No. 82303694 National Natural Science Foundation of China
- No. 2023NSFSC1885 Natural Science Foundation of Sichuan Province
- No. 2023NSFSC1885 Natural Science Foundation of Sichuan Province
- No. 2023YFS0306 Key Research, Development Program of Sichuan Province
- No. 2023YFS0306 Key Research, Development Program of Sichuan Province
- No. GZB20230481 Postdoctoral Fellowship Program of CPSF
- No. 2024HXBH149, No. 2024HXBH006 Post-Doctor Research Project, West China Hospital, Sichuan University
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Affiliation(s)
- Yue Zheng
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China
| | - Chunlan Zhang
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China
| | - Xu Sun
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China
| | - Kai Kang
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ren Luo
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ailin Zhao
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China.
| | - Yijun Wu
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
- Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China.
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13
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Huang D, Gong L, Wei C, Wang X, Liang Z. An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease. Respir Res 2024; 25:246. [PMID: 38890628 PMCID: PMC11186131 DOI: 10.1186/s12931-024-02874-3] [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: 02/01/2024] [Accepted: 06/09/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients. METHODS This was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model. RESULTS The included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-β-D-glucan test (G test). CONCLUSION We successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.
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Affiliation(s)
- Dong Huang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
| | - Linjing Gong
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
| | - Chang Wei
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
| | - Xinyu Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
| | - Zongan Liang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China.
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14
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Wang Y, Cao Y, Li Y, Zhu F, Yuan M, Xu J, Ma X, Li J. Development of an immunoinflammatory indicator-related dynamic nomogram based on machine learning for the prediction of intravenous immunoglobulin-resistant Kawasaki disease patients. Int Immunopharmacol 2024; 134:112194. [PMID: 38703570 DOI: 10.1016/j.intimp.2024.112194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Approximately 10-20% of Kawasaki disease (KD) patients suffer from intravenous immunoglobulin (IVIG) resistance, placing them at higher risk of developing coronary artery aneurysms. Therefore, we aimed to construct an IVIG resistance prediction tool for children with KD in Shanghai, China. METHODS Retrospective analysis was conducted on data from 1271 patients diagnosed with KD and the patients were randomly divided into a training set and a validation set in a 2:1 ratio. Machine learning algorithms were employed to identify important predictors associated with IVIG resistance and to build a predictive model. The best-performing model was used to construct a dynamic nomogram. Moreover, receiver operating characteristic curves, calibration plots, and decision-curve analysis were utilized to measure the discriminatory power, accuracy, and clinical utility of the nomogram. RESULTS Six variables were identified as important predictors, including C-reactive protein, neutrophil ratio, procalcitonin, CD3 ratio, CD19 count, and IgM level. A dynamic nomogram constructed with these factors was available at https://hktk.shinyapps.io/dynnomapp/. The nomogram demonstrated good diagnostic performance in the training and validation sets (area under the receiver operating characteristic curve = 0.816 and 0.800, respectively). Moreover, the calibration curves and decision curves analysis indicated that the nomogram showed good consistency between predicted and actual outcomes and had good clinical benefits. CONCLUSION A web-based dynamic nomogram for IVIG resistance was constructed with good predictive performance, which can be used as a practical approach for early screening to assist physicians in personalizing the treatment of KD patients in Shanghai.
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Affiliation(s)
- Yue Wang
- Clinical Laboratory Center, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China.
| | - Yinyin Cao
- Cardiovascular Center, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China.
| | - Yang Li
- Clinical Laboratory Center, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China.
| | - Fenhua Zhu
- Clinical Laboratory Center, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China.
| | - Meifen Yuan
- Clinical Laboratory Center, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China.
| | - Jin Xu
- Clinical Laboratory Center, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China.
| | - Xiaojing Ma
- Cardiovascular Center, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China.
| | - Jian Li
- Clinical Laboratory Center, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China.
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15
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Zhang J, Meng Y, Yang M, Hao W, Liu J, Wu L, Yu X, Zhang Y, Lin B, Xie C, Ge L, Zhijie Zhang, Tong W, Chang Q, Liu Y, Zhang Y, Qin X. A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vaccines. Int Immunopharmacol 2024; 134:112141. [PMID: 38733819 DOI: 10.1016/j.intimp.2024.112141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/14/2024] [Accepted: 04/20/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND Novel coronaviruses constitute a significant health threat, prompting the adoption of vaccination as the primary preventive measure. However, current evaluations of immune response and vaccine efficacy are deemed inadequate. OBJECTIVES The study sought to explore the evolving dynamics of immune response at various vaccination time points and during breakthrough infections. It aimed to elucidate the synergistic effects of epidemiological factors, humoral immunity, and cellular immunity. Additionally, regression curves were used to determine the correlation between the protective efficacy of the vaccine and the stimulated immune response. METHODS Employing LASSO for high-dimensional data analysis, the study utilised four machine learning algorithms-logistical regression, random forest, LGBM classifier, and AdaBoost classifier-to comprehensively assess the immune response following booster vaccination. RESULTS Neutralising antibody levels exhibited a rapid surge post-booster, escalating to 102.38 AU/mL at one week and peaking at 298.02 AU/mL at two weeks. Influential factors such as sex, age, disease history, and smoking status significantly impacted post-booster antibody levels. The study further constructed regression curves for neutralising antibodies, non-switched memory B cells, CD4+T cells, and CD8+T cells using LASSO combined with the random forest algorithm. CONCLUSION The establishment of an artificial intelligence evaluation system emerges as pivotal for predicting breakthrough infection prognosis after the COVID-19 booster vaccination. This research underscores the intricate interplay between various components of immunity and external factors, elucidating key insights to enhance vaccine effectiveness. 3D modelling discerned distinctive interactions between humoral and cellular immunity within prognostic groups (Class 0-2). This underscores the critical role of the synergistic effect of humoral immunity, cellular immunity, and epidemiological factors in determining the protective efficacy of COVID-19 vaccines post-booster administration.
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Affiliation(s)
- Jin Zhang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yuan Meng
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Mei Yang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Wudi Hao
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Jianhua Liu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Lina Wu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Xiaojun Yu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yue Zhang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Baoxu Lin
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Chonghong Xie
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Lili Ge
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Zhijie Zhang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Weiwei Tong
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yong Liu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yixiao Zhang
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
| | - Xiaosong Qin
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
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Yang L, Leynes C, Pawelka A, Lorenzo I, Chou A, Lee B, Heaney JD. Machine learning in time-lapse imaging to differentiate embryos from young vs old mice†. Biol Reprod 2024; 110:1115-1124. [PMID: 38685607 PMCID: PMC11180621 DOI: 10.1093/biolre/ioae056] [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: 11/24/2023] [Revised: 02/15/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024] Open
Abstract
Time-lapse microscopy for embryos is a non-invasive technology used to characterize early embryo development. This study employs time-lapse microscopy and machine learning to elucidate changes in embryonic growth kinetics with maternal aging. We analyzed morphokinetic parameters of embryos from young and aged C57BL6/NJ mice via continuous imaging. Our findings show that aged embryos accelerated through cleavage stages (from 5-cells) to morula compared to younger counterparts, with no significant differences observed in later stages of blastulation. Unsupervised machine learning identified two distinct clusters comprising of embryos from aged or young donors. Moreover, in supervised learning, the extreme gradient boosting algorithm successfully predicted the age-related phenotype with 0.78 accuracy, 0.81 precision, and 0.83 recall following hyperparameter tuning. These results highlight two main scientific insights: maternal aging affects embryonic development pace, and artificial intelligence can differentiate between embryos from aged and young maternal mice by a non-invasive approach. Thus, machine learning can be used to identify morphokinetics phenotypes for further studies. This study has potential for future applications in selecting human embryos for embryo transfer, without or in complement with preimplantation genetic testing.
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Affiliation(s)
- Liubin Yang
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, Texas, USA
- Division of Reproductive Endocrinology and Infertility, Division of Reproductive Sciences, Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Carolina Leynes
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Ashley Pawelka
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Isabel Lorenzo
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Andrew Chou
- Pain Research, Informatics, Multi-morbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, USA
- Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Jason D Heaney
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
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17
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Hu Z, Hu Y, Zhang S, Dong L, Chen X, Yang H, Su L, Hou X, Huang X, Shen X, Ye C, Tu W, Chen Y, Chen Y, Cai S, Zhong J, Dong L. Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study. Chin Med J (Engl) 2024:00029330-990000000-01099. [PMID: 38863118 DOI: 10.1097/cm9.0000000000003025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD. METHODS In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort. RESULTS In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone. CONCLUSION ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE. TRIAL REGISTRATION Chictr.org.cn: ChiCTR2200059599.
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Affiliation(s)
- Ziwei Hu
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yangyang Hu
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Shuoqi Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Li Dong
- Department of Rheumatology and Immunology, Jingzhou Central Hospital, Yangtze University, Jinzhou, Hubei 434020, China
| | - Xiaoqi Chen
- Department of Rheumatology and Immunology, Zhongnan Hospital, Wuhan University, Wuhan, Hubei 430071, China
| | - Huiqin Yang
- Department of Rheumatology, Wuhan No.1 Hospital, Wuhan, Hubei 430022, China
| | - Linchong Su
- Department of Rheumatology, Minda Hospital of Hubei Minzu University, Enshi, Hubei 445000, China
| | - Xiaoqiang Hou
- Department of Rheumatology and Immunology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443003, China
| | - Xia Huang
- Department of Rheumatology, Minda Hospital of Hubei Minzu University, Enshi, Hubei 445000, China
| | - Xiaolan Shen
- Department of Rheumatology and Immunology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443003, China
| | - Cong Ye
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Wei Tu
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yu Chen
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yuxue Chen
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Shaozhe Cai
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Jixin Zhong
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Lingli Dong
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
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Hu K, Ou Y, Xiao L, Gu R, He F, Peng J, Shu Y, Li T, Hao L. Identification and Construction of a Disulfidptosis-Mediated Diagnostic Model and Associated Immune Microenvironment of Osteoarthritis from the Perspective of PPPM. J Inflamm Res 2024; 17:3753-3770. [PMID: 38882183 PMCID: PMC11179642 DOI: 10.2147/jir.s462179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/29/2024] [Indexed: 06/18/2024] Open
Abstract
Background Osteoarthritis (OA) is a major cause of human disability. Despite receiving treatment, patients with the middle and late stage of OA have poor survival outcomes. Therefore, within the framework of predictive, preventive, and personalized medicine (PPPM/3PM), early personalized diagnosis of OA is particularly prominent. PPPM aims to accurately identify disease by integrating multiple omic techniques; however, the efficiency of currently available methods and biomarkers in predicting and diagnosing OA should be improved. Disulfidptosis, a novel programmed cell death mechanism and appeared in particular metabolic status, plays a mysterious characteristic in the occurrence and development of OA, which warrants further investigation. Methods In this study, we integrated three public datasets from the Gene Expression Omnibus (GEO) database, including 26 OA samples and 20 normal samples. Via a series of bioinformatic analysis and machine learning, we identified the diagnostic biomarkers and several subtypes of OA. Moreover, the expression of these biomarkers were verified in our in-house cohort and the single cell dataset. Results Three significant regulators of disulfidptosis (NCKAP1, OXSM, and SLC3A2) were identified through differential expression analysis and machine learning. And a nomogram constructed based on these three regulators exhibited ideal efficiency in predicting early- and late-stage OA. Furthermore, based on the expression of three regulators, we identified two disulfidptosis-related subtypes of OA with different infiltration of immune cells and personalized expression level of immune checkpoints. Notably, the expression of the three regulators was demonstrated in a single-cell RNA profile and verified in the synovial tissue in our in-house cohort including 6 OA patients and 6 normal people. Finally, an efficient disulfidptosis-mediated diagnostic model was constructed for OA, with the AUC value of 97.6923% in the training set and 93.3333% and 100% in two validation sets. Conclusion Overall, with regard to PPPM, this study provided novel insights into the role of disulfidptosis regulators in the personalized diagnosis and treatment of OA.
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Affiliation(s)
- Kaibo Hu
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Yanghuan Ou
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Leyang Xiao
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Ruonan Gu
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Fei He
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Jie Peng
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Yuan Shu
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Ting Li
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Liang Hao
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
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19
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Chen J, Shen L, Wu T, Yang Y. Unraveling the significance of AGPAT4 for the pathogenesis of endometriosis via a multi-omics approach. Hum Genet 2024:10.1007/s00439-024-02681-2. [PMID: 38850428 DOI: 10.1007/s00439-024-02681-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/26/2024] [Indexed: 06/10/2024]
Abstract
Endometriosis is characterized by the ectopic proliferation of endometrial cells, posing considerable diagnostic and therapeutic challenges. Our study investigates AGPAT4's involvement in endometriosis pathogenesis, aiming to unveil new therapeutic targets. Our investigation by analyzing eQTL data from GWAS for preliminary screening. Subsequently, within the GEO dataset, we utilized four machine learning algorithms to precisely identify risk-associated genes. Gene validity was confirmed through five Mendelian Randomization methods. AGPAT4 expression was measured by Single-Cell Analysis, ELISA and immunohistochemistry. We investigated AGPAT4's effect on endometrial stromal cells using RNA interference, assessing cell proliferation, invasion, and migration with CCK8, wound-healing, and transwell assays. Protein expression was analyzed by western blot, and AGPAT4 interactions were explored using AutoDock. Our investigation identified 11 genes associated with endometriosis risk, with AGPAT4 and COMT emerging as pivotal biomarkers through machine learning analysis. AGPAT4 exhibited significant upregulation in both ectopic tissues and serum samples from patients with endometriosis. Reduced expression of AGPAT4 was observed to detrimentally impact the proliferation, invasion, and migration capabilities of endometrial stromal cells, concomitant with diminished expression of key signaling molecules such as Wnt3a, β-Catenin, MMP-9, and SNAI2. Molecular docking analyses further underscored a substantive interaction between AGPAT4 and Wnt3a.Our study highlights AGPAT4's key role in endometriosis, influencing endometrial stromal cell behavior, and identifies AGPAT4 pathways as promising therapeutic targets for this condition.
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Affiliation(s)
- Jun Chen
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Licong Shen
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Tingting Wu
- Department of Cardiovasology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Yongwen Yang
- Department of Clinical Laboratory, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, China.
- National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
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20
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Sun H, Han X, Du Z, Chen G, Guo T, Xie F, Gu W, Shi Z. Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing. Br J Cancer 2024:10.1038/s41416-024-02737-0. [PMID: 38849478 DOI: 10.1038/s41416-024-02737-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for identifying Neo T cells and their corresponding T cell receptors (TCRs). METHODS By mapping neoantigen-reactive T cells from the single-cell transcriptomes of thousands of tumour-infiltrating lymphocytes, we developed a 26-gene machine learning model for the identification of neoantigen-reactive T cells. RESULTS In both training and validation sets, the model performed admirably. We discovered that the majority of Neo T cells exhibited notable differences in the biological processes of amide-related signal pathways. The analysis of potential cell-to-cell interactions, in conjunction with spatial transcriptomic and multiplex immunohistochemistry data, has revealed that Neo T cells possess potent signalling molecules, including LTA, which can potentially engage with tumour cells within the tumour microenvironment, thereby exerting anti-tumour effects. By sequencing CD8 + T cells in tumour samples of patients undergoing neoadjuvant immunotherapy, we determined that the fraction of Neo T cells was significantly and positively linked with the clinical benefit and overall survival rate of patients. CONCLUSION This method expedites the identification of neoantigen-reactive TCRs and the engineering of neoantigen-reactive T cells for therapy.
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Affiliation(s)
- Hongwei Sun
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiao Han
- KangChen Bio-tech., Ltd, ShangHai, China
| | - Zhengliang Du
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Geer Chen
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tonglei Guo
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China
| | - Fei Xie
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China
| | - Weiyue Gu
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China
- Chineo Medical Technology Co., Ltd, Beijing, 100101, China
| | - Zhiwen Shi
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China.
- Chineo Medical Technology Co., Ltd, Beijing, 100101, China.
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21
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Ai M, Zhang H, Feng J, Chen H, Liu D, Li C, Yu F, Li C. Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview. PeerJ 2024; 12:e17556. [PMID: 38860211 PMCID: PMC11164062 DOI: 10.7717/peerj.17556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/12/2024] Open
Abstract
Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.
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Affiliation(s)
- Min Ai
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Hanghang Zhang
- Department of Breast and Thyroid Surgery, Chongqing Bishan District Maternal and Child Health Care Hospital, Chongqing, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Hongying Chen
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Di Liu
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Chang Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Fei Yu
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
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Anuntakarun S, Khamjerm J, Tangkijvanich P, Chuaypen N. Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach. Bioinform Biol Insights 2024; 18:11779322241258586. [PMID: 38846329 PMCID: PMC11155358 DOI: 10.1177/11779322241258586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 05/10/2024] [Indexed: 06/09/2024] Open
Abstract
Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.
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Affiliation(s)
- Songtham Anuntakarun
- Center of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Jakkrit Khamjerm
- Center of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Biomedical Engineering Program, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Pisit Tangkijvanich
- Center of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Natthaya Chuaypen
- Center of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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23
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Chen Y, Yang S, Liu R, Xiong R, Wang Y, Li C, Zheng Y, He M, Wang W. Forecasting Myopic Maculopathy Risk Over a Decade: Development and Validation of an Interpretable Machine Learning Algorithm. Invest Ophthalmol Vis Sci 2024; 65:40. [PMID: 38935031 DOI: 10.1167/iovs.65.6.40] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024] Open
Abstract
Purpose The purpose of this study was to develop and validate prediction model for myopic macular degeneration (MMD) progression in patients with high myopia. Methods The Zhongshan High Myopia Cohort for model development included 660 patients aged 7 to 70 years with a bilateral sphere of ≤-6.00 diopters (D). Two hundred twelve participants with an axial length (AL) ≥25.5 mm from the Chinese Ocular Imaging Project were used for external validation. Thirty-four clinical variables, including demographics, lifestyle, myopia history, and swept source optical coherence tomography data, were analyzed. Sequential forward selection was used for predictor selection, and binary classification models were created using five machine learning algorithms to forecast the risk of MMD progression over 10 years. Results Over a median follow-up of 10.9 years, 133 patients (20.2%) showed MMD progression in the development cohort. Among them, 69 (51.9%) developed newly-onset MMD, 11 (8.3%) developed patchy atrophy from diffuse atrophy, 54 (40.6%) showed an enlargement of lesions, and 9 (6.8%) developed plus signs. Top six predictors for MMD progression included thinner subfoveal choroidal thickness, longer AL, worse best-corrected visual acuity, older age, female gender, and shallower anterior chamber depth. The eXtreme Gradient Boosting algorithm yielded the best discriminative performance (area under the receiver operating characteristic curve [AUROC] = 0.87 ± 0.02) with good calibration in the training cohort. In a less myopic external validation group (median -5.38 D), 48 patients (22.6%) developed MMD progression over 4 years, with the model's AUROC validated at 0.80 ± 0.008. Conclusions Machine learning model effectively predicts MMD progression a decade ahead using clinical and imaging indicators. This tool shows promise for identifying "at-risk" high myopes for timely intervention and vision protection.
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Affiliation(s)
- Yanping Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Shaopeng Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Riqian Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Ruilin Xiong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yueye Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Cong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Center for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Haikou, Hainan Province, China
- https://orcid.org/0000-0002-5273-3332
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24
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Rivero-Garcia I, Torres M, Sánchez-Cabo F. Deep generative models in single-cell omics. Comput Biol Med 2024; 176:108561. [PMID: 38749321 DOI: 10.1016/j.compbiomed.2024.108561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 04/30/2024] [Accepted: 05/05/2024] [Indexed: 05/31/2024]
Abstract
Deep Generative Models (DGMs) are becoming instrumental for inferring probability distributions inherent to complex processes, such as most questions in biomedical research. For many years, there was a lack of mathematical methods that would allow this inference in the scarce data scenario of biomedical research. The advent of single-cell omics has finally made square the so-called "skinny matrix", allowing to apply mathematical methods already extensively used in other areas. Moreover, it is now possible to integrate data at different molecular levels in thousands or even millions of samples, thanks to the number of single-cell atlases being collaboratively generated. Additionally, DGMs have proven useful in other frequent tasks in single-cell analysis pipelines, from dimensionality reduction, cell type annotation to RNA velocity inference. In spite of its promise, DGMs need to be used with caution in biomedical research, paying special attention to its use to answer the right questions and the definition of appropriate error metrics and validation check points that confirm not only its correct use but also its relevance. All in all, DGMs provide an exciting tool that opens a bright future for the integrative analysis of single-cell -omics to understand health and disease.
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Affiliation(s)
- Inés Rivero-Garcia
- Universidad Politécnica de Madrid, Madrid, 28040, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain
| | - Miguel Torres
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain
| | - Fátima Sánchez-Cabo
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain.
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25
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Zhu S, Yuan S, Niu R, Zhou Y, Wang Z, Xu G. RNAirport: a deep neural network-based database characterizing representative gene models in plants. J Genet Genomics 2024; 51:652-664. [PMID: 38518981 DOI: 10.1016/j.jgg.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 03/24/2024]
Abstract
A 5'-leader, known initially as the 5'-untranslated region, contains multiple isoforms due to alternative splicing (aS) and alternative transcription start site (aTSS). Therefore, a representative 5'-leader is demanded to examine the embedded RNA regulatory elements in controlling translation efficiency. Here, we develop a ranking algorithm and a deep-learning model to annotate representative 5'-leaders for five plant species. We rank the intra-sample and inter-sample frequency of aS-mediated transcript isoforms using the Kruskal-Wallis test-based algorithm and identify the representative aS-5'-leader. To further assign a representative 5'-end, we train the deep-learning model 5'leaderP to learn aTSS-mediated 5'-end distribution patterns from cap-analysis gene expression data. The model accurately predicts the 5'-end, confirmed experimentally in Arabidopsis and rice. The representative 5'-leader-contained gene models and 5'leaderP can be accessed at RNAirport (http://www.rnairport.com/leader5P/). The Stage 1 annotation of 5'-leader records 5'-leader diversity and will pave the way to Ribo-Seq open-reading frame annotation, identical to the project recently initiated by human GENCODE.
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Affiliation(s)
- Sitao Zhu
- State Key Laboratory of Hybrid Rice, Institute for Advanced Studies (IAS), Wuhan University, Wuhan, Hubei 430072, China
| | - Shu Yuan
- State Key Laboratory of Hybrid Rice, Institute for Advanced Studies (IAS), Wuhan University, Wuhan, Hubei 430072, China
| | - Ruixia Niu
- State Key Laboratory of Hybrid Rice, Institute for Advanced Studies (IAS), Wuhan University, Wuhan, Hubei 430072, China
| | - Yulu Zhou
- State Key Laboratory of Hybrid Rice, Institute for Advanced Studies (IAS), Wuhan University, Wuhan, Hubei 430072, China
| | - Zhao Wang
- State Key Laboratory of Hybrid Rice, Institute for Advanced Studies (IAS), Wuhan University, Wuhan, Hubei 430072, China
| | - Guoyong Xu
- State Key Laboratory of Hybrid Rice, Institute for Advanced Studies (IAS), Wuhan University, Wuhan, Hubei 430072, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China.
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Tan J, Yang R, Xiao L, Xia Y, Qin W. Personalized decision support system for tailoring IgA nephropathy treatment strategies. Eur J Intern Med 2024; 124:69-77. [PMID: 38443263 DOI: 10.1016/j.ejim.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/06/2024] [Accepted: 02/04/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND The ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies. METHODS Analyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80-20 split for training and testing purposes. RESULTS In the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies. CONCLUSION Our system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients.
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Affiliation(s)
- Jiaxing Tan
- Division of Nephrology, Department of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Rongxin Yang
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Liyin Xiao
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Yuanlin Xia
- School of Mechanical Engineering, Sichuan University College of Computer Science, Sichuan University, Chengdu, China
| | - Wei Qin
- Division of Nephrology, Department of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Mirzaei A, Hiller BC, Stelzer IA, Thiele K, Tan Y, Becker M. Computational Approaches for Connecting Maternal Stress to Preterm Birth. Clin Perinatol 2024; 51:345-360. [PMID: 38705645 DOI: 10.1016/j.clp.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Multiple studies have hinted at a complex connection between maternal stress and preterm birth (PTB). This article describes the potential of computational methods to provide new insights into this relationship. For this, we outline existing approaches for stress assessments and various data modalities available for profiling stress responses, and review studies that sought either to establish a connection between stress and PTB or to predict PTB based on stress-related factors. Finally, we summarize the challenges of computational methods, highlighting potential future research directions within this field.
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Affiliation(s)
- Amin Mirzaei
- Department of Computer Science and Electrical Engineering, Institute for Visual and Analytic Computing, Universität Rostock, Albert-Einstein-Straße 22, 18059 Rostock, Germany
| | - Bjarne C Hiller
- Department of Computer Science and Electrical Engineering, Institute for Visual and Analytic Computing, Universität Rostock, Albert-Einstein-Straße 22, 18059 Rostock, Germany
| | - Ina A Stelzer
- Department of Pathology, University of California San Diego, GPL/CMM-West, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Kristin Thiele
- Division for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf, Center for Obstetrics and Pediatrics, Martinistrasse 52, 20246 Hamburg, Germany
| | - Yuqi Tan
- Department of Microbiology and Immunology, Stanford University School of Medicine, CSSR3220, 269 Campus Drive, Stanford, CA 94305, USA
| | - Martin Becker
- Department of Computer Science and Electrical Engineering, Institute for Visual and Analytic Computing, Universität Rostock, Albert-Einstein-Straße 22, 18059 Rostock, Germany.
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Shroff H, Testa I, Jug F, Manley S. Live-cell imaging powered by computation. Nat Rev Mol Cell Biol 2024; 25:443-463. [PMID: 38378991 DOI: 10.1038/s41580-024-00702-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2024] [Indexed: 02/22/2024]
Abstract
The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.
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Affiliation(s)
- Hari Shroff
- Janelia Research Campus, Howard Hughes Medical Institute (HHMI), Ashburn, VA, USA
| | - Ilaria Testa
- Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Florian Jug
- Fondazione Human Technopole (HT), Milan, Italy
| | - Suliana Manley
- Institute of Physics, School of Basic Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
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Xu W, Rong Z, Ma W, Zhu B, Li N, Huang J, Liu Z, Yu Y, Zhang F, Zhang X, Ge M, Hou Y. Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network. Comput Biol Med 2024; 176:108530. [PMID: 38749324 DOI: 10.1016/j.compbiomed.2024.108530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 04/14/2024] [Accepted: 04/28/2024] [Indexed: 05/31/2024]
Abstract
As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown pathogenesis. This is due to their similar clinical presentations and imaging manifestations. That misdiagnosis can significantly increase the occurrence of adverse events. Delayed or incorrect treatment is one of the most important causes of MS progression. Therefore, the development of a practical diagnostic imaging aid could significantly reduce the risk of misdiagnosis and improve patient prognosis. We propose an interpretable deep learning (DL) model that differentiates MS and cSVD using T2-weighted fluid-attenuated inversion recovery (FLAIR) images. Transfer learning (TL) was utilized to extract features from the ImageNet dataset. This pioneering model marks the first of its kind in neuroimaging, showing great potential in enhancing differential diagnostic capabilities within the field of neurological disorders. Our model extracts the texture features of the images and achieves more robust feature learning through two attention modules. The attention maps provided by the attention modules provide model interpretation to validate model learning and reveal more information to physicians. Finally, the proposed model is trained end-to-end using focal loss to reduce the influence of class imbalance. The model was validated using clinically diagnosed MS (n=112) and cSVD (n=321) patients from the Beijing Tiantan Hospital. The performance of the proposed model was better than that of two commonly used DL approaches, with a mean balanced accuracy of 86.06 % and a mean area under the receiver operating characteristic curve of 98.78 %. Moreover, the generated attention heat maps showed that the proposed model could focus on the lesion signatures in the image. The proposed model provides a practical diagnostic imaging aid for the use of routinely available imaging techniques such as magnetic resonance imaging to classify MS and cSVD by linking DL to human brain disease. We anticipate a substantial improvement in accurately distinguishing between various neurological conditions through this novel model.
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Affiliation(s)
- Wangshu Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Zhiwei Rong
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Wenping Ma
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Bin Zhu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Na Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Jiansong Huang
- Peking University Health Science Center, Beijing, 100191, China
| | - Zhilin Liu
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Yipei Yu
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Fa Zhang
- The School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Xinghu Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China.
| | - Ming Ge
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
| | - Yan Hou
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China; Peking University Clinical Research Center, Beijing, 100191, China.
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Yang T, Liu D, Zhang Z, Sa R, Guan F. Predicting T-Cell Lymphoma in Children From 18F-FDG PET-CT Imaging With Multiple Machine Learning Models. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:952-964. [PMID: 38321311 PMCID: PMC11169166 DOI: 10.1007/s10278-024-01007-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 02/08/2024]
Abstract
This study aimed to examine the feasibility of utilizing radiomics models derived from 18F-FDG PET/CT imaging to screen for T-cell lymphoma in children with lymphoma. All patients had undergone 18F-FDG PET/CT scans. Lesions were extracted from PET/CT and randomly divided into training and validation sets. Two different types of models were constructed as follows: features that are extracted from standardized uptake values (SUV)-associated parameters, and CT images were used to build SUV/CT-based model. Features that are derived from PET and CT images were used to build PET/CT-based model. Logistic regression (LR), linear support vector machine, support vector machine with the radial basis function kernel, neural networks, and adaptive boosting were performed as classifiers in each model. In the training sets, 77 patients, and 247 lesions were selected for building the models. In the validation sets, PET/CT-based model demonstrated better performance than that of SUV/CT-based model in the prediction of T-cell lymphoma. LR showed highest accuracy with 0.779 [0.697, 0.860], area under the receiver operating characteristic curve (AUC) with 0.863 [0.762, 0.963], and preferable goodness-of-fit in PET/CT-based model at the patient level. LR also showed best performance with accuracy of 0.838 [0.741, 0.936], AUC of 0.907 [0.839, 0.976], and preferable goodness-of-fit in PET/CT-based model at the lesion level. 18F-FDG PET/CT-based radiomics models with different machine learning classifiers were able to screen T-cell lymphoma in children with high accuracy, AUC, and preferable goodness-of-fit, providing incremental value compared with SUV-associated features.
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Affiliation(s)
- Taiyu Yang
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China
| | - Danyan Liu
- Department of Radiology, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China
| | - Zexu Zhang
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China
| | - Ri Sa
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China.
| | - Feng Guan
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China.
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Wang H, Wang Q, He Q, Li S, Zhao Y, Zuo Y. Current perioperative nociception monitoring and potential directions. Asian J Surg 2024; 47:2558-2565. [PMID: 38548545 DOI: 10.1016/j.asjsur.2024.03.090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/04/2024] [Accepted: 03/06/2024] [Indexed: 06/05/2024] Open
Abstract
Perioperative nociception-antinociception balance is essential for the prevention of adverse postoperative events. Estimating the nociception level helps optimize intraoperative management. In the past two decades, various nociception monitoring devices have been developed for the identification of intraoperative nociception. However, each type of nociception monitoring device has advantages and disadvantages, limiting their clinical application in particular patients and settings. Therefore, this review aimed to summarize the information on nociceptor monitoring in current clinical settings, explore each technique's particularities, and possible future directions to provide a reference for clinicians and researchers.
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Affiliation(s)
- Haiyan Wang
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, China
| | - Qifeng Wang
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, China
| | - Qinqin He
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, China
| | - Shikuo Li
- Department of Anesthesiology, Yan'an Hospital of Kunming City, Kunming Medical University, Kunming, Yunnan, China
| | - Yuyi Zhao
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yunxia Zuo
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, China.
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32
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Cui Y, Zhou Y, Liu C, Mao Z, Zhou F. Interpretable machine learning models for predicting the incidence of antibiotic- associated diarrhea in elderly ICU patients. BMC Geriatr 2024; 24:458. [PMID: 38789951 PMCID: PMC11127392 DOI: 10.1186/s12877-024-05028-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 04/30/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Antibiotic-associated diarrhea (AAD) can prolong hospitalization, increase medical costs, and even lead to higher mortality rates. Therefore, it is essential to predict the incidence of AAD in elderly intensive care unit(ICU) patients. The objective of this study was to create a prediction model that is both interpretable and generalizable for predicting the incidence of AAD in elderly ICU patients. METHODS We retrospectively analyzed data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH) in China. We utilized the machine learning model Extreme Gradient Boosting (XGBoost) and Shapley's additive interpretation method to predict the incidence of AAD in elderly ICU patients in an interpretable manner. RESULTS A total of 848 adult ICU patients were eligible for this study. The XGBoost model predicted the incidence of AAD with an area under the receiver operating characteristic curve (ROC) of 0.917, sensitivity of 0.889, specificity of 0.806, accuracy of 0.870, and an F1 score of 0.780. The XGBoost model outperformed the other models, including logistic regression, support vector machine (AUC = 0.809), K-nearest neighbor algorithm (AUC = 0.872), and plain Bayes (AUC = 0.774). CONCLUSIONS While the XGBoost model may not excel in absolute performance, it demonstrates superior predictive capabilities compared to other models in forecasting the incidence of AAD in elderly ICU patients categorized based on their characteristics.
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Affiliation(s)
- Yating Cui
- Medical School of Chinese PLA, Beijing, 100853, China
- Department of Critical Care Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yibo Zhou
- Medical School of Chinese PLA, Beijing, 100853, China
- Department of Critical Care Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Chao Liu
- Medical School of Chinese PLA, Beijing, 100853, China
- Department of Critical Care Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China.
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Wang Q, Tang X, Qiao W, Sun L, Shi H, Chen D, Xu B, Liu Y, Zhao J, Huang C, Jin R. Machine learning-based characterization of the gut microbiome associated with the progression of primary biliary cholangitis to cirrhosis. Microbes Infect 2024:105368. [PMID: 38797428 DOI: 10.1016/j.micinf.2024.105368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 04/20/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Primary biliary cholangitis (PBC) is associated closely with the gut microbiota. This study aimed to explore the characteristics of the gut microbiota after the progress of PBC to cirrhosis. METHOD This study focuses on utilizing the 16S rRNA gene sequencing method to screen for differences in gut microbiota in PBC patients who progress to cirrhosis. Then, we divided the data into training and verification sets and used seven different machine learning (ML) models to validate them respectively, calculating and comparing the accuracy, F1 score, precision, and recall, and screening the dominant intestinal flora affecting PBC cirrhosis. RESULT PBC cirrhosis patients showed decreased diversity and richness of gut microbiota. Additionally, there are alterations in the composition of gut microbiota in PBC cirrhosis patients. The abundance of Faecalibacterium and Gemmiger bacteria significantly decreases, while the abundance of Veillonella and Streptococcus significantly increases. Furthermore, machine learning methods identify Streptococcus and Gemmiger as the predominant gut microbiota in PBC patients with cirrhosis, serving as non-invasive biomarkers (AUC = 0.902). CONCLUSION Our study revealed that PBC cirrhosis patients gut microbiota composition and function have significantly changed. Streptococcus and Gemmiger may become a non-invasive biomarker for predicting the progression of PBC progress to cirrhosis.
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Affiliation(s)
- Qi Wang
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, PR China; Beijing Institute of Infectious Diseases, Beijing, PR China; National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, PR China
| | - Xiaomeng Tang
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, PR China; Beijing Institute of Infectious Diseases, Beijing, PR China; National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, PR China
| | - Wenying Qiao
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, PR China; Beijing Institute of Infectious Diseases, Beijing, PR China; National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, PR China; Changping Laboratory, Beijing, PR China
| | - Lina Sun
- Beijing Institute of Hepatology, Beijing You 'an Hospital, Capital Medical University, Beijing 100069, PR China
| | - Han Shi
- Beijing Institute of Hepatology, Beijing You 'an Hospital, Capital Medical University, Beijing 100069, PR China
| | - Dexi Chen
- Beijing Institute of Hepatology, Beijing You 'an Hospital, Capital Medical University, Beijing 100069, PR China
| | - Bin Xu
- Second Department of Liver Disease Center, Beijing You 'an Hospital, Capital Medical University, Beijing 100069, PR China
| | - Yanmin Liu
- Second Department of Liver Disease Center, Beijing You 'an Hospital, Capital Medical University, Beijing 100069, PR China
| | - Juan Zhao
- Second Department of Liver Disease Center, Beijing You 'an Hospital, Capital Medical University, Beijing 100069, PR China
| | - Chunyang Huang
- Second Department of Liver Disease Center, Beijing You 'an Hospital, Capital Medical University, Beijing 100069, PR China.
| | - Ronghua Jin
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, PR China; Beijing Institute of Infectious Diseases, Beijing, PR China; National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, PR China; Changping Laboratory, Beijing, PR China.
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Xu J, Xu X, Huang D, Luo Y, Lin L, Bai X, Zheng Y, Yang Q, Cheng Y, Huang A, Shi J, Bo X, Gu J, Chen H. A comprehensive benchmarking with interpretation and operational guidance for the hierarchy of topologically associating domains. Nat Commun 2024; 15:4376. [PMID: 38782890 PMCID: PMC11116433 DOI: 10.1038/s41467-024-48593-7] [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: 09/18/2023] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Topologically associating domains (TADs), megabase-scale features of chromatin spatial architecture, are organized in a domain-within-domain TAD hierarchy. Within TADs, the inner and smaller subTADs not only manifest cell-to-cell variability, but also precisely regulate transcription and differentiation. Although over 20 TAD callers are able to detect TAD, their usability in biomedicine is confined by a disagreement of outputs and a limit in understanding TAD hierarchy. We compare 13 computational tools across various conditions and develop a metric to evaluate the similarity of TAD hierarchy. Although outputs of TAD hierarchy at each level vary among callers, data resolutions, sequencing depths, and matrices normalization, they are more consistent when they have a higher similarity of larger TADs. We present comprehensive benchmarking of TAD hierarchy callers and operational guidance to researchers of life science researchers. Moreover, by simulating the mixing of different types of cells, we confirm that TAD hierarchy is generated not simply from stacking Hi-C heatmaps of heterogeneous cells. Finally, we propose an air conditioner model to decipher the role of TAD hierarchy in transcription.
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Affiliation(s)
- Jingxuan Xu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Xiang Xu
- Academy of Military Medical Science, Beijing, 100850, China
| | - Dandan Huang
- Department of Oncology, Peking University Shougang Hospital, Beijing, China
- Center for Precision Diagnosis and Treatment of Colorectal Cancer and Inflammatory Diseases, Peking University Health Science Center, Beijing, China
| | - Yawen Luo
- Academy of Military Medical Science, Beijing, 100850, China
| | - Lin Lin
- Academy of Military Medical Science, Beijing, 100850, China
- School of Computer Science and Information Technology& KLAS, Northeast Normal University, Changchun, China
| | - Xuemei Bai
- Academy of Military Medical Science, Beijing, 100850, China
| | - Yang Zheng
- Academy of Military Medical Science, Beijing, 100850, China
| | - Qian Yang
- Academy of Military Medical Science, Beijing, 100850, China
| | - Yu Cheng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - An Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Jingyi Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Xiaochen Bo
- Academy of Military Medical Science, Beijing, 100850, China.
| | - Jin Gu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
- Department of Oncology, Peking University Shougang Hospital, Beijing, China.
- Center for Precision Diagnosis and Treatment of Colorectal Cancer and Inflammatory Diseases, Peking University Health Science Center, Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
- Peking University International Cancer Institute, Beijing, China.
| | - Hebing Chen
- Academy of Military Medical Science, Beijing, 100850, China.
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Yaseen I, Rather RA. A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery. Int J Womens Health 2024; 16:903-915. [PMID: 38800118 PMCID: PMC11128252 DOI: 10.2147/ijwh.s454127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.
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Affiliation(s)
- Ishfaq Yaseen
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Riyaz Ahmad Rather
- Department of Biotechnology, College of Natural and Computational Science, Wachemo University, Hossana, Ethiopia
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Wang L, Cheng Y, Meftaul IM, Luo F, Kabir MA, Doyle R, Lin Z, Naidu R. Advancing Soil Health: Challenges and Opportunities in Integrating Digital Imaging, Spectroscopy, and Machine Learning for Bioindicator Analysis. Anal Chem 2024; 96:8109-8123. [PMID: 38490962 DOI: 10.1021/acs.analchem.3c05311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Affiliation(s)
- Liang Wang
- Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, New South Wales 2308, Australia
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
| | - Ying Cheng
- Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, New South Wales 2308, Australia
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
| | - Islam Md Meftaul
- Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, New South Wales 2308, Australia
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
| | - Fang Luo
- Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety, Fuzhou University, Fuzhou, Fjian 350108, China
| | - Muhammad Ashad Kabir
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
- School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, New South Wales 2795, Australia
| | - Richard Doyle
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
- Tasmanian Institute of Agriculture (TIA), University of Tasmania, Launceston, Tasmania 7250, Australia
| | - Zhenyu Lin
- Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety, Fuzhou University, Fuzhou, Fjian 350108, China
| | - Ravi Naidu
- Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, New South Wales 2308, Australia
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
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Feng Y, Feng Y, Hu M, Xu H, Wang Z, Xu S, Yan Y, Feng C, Li Z, Feng G, Shang W. Early prediction of growth patterns after pediatric kidney transplantation based on height-related single-nucleotide polymorphisms. Chin Med J (Engl) 2024; 137:1199-1206. [PMID: 37672508 PMCID: PMC11101222 DOI: 10.1097/cm9.0000000000002828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Growth retardation is a common complication of chronic kidney disease in children, which can be partially relieved after renal transplantation. This study aimed to develop and validate a predictive model for growth patterns of children with end-stage renal disease (ESRD) after kidney transplantation using machine learning algorithms based on genomic and clinical variables. METHODS A retrospective cohort of 110 children who received kidney transplants between May 2013 and September 2021 at the First Affiliated Hospital of Zhengzhou University were recruited for whole-exome sequencing (WES), and another 39 children who underwent transplant from October 2021 to March 2022 were enrolled for external validation. Based on previous studies, we comprehensively collected 729 height-related single-nucleotide polymorphisms (SNPs) in exon regions. Seven machine learning algorithms and 10-fold cross-validation analysis were employed for model construction. RESULTS The 110 children were divided into two groups according to change in height-for-age Z -score. After univariate analysis, age and 19 SNPs were incorporated into the model and validated. The random forest model showed the best prediction efficacy with an accuracy of 0.8125 and an area under curve (AUC) of 0.924, and also performed well in the external validation cohort (accuracy, 0.7949; AUC, 0.796). CONCLUSIONS A model with good performance for predicting post-transplant growth patterns in children based on SNPs and clinical variables was constructed and validated using machine learning algorithms. The model is expected to guide clinicians in the management of children after renal transplantation, including the use of growth hormone, glucocorticoid withdrawal, and nutritional supplementation, to alleviate growth retardation in children with ESRD.
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Affiliation(s)
- Yi Feng
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yonghua Feng
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Mingyao Hu
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Hongen Xu
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Zhigang Wang
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Shicheng Xu
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yongchuang Yan
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Chenghao Feng
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Zhou Li
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Guiwen Feng
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Wenjun Shang
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
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Deshmukh A, Chang K, Cuala J, Vanslembrouck B, Georgia S, Loconte V, White KL. Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography. Cells 2024; 13:869. [PMID: 38786091 PMCID: PMC11119489 DOI: 10.3390/cells13100869] [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: 04/26/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.
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Affiliation(s)
- Aneesh Deshmukh
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
| | - Kevin Chang
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
| | - Janielle Cuala
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
- Medical Biophysics Program, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Bieke Vanslembrouck
- Department of Anatomy, School of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Senta Georgia
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Valentina Loconte
- Department of Anatomy, School of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Kate L. White
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Wossnig L, Furtmann N, Buchanan A, Kumar S, Greiff V. Best practices for machine learning in antibody discovery and development. Drug Discov Today 2024; 29:104025. [PMID: 38762089 DOI: 10.1016/j.drudis.2024.104025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.
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Affiliation(s)
- Leonard Wossnig
- LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
| | - Norbert Furtmann
- R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany
| | - Andrew Buchanan
- Biologics Engineering, R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Sandeep Kumar
- Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, USA
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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Yang XL, Zeng Z, Wang C, Wang GY, Zhang FQ. Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms. Immunol Res 2024:10.1007/s12026-024-09492-7. [PMID: 38755433 DOI: 10.1007/s12026-024-09492-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 05/09/2024] [Indexed: 05/18/2024]
Abstract
This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.
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Affiliation(s)
- Xi-Lin Yang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Zheng Zeng
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Chen Wang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Guang-Yu Wang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Fu-Quan Zhang
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
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Liu K, Geng S, Shen P, Zhao L, Zhou P, Liu W. Development and application of a machine learning-based predictive model for obstructive sleep apnea screening. Front Big Data 2024; 7:1353469. [PMID: 38817683 PMCID: PMC11137315 DOI: 10.3389/fdata.2024.1353469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/29/2024] [Indexed: 06/01/2024] Open
Abstract
Objective To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention. Methods We conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance. Results Among the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal. Conclusion Machine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.
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Affiliation(s)
- Kang Liu
- Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Shi Geng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Ping Shen
- Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Lei Zhao
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Peng Zhou
- Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Wen Liu
- Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
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Cao S, Hu Y. Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index. Nutr Metab (Lond) 2024; 21:25. [PMID: 38745171 PMCID: PMC11092237 DOI: 10.1186/s12986-024-00802-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 05/01/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout. METHODS Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005-2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model. RESULTS An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798-0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model. CONCLUSION The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.
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Affiliation(s)
- Shunshun Cao
- Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yangyang Hu
- Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
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Shi Y, Fu Z, Wu S, Yu X. Publication Trends and Research Hotspots of the Cardiorenal Syndrome: A Bibliometrics and Visual Analysis from 2003 to 2023. Cardiorenal Med 2024; 14:307-319. [PMID: 38740015 DOI: 10.1159/000539306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 04/26/2024] [Indexed: 05/16/2024] Open
Abstract
INTRODUCTION Cardiorenal syndrome encompasses a range of disorders involving both the heart and kidneys, wherein dysfunction in one organ may induce dysfunction in the other, either acutely or chronically. METHODS This study conducted a literature search on cardiorenal syndrome from January 1, 2003, to September 8, 2023. Meanwhile, a quantitative analysis of the developmental trajectory, research hotspots and evolutionary trends in the field of cardiorenal syndrome through bibliometric analysis and knowledge mapping was carried out. RESULTS The annual publication trend analysis revealed a consistent annual increase in cardiorenal syndrome literature over the last 20 years. The IL6, REN, and INS genes were identified as the current research hotspots. CONCLUSION The field of cardiorenal syndrome exhibits promising potential to grow and is emerging as a prominent research area. Future endeavours should prioritise a comprehensive understanding of the field and foster multi-centre co-operation among different countries and regions.
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Affiliation(s)
- Yibo Shi
- Department of Urology Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zean Fu
- Clinical School of Cardiovascular Disease, Tianjin Medical University, Tianjin, China
| | - Shixiong Wu
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinyi Yu
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Yan L, Li Z, Li C, Chen J, Zhou X, Cui J, Liu P, Shen C, Chen C, Hong H, Xu G, Cui Z. Hspb1 and Lgals3 in spinal neurons are closely associated with autophagy following excitotoxicity based on machine learning algorithms. PLoS One 2024; 19:e0303235. [PMID: 38728287 PMCID: PMC11086895 DOI: 10.1371/journal.pone.0303235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 04/23/2024] [Indexed: 05/12/2024] Open
Abstract
Excitotoxicity represents the primary cause of neuronal death following spinal cord injury (SCI). While autophagy plays a critical and intricate role in SCI, the specific mechanism underlying the relationship between excitotoxicity and autophagy in SCI has been largely overlooked. In this study, we isolated primary spinal cord neurons from neonatal rats and induced excitotoxic neuronal injury by high concentrations of glutamic acid, mimicking an excitotoxic injury model. Subsequently, we performed transcriptome sequencing. Leveraging machine learning algorithms, including weighted correlation network analysis (WGCNA), random forest analysis (RF), and least absolute shrinkage and selection operator analysis (LASSO), we conducted a comprehensive investigation into key genes associated with spinal cord neuron injury. We also utilized protein-protein interaction network (PPI) analysis to identify pivotal proteins regulating key gene expression and analyzed key genes from public datasets (GSE2599, GSE20907, GSE45006, and GSE174549). Our findings revealed that six genes-Anxa2, S100a10, Ccng1, Timp1, Hspb1, and Lgals3-were significantly upregulated not only in vitro in neurons subjected to excitotoxic injury but also in rats with subacute SCI. Furthermore, Hspb1 and Lgals3 were closely linked to neuronal autophagy induced by excitotoxicity. Our findings contribute to a better understanding of excitotoxicity and autophagy, offering potential targets and a theoretical foundation for SCI diagnosis and treatment.
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Affiliation(s)
- Lei Yan
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Zihao Li
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Chuanbo Li
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Jingyu Chen
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Xun Zhou
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Jiaming Cui
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Peng Liu
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Chong Shen
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Chu Chen
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Hongxiang Hong
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Guanhua Xu
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Zhiming Cui
- The First People’s Hospital of Nantong, Research Institute for Spine and Spinal Cord Disease of Nantong University, The Second Affiliated Hospital of Nantong University, Nantong, China
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Yu H, Lin J, Yuan J, Sun X, Wang C, Bai B. Screening mitochondria-related biomarkers in skin and plasma of atopic dermatitis patients by bioinformatics analysis and machine learning. Front Immunol 2024; 15:1367602. [PMID: 38774875 PMCID: PMC11106410 DOI: 10.3389/fimmu.2024.1367602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/22/2024] [Indexed: 05/24/2024] Open
Abstract
Background There is a significant imbalance of mitochondrial activity and oxidative stress (OS) status in patients with atopic dermatitis (AD). This study aims to screen skin and peripheral mitochondria-related biomarkers, providing insights into the underlying mechanisms of mitochondrial dysfunction in AD. Methods Public data were obtained from MitoCarta 3.0 and GEO database. We screened mitochondria-related differentially expressed genes (MitoDEGs) using R language and then performed GO and KEGG pathway analysis on MitoDEGs. PPI and machine learning algorithms were also used to select hub MitoDEGs. Meanwhile, the expression of hub MitoDEGs in clinical samples were verified. Using ROC curve analysis, the diagnostic performance of risk model constructed from these hub MitoDEGs was evaluated in the training and validation sets. Further computer-aided algorithm analyses included gene set enrichment analysis (GSEA), immune infiltration and mitochondrial metabolism, centered on these hub MitoDEGs. We also used real-time PCR and Spearman method to evaluate the relationship between plasma circulating cell-free mitochondrial DNA (ccf-mtDNA) levels and disease severity in AD patients. Results MitoDEGs in AD were significantly enriched in pathways involved in mitochondrial respiration, mitochondrial metabolism, and mitochondrial membrane transport. Four hub genes (BAX, IDH3A, MRPS6, and GPT2) were selected to take part in the creation of a novel mitochondrial-based risk model for AD prediction. The risk score demonstrated excellent diagnostic performance in both the training cohort (AUC = 1.000) and the validation cohort (AUC = 0.810). Four hub MitoDEGs were also clearly associated with the innate immune cells' infiltration and the molecular modifications of mitochondrial hypermetabolism in AD. We further discovered that AD patients had considerably greater plasma ccf-mtDNA levels than controls (U = 92.0, p< 0.001). Besides, there was a significant relationship between the up-regulation of plasma mtDNA and the severity of AD symptoms. Conclusions The study highlights BAX, IDH3A, MRPS6 and GPT2 as crucial MitoDEGs and demonstrates their efficiency in identifying AD. Moderate to severe AD is associated with increased markers of mitochondrial damage and cellular stress (ccf=mtDNA). Our study provides data support for the variation in mitochondria-related functional characteristics of AD patients.
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Affiliation(s)
| | | | | | | | | | - Bingxue Bai
- Department of Dermatology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
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Miljković N, Milenić N, Popović NB, Sodnik J. Data augmentation for generating synthetic electrogastrogram time series. Med Biol Eng Comput 2024:10.1007/s11517-024-03112-0. [PMID: 38705957 DOI: 10.1007/s11517-024-03112-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/25/2024] [Indexed: 05/07/2024]
Abstract
To address an emerging need for large number of diverse datasets for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram time series. We used electrogastrography (EGG) data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG time series alterations caused by the simulator sickness. Proposed data augmentation method generates synthetic EGG data with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in > 70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. The code for generation of synthetic EGG time series is not only freely available and can be further customized to assess signal processing algorithms but also may be used to increase data diversity for training artificial intelligence (AI) algorithms. The proposed approach is customized for EGG data synthesis but can be easily utilized for other biosignals with similar nature such as electroencephalogram.
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Affiliation(s)
- Nadica Miljković
- University of Belgrade-School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11000, Belgrade, Serbia.
- Faculty of Electrical Engineering, University of Ljubljana, Tržaška Cesta 25, 1000, Ljubljana, Slovenia.
| | - Nikola Milenić
- University of Belgrade-School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11000, Belgrade, Serbia
| | - Nenad B Popović
- University of Belgrade-School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11000, Belgrade, Serbia
| | - Jaka Sodnik
- Faculty of Electrical Engineering, University of Ljubljana, Tržaška Cesta 25, 1000, Ljubljana, Slovenia
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47
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Zhang Z, Lin J, Owens G, Chen Z. Deciphering silver nanoparticles perturbation effects and risks for soil enzymes worldwide: Insights from machine learning and soil property integration. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:134052. [PMID: 38493625 DOI: 10.1016/j.jhazmat.2024.134052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/15/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
Globally extensive research into how silver nanoparticles (AgNPs) affect enzyme activity in soils with differing properties has been limited by cost-prohibitive sampling. In this study, customized machine learning (ML) was used to extract data patterns from complex research, with a hit rate of Random Forest > Multiple Imputation by Chained Equations > Decision Tree > K-Nearest Neighbors. Results showed that soil properties played a pivotal role in determining AgNPs' effect on soil enzymes, with the order being pH > organic matter (OM) > soil texture ≈ cation exchange capacity (CEC). Notably, soil enzyme activity was more sensitive to AgNPs in acidic soil (pH < 5.5), while elevated OM content (>1.9 %) attenuated AgNPs toxicity. Compared to soil acidification, reducing soil OM content is more detrimental in exacerbating AgNPs' toxicity and it emerged that clay particles were deemed effective in curbing their toxicity. Meanwhile sand particles played a very different role, and a sandy soil sample at > 40 % of the water holding capacity (WHC), amplified the toxicity of AgNPs. Perturbation mapping of how soil texture alters enzyme activity under AgNPs exposure was generated, where soils with sand (45-65 %), silt (< 22 %), and clay (35-55 %) exhibited even higher probability of positive effects of AgNPs. The average calculation results indicate the sandy clay loam (75.6 %), clay (74.8 %), silt clay (65.8 %), and sandy clay (55.9 %) texture soil demonstrate less AgNPs inhibition effect. The results herein advance the prediction of the effect of AgNPs on soil enzymes globally and determine the soil types that are more sensitive to AgNPs worldwide.
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Affiliation(s)
- Zhenjun Zhang
- Fujian Key Laboratory of Pollution Control and Resource Reuse, College of Environmental and Resource Sciences, Fujian Normal University, Fuzhou 350117, Fujian Province, China
| | - Jiajiang Lin
- Fujian Key Laboratory of Pollution Control and Resource Reuse, College of Environmental and Resource Sciences, Fujian Normal University, Fuzhou 350117, Fujian Province, China.
| | - Gary Owens
- Environmental Contaminants Group, Future Industries Institute, University of South Australian, Mawson Lakes, SA 5095, Australia
| | - Zuliang Chen
- Fujian Key Laboratory of Pollution Control and Resource Reuse, College of Environmental and Resource Sciences, Fujian Normal University, Fuzhou 350117, Fujian Province, China.
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de Vrij N, Pollmann J, Rezende AM, Ibarra-Meneses AV, Pham TT, Hailemichael W, Kassa M, Bogale T, Melkamu R, Yeshanew A, Mohammed R, Diro E, Maes I, Domagalska MA, Landuyt H, Vogt F, van Henten S, Laukens K, Cuypers B, Meysman P, Beyene H, Sisay K, Kibret A, Mersha D, Ritmeijer K, van Griensven J, Adriaensen W. Persistent T cell unresponsiveness associated with chronic visceral leishmaniasis in HIV-coinfected patients. Commun Biol 2024; 7:524. [PMID: 38702419 PMCID: PMC11068874 DOI: 10.1038/s42003-024-06225-2] [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: 01/05/2023] [Accepted: 04/22/2024] [Indexed: 05/06/2024] Open
Abstract
A large proportion of HIV-coinfected visceral leishmaniasis (VL-HIV) patients exhibit chronic disease with frequent VL recurrence. However, knowledge on immunological determinants underlying the disease course is scarce. We longitudinally profiled the circulatory cellular immunity of an Ethiopian HIV cohort that included VL developers. We show that chronic VL-HIV patients exhibit high and persistent levels of TIGIT and PD-1 on CD8+/CD8- T cells, in addition to a lower frequency of IFN-γ+ TIGIT- CD8+/CD8- T cells, suggestive of impaired T cell functionality. At single T cell transcriptome and clonal resolution, the patients show CD4+ T cell anergy, characterised by a lack of T cell activation and lymphoproliferative response. These findings suggest that PD-1 and TIGIT play a pivotal role in VL-HIV chronicity, and may be further explored for patient risk stratification. Our findings provide a strong rationale for adjunctive immunotherapy for the treatment of chronic VL-HIV patients to break the recurrent disease cycle.
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Affiliation(s)
- Nicky de Vrij
- Clinical Immunology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, 2000, Antwerp, Belgium
- Adrem Data Lab, Department of Computer Science, University of Antwerp, 2020, Antwerp, Belgium
| | - Julia Pollmann
- Department of Medical Oncology, University Hospital Heidelberg, National Center for Tumor Diseases (NCT) Heidelberg, 69120, Heidelberg, Germany
| | - Antonio M Rezende
- Department of Microbiology, Aggeu Magalhães Institute-FIOCRUZ/PE, Recife, Brazil
| | - Ana V Ibarra-Meneses
- Département de Pathologie et Microbiologie, Faculté de Médecine Vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Thao-Thy Pham
- Clinical Immunology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, 2000, Antwerp, Belgium
| | - Wasihun Hailemichael
- Department of Immunology and Molecular Biology, Faculty of Biomedical Sciences, University of Gondar, Gondar, Ethiopia
| | - Mekibib Kassa
- Leishmaniasis Research and Treatment Centre, University of Gondar, Gondar, Ethiopia
| | - Tadfe Bogale
- Leishmaniasis Research and Treatment Centre, University of Gondar, Gondar, Ethiopia
| | - Roma Melkamu
- Leishmaniasis Research and Treatment Centre, University of Gondar, Gondar, Ethiopia
| | - Arega Yeshanew
- Leishmaniasis Research and Treatment Centre, University of Gondar, Gondar, Ethiopia
| | - Rezika Mohammed
- Leishmaniasis Research and Treatment Centre, University of Gondar, Gondar, Ethiopia
| | - Ermias Diro
- Leishmaniasis Research and Treatment Centre, University of Gondar, Gondar, Ethiopia
| | - Ilse Maes
- Molecular Parasitology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, 2000, Antwerp, Belgium
| | - Malgorzata A Domagalska
- Molecular Parasitology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, 2000, Antwerp, Belgium
| | - Hanne Landuyt
- Clinical Trial Unit, Department of Clinical Sciences, Institute of Tropical Medicine, 2000, Antwerp, Belgium
| | - Florian Vogt
- National Centre for Epidemiology and Population Health, The Australian National University, Canberra, 2601, Australia
- The Kirby Institute, University of New South Wales, Sydney, 2052, Australia
- Unit of Neglected Tropical Diseases, Department of Clinical Sciences, Institute of Tropical Medicine, 2000, Antwerp, Belgium
| | - Saskia van Henten
- Unit of Neglected Tropical Diseases, Department of Clinical Sciences, Institute of Tropical Medicine, 2000, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, 2020, Antwerp, Belgium
| | - Bart Cuypers
- Adrem Data Lab, Department of Computer Science, University of Antwerp, 2020, Antwerp, Belgium
| | - Pieter Meysman
- Adrem Data Lab, Department of Computer Science, University of Antwerp, 2020, Antwerp, Belgium
| | | | | | | | | | | | - Johan van Griensven
- Unit of Neglected Tropical Diseases, Department of Clinical Sciences, Institute of Tropical Medicine, 2000, Antwerp, Belgium
| | - Wim Adriaensen
- Clinical Immunology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, 2000, Antwerp, Belgium.
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49
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Gan Y, Yu J, Xu G, Yan C, Zou G. Inferring gene regulatory networks from single-cell transcriptomics based on graph embedding. Bioinformatics 2024; 40:btae291. [PMID: 38810116 PMCID: PMC11142726 DOI: 10.1093/bioinformatics/btae291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/06/2024] [Accepted: 05/28/2024] [Indexed: 05/31/2024] Open
Abstract
MOTIVATION Gene regulatory networks (GRNs) encode gene regulation in living organisms, and have become a critical tool to understand complex biological processes. However, due to the dynamic and complex nature of gene regulation, inferring GRNs from scRNA-seq data is still a challenging task. Existing computational methods usually focus on the close connections between genes, and ignore the global structure and distal regulatory relationships. RESULTS In this study, we develop a supervised deep learning framework, IGEGRNS, to infer GRNs from scRNA-seq data based on graph embedding. In the framework, contextual information of genes is captured by GraphSAGE, which aggregates gene features and neighborhood structures to generate low-dimensional embedding for genes. Then, the k most influential nodes in the whole graph are filtered through Top-k pooling. Finally, potential regulatory relationships between genes are predicted by stacking CNNs. Compared with nine competing supervised and unsupervised methods, our method achieves better performance on six time-series scRNA-seq datasets. AVAILABILITY AND IMPLEMENTATION Our method IGEGRNS is implemented in Python using the Pytorch machine learning library, and it is freely available at https://github.com/DHUDBlab/IGEGRNS.
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Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, Shanghai 201620, China
| | - Jiacheng Yu
- School of Computer Science and Technology, Donghua University, Shanghai 201620, China
| | - Guangwei Xu
- School of Computer Science and Technology, Donghua University, Shanghai 201620, China
| | - Cairong Yan
- School of Computer Science and Technology, Donghua University, Shanghai 201620, China
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
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50
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Lin Y, Chen X, Lin L, Xu B, Zhu X, Lin X. Sesamolin serves as an MYH14 inhibitor to sensitize endometrial cancer to chemotherapy and endocrine therapy via suppressing MYH9/GSK3β/β-catenin signaling. Cell Mol Biol Lett 2024; 29:63. [PMID: 38698330 PMCID: PMC11067147 DOI: 10.1186/s11658-024-00583-9] [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: 11/06/2023] [Accepted: 04/24/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Endometrial cancer (EC) is one of the most common gynecological cancers. Herein, we aimed to define the role of specific myosin family members in EC because this protein family is involved in the progression of various cancers. METHODS Bioinformatics analyses were performed to reveal EC patients' prognosis-associated genes in patients with EC. Furthermore, colony formation, immunofluorescence, cell counting kit 8, wound healing, and transwell assays as well as coimmunoprecipitation, cycloheximide chase, luciferase reporter, and cellular thermal shift assays were performed to functionally and mechanistically analyze human EC samples, cell lines, and a mouse model, respectively. RESULTS Machine learning techniques identified MYH14, a member of the myosin family, as the prognosis-associated gene in patients with EC. Furthermore, bioinformatics analyses based on public databases showed that MYH14 was associated with EC chemoresistance. Moreover, immunohistochemistry validated MYH14 upregulation in EC cases compared with that in normal controls and confirmed that MYH14 was an independent and unfavorable prognostic indicator of EC. MYH14 impaired cell sensitivity to carboplatin, paclitaxel, and progesterone, and increased cell proliferation and metastasis in EC. The mechanistic study showed that MYH14 interacted with MYH9 and impaired GSK3β-mediated β-catenin ubiquitination and degradation, thus facilitating the Wnt/β-catenin signaling pathway and epithelial-mesenchymal transition. Sesamolin, a natural compound extracted from Sesamum indicum (L.), directly targeted MYH14 and attenuated EC progression. Additionally, the compound disrupted the interplay between MYH14 and MYH9 and repressed MYH9-regulated Wnt/β-catenin signaling. The in vivo study further verified sesamolin as a therapeutic drug without side effects. CONCLUSIONS Herein, we identified that EC prognosis-associated MYH14 was independently responsible for poor overall survival time of patients, and it augmented EC progression by activating Wnt/β-catenin signaling. Targeting MYH14 by sesamolin, a cytotoxicity-based approach, can be applied synergistically with chemotherapy and endocrine therapy to eventually mitigate EC development. This study emphasizes MYH14 as a potential target and sesamolin as a valuable natural drug for EC therapy.
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Affiliation(s)
- Yibin Lin
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Xiao Chen
- Department of Intensive Care Unit, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, China
- Department of Intensive Care Unit, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China
| | - Linping Lin
- Hunan Institute of Engineering, Xiangtan, 411100, Hunan, China
| | - Benhua Xu
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Xinquan Road 29, Gulou District, Fuzhou, 350001, Fujian, China.
| | - Xiaofeng Zhu
- Department of Oral Maxillo-Facial Surgery, The First Affiliated Hospital, Fujian Medical University, No. 20 Chazhong Road, Taijing District, Fuzhou, 350005, Fujian, China.
- Department of Oral Maxillo-Facial Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
| | - Xian Lin
- Shenzhen Key Laboratory of Inflammatory and Immunology Diseases, No. 1120 Lianhua Road, Futian District, Shenzhen, 518036, Guangdong, China.
- Peking University Shenzhen Hospital, Shenzhen, 518036, Guangdong, China.
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