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Tan S, Lu X, Chen W, Pan B, Kong G, Wei L. Analysis and experimental validation of IL-17 pathway and key genes as central roles associated with inflammation in hepatic ischemia-reperfusion injury. Sci Rep 2024; 14:6423. [PMID: 38494504 PMCID: PMC10944831 DOI: 10.1038/s41598-024-57139-2] [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: 09/20/2023] [Accepted: 03/14/2024] [Indexed: 03/19/2024] Open
Abstract
Hepatic ischemia-reperfusion injury (HIRI) elicits an immune-inflammatory response that may result in hepatocyte necrosis and apoptosis, ultimately culminating in postoperative hepatic dysfunction and hepatic failure. The precise mechanisms governing the pathophysiology of HIRI remain incompletely understood, necessitating further investigation into key molecules and pathways implicated in disease progression to guide drug discovery and potential therapeutic interventions. Gene microarray data was downloaded from the GEO expression profile database. Integrated bioinformatic analyses were performed to identify HIRI signature genes, which were subsequently validated for expression levels and diagnostic efficacy. Finally, the gene expression was verified in an experimental HIRI model and the effect of anti-IL17A antibody intervention in three time points (including pre-ischemic, post-ischemic, and at 1 h of reperfusion) on HIRI and the expression of these genes was investigated. Bioinformatic analyses of the screened characterized genes revealed that inflammation, immune response, and cell death modulation were significantly associated with HIRI pathophysiology. CCL2, BTG2, GADD45A, FOS, CXCL10, TNFRSF12A, and IL-17 pathway were identified as key components involved in the HIRI. Serum and liver IL-17A expression were significantly upregulated during the initial phase of HIRI. Pretreatment with anti-IL-17A antibody effectively alleviated the damage of liver tissue, suppressed inflammatory factors, and serum transaminase levels, and downregulated the mRNA expression of CCL2, GADD45A, FOS, CXCL10, and TNFRSF12A. Injection of anti-IL17A antibody after ischemia and at 1 h of reperfusion failed to demonstrate anti-inflammatory and attenuating HIRI benefits relative to earlier intervention. Our study reveals that the IL-17 pathway and related genes may be involved in the proinflammatory mechanism of HIRI, which may provide a new perspective and theoretical basis for the prevention and treatment of HIRI.
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Affiliation(s)
- Siyou Tan
- Department of Anesthesiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Jiefang West Road NO. 61, Changsha, 410005, China
| | - Xiang Lu
- Department of Anesthesiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Jiefang West Road NO. 61, Changsha, 410005, China
| | - Wenyan Chen
- Department of Anesthesiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Jiefang West Road NO. 61, Changsha, 410005, China
| | - Bingbing Pan
- Department of Anesthesiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Jiefang West Road NO. 61, Changsha, 410005, China
| | - Gaoyin Kong
- Department of Anesthesiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Jiefang West Road NO. 61, Changsha, 410005, China
- Clinical Research Center for Anesthesiology of ERAS in Hunan Province, Changsha, China
| | - Lai Wei
- Department of Anesthesiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Jiefang West Road NO. 61, Changsha, 410005, China.
- Clinical Research Center for Anesthesiology of ERAS in Hunan Province, Changsha, China.
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Xia Q, Shen J, Wang Q, Chen R, Zheng X, Yan Q, Du L, Li H, Duan S. Cuproptosis-associated ncRNAs predict breast cancer subtypes. PLoS One 2024; 19:e0299138. [PMID: 38408075 PMCID: PMC10896520 DOI: 10.1371/journal.pone.0299138] [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: 07/03/2023] [Accepted: 02/05/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND Cuproptosis is a novel copper-dependent mode of cell death that has recently been discovered. The relationship between Cuproptosis-related ncRNAs and breast cancer subtypes, however, remains to be studied. METHODS The aim of this study was to construct a breast cancer subtype prediction model associated with Cuproptosis. This model could be used to determine the subtype of breast cancer patients. To achieve this aim, 21 Cuproptosis-related genes were obtained from published articles and correlation analysis was performed with ncRNAs differentially expressed in breast cancer. Random forest algorithms were subsequently utilized to select important ncRNAs and build breast cancer subtype prediction models. RESULTS A total of 94 ncRNAs significantly associated with Cuproptosis were obtained and the top five essential features were chosen to build a predictive model. These five biomarkers were differentially expressed in the five breast cancer subtypes and were closely associated with immune infiltration, RNA modification, and angiogenesis. CONCLUSION The random forest model constructed based on Cuproptosis-related ncRNAs was able to accurately predict breast cancer subtypes, providing a new direction for the study of clinical therapeutic targets.
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Affiliation(s)
- Qing Xia
- College of Pharmacy, Zhejiang University of Technology, Hangzhou, Zhejiang, China
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Jinze Shen
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Qurui Wang
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Ruixiu Chen
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Xinying Zheng
- College of Pharmacy, Zhejiang University of Technology, Hangzhou, Zhejiang, China
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Qibin Yan
- College of Pharmacy, Zhejiang University of Technology, Hangzhou, Zhejiang, China
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Lihua Du
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
| | - Hanbing Li
- College of Pharmacy, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Shiwei Duan
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang, China
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Zhang S, Zhang G, Wang W, Guo SB, Zhang P, Wang F, Zhou Q, Zhou Z, Wang Y, Sun H, Cui W, Yang S, Yuan W. An assessment system for clinical and biological interpretability in ulcerative colitis. Aging (Albany NY) 2024; 16:3856-3879. [PMID: 38372705 DOI: 10.18632/aging.205564] [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: 07/25/2023] [Accepted: 01/09/2024] [Indexed: 02/20/2024]
Abstract
Ulcerative colitis (UC) is a serious inflammatory bowel disease (IBD) with high morbidity and mortality worldwide. As the traditional diagnostic techniques have various limitations in the practice and diagnosis of early ulcerative colitis, it is necessary to develop new diagnostic models from molecular biology to supplement the existing methods. In this study, we developed a machine learning-based synthesis to construct an artificial intelligence diagnostic model for ulcerative colitis, and the correctness of the model is verified using an external independent dataset. According to the significantly expressed genes related to the occurrence of UC in the model, an unsupervised quantitative ulcerative colitis related score (UCRScore) based on principal coordinate analysis was established. The UCRScore is not only highly generalizable across UC bulk cohorts at different stages, but also highly generalizable across single-cell datasets, with the same effect in terms of cell numbers, activation pathways and mechanisms. As an important role of screening genes in disease occurrence, based on connectivity map analysis, 5 potential targeting molecular compounds were identified, which can be used as an additional supplement to the therapeutic of UC. Overall, this study provides a potential tool for differential diagnosis and assessment of bio-pathological changes in UC at the macroscopic level, providing an opportunity to optimize the diagnosis and treatment of UC.
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Affiliation(s)
- Shiqian Zhang
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou 450052, Henan, China
| | - Wenxiu Wang
- Department of Neonatology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Song-Bin Guo
- Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, Guangdong, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou 510060, Guangdong, China
| | - Pengpeng Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Fuqi Wang
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Quanbo Zhou
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Zhaokai Zhou
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Yujia Wang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou 450052, Henan, China
| | - Haifeng Sun
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Wenming Cui
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Shuaixi Yang
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Weitang Yuan
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
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Bian R, Huang S, Cao X, Qi W, Peng J, Liu H, Wu X, Li C, Qu J. Spatial and temporal distribution of the microbial community structure in the receiving rivers of the middle and lower reaches of the Yangtze River under the influence of different wastewater types. JOURNAL OF HAZARDOUS MATERIALS 2024; 462:132835. [PMID: 37879279 DOI: 10.1016/j.jhazmat.2023.132835] [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: 07/25/2023] [Revised: 10/01/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023]
Abstract
The gradual intensification of human activity has caused severe negative impacts on the ecosystems of the Yangtze River Basin. Treated effluents still affect the environment and health of receiving rivers, particularly in terms of microbial community structure. However, relatively few studies have been conducted on the differences in the effects of wastewater types on microbial community structure. Three sampling campaigns (237 samples) were conducted in the Nanjing and Wuhan sections of the Yangtze River Basin. Our results showed that the microbial community structure differed significantly among the water periods and could recover to its original state at > 500 m downstream of the outfall. The diversity of the receiving rivers under the influence of industrial wastewater was higher than that of the other wastewater types, although the number of taxa was lower than that of other wastewater types. Cyanobium_PCC-6307 and Rhodoferax were screened for biomarkers in samples affected by domestic and industrial wastewater, respectively. Although different kinds of wastewater influenced the microbial community structure, environmental factors, and geographical distance were still the main drivers. This study suggests that treated wastewater still poses a risk to ecosystems and highlights the importance of effective management strategies for assessing ecosystem health.
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Affiliation(s)
- Rui Bian
- Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; School of Environment, Northeast Normal University, Changchun 130117, China; Yangtze Eco-Environment Engineering Research Center, China Three Gorges Corporation, Beijing 100038, China
| | - Shier Huang
- Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Xiaofeng Cao
- Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Weixiao Qi
- Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
| | - Jianfeng Peng
- Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Huijuan Liu
- Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Xinghua Wu
- Yangtze Eco-Environment Engineering Research Center, China Three Gorges Corporation, Beijing 100038, China
| | - Chong Li
- Yangtze Eco-Environment Engineering Research Center, China Three Gorges Corporation, Beijing 100038, China
| | - Jiuhui Qu
- Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
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Wu Y, Xiao Q, Wang S, Xu H, Fang Y. Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques. J Inflamm Res 2023; 16:5667-5676. [PMID: 38050562 PMCID: PMC10693771 DOI: 10.2147/jir.s438838] [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: 09/05/2023] [Accepted: 11/10/2023] [Indexed: 12/06/2023] Open
Abstract
Background To identify novel gene combinations and to develop an early diagnostic model for Polycystic Ovary Syndrome (PCOS) through the integration of artificial neural networks (ANN) and random forest (RF) methods. Methods We retrieved and processed gene expression datasets for PCOS from the Gene Expression Omnibus (GEO) database. Differential expression analysis of genes (DEGs) within the training set was performed using the "limma" R package. Enrichment analyses on DEGs using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and immune cell infiltration. The identification of critical genes from DEGs was then performed using random forests, followed by the developing of new diagnostic models for PCOS using artificial neural networks. Results We identified 130 up-regulated genes and 132 down-regulated genes in PCOS compared to normal samples. Gene Ontology analysis revealed significant enrichment in myofibrils and highlighted crucial biological functions related to myofilament sliding, myofibril, and actin-binding. Compared with normal tissues, the types of immune cells expressed in PCOS samples are different. A random forest algorithm identified 10 significant genes proposed as potential PCOS-specific biomarkers. Using these genes, an artificial neural network diagnostic model accurately distinguished PCOS from normal samples. The diagnostic model underwent validation using the independent validation set, and the resulting area under the receiver operating characteristic curve (AUC) values was consistent with the anticipated outcomes. Conclusion Utilizing unique gene combinations, this research created a diagnostic model by merging random forest techniques with artificial neural networks. The AUC indicated a notably superior performance of the diagnostic model.
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Affiliation(s)
- Yumi Wu
- Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
| | - QiWei Xiao
- Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
| | - ShouDong Wang
- The Out-Patient Department of TCM of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
| | - Huanfang Xu
- Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
- Acupuncture and Moxibustion Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
| | - YiGong Fang
- Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
- Acupuncture and Moxibustion Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
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Pang W, Zhang B, Jin L, Yao Y, Han Q, Zheng X. Serological Biomarker-Based Machine Learning Models for Predicting the Relapse of Ulcerative Colitis. J Inflamm Res 2023; 16:3531-3545. [PMID: 37636275 PMCID: PMC10455884 DOI: 10.2147/jir.s423086] [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: 05/26/2023] [Accepted: 08/11/2023] [Indexed: 08/29/2023] Open
Abstract
Purpose To explore whether machine learning models using serological markers can predict the relapse of Ulcerative colitis (UC). Patients and Methods This clinical cohort study included 292 UC patients, and serological markers were obtained when patients were discharged from the hospital. Subsequently, four machine learning models including the random forest (RF) model, the logistic regression model, the decision tree, and the neural network were compared to predict the relapse of UC. A nomogram was constructed, and the performance of these models was evaluated by accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Results Based on the patients' characteristics and serological markers, we selected the relevant variables associated with relapse and developed a LR model. The novel model including gender, white blood cell count, percentage of leukomonocyte, percentage of monocyte, absolute value of neutrophilic granulocyte, and erythrocyte sedimentation rate was established for predicting the relapse. In addition, the average AUC of the four machine learning models was 0.828, of which the RF model was the best. The AUC of the test group was 0.889, the accuracy was 76.4%, the sensitivity was 78.5%, and the specificity was 76.4%. There were 45 variables in the RF models, and the relative weight coefficients of these variables were determined. Age has the greatest impact on classification results, followed by hemoglobin concentration, white blood cell count, and platelet distribution width. Conclusion Machine learning models based on serological markers had high accuracy in predicting the relapse of UC. The model can be used to noninvasively predict patient outcomes and can be an effective tool for determining personalized treatment plans.
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Affiliation(s)
- Wenwen Pang
- Department of Clinical Laboratory, Tianjin Union Medical Center, Nankai University, Tianjin, People’s Republic of China
| | - Bowei Zhang
- School of Medicine, Nankai University, Tianjin, People’s Republic of China
| | - Leixin Jin
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
| | - Yao Yao
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
| | - Qiurong Han
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
| | - Xiaoli Zheng
- Department of Clinical Laboratory, Tianjin Union Medical Center, Nankai University, Tianjin, People’s Republic of China
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Guo X, Cai L, Cao Y, Liu Z, Zhang J, Liu D, Jiang Z, Chen Y, Fu M, Xia Z, Yi G. New pattern of individualized management of chronic diseases: focusing on inflammatory bowel diseases and looking to the future. Front Med (Lausanne) 2023; 10:1186143. [PMID: 37265491 PMCID: PMC10231387 DOI: 10.3389/fmed.2023.1186143] [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: 03/14/2023] [Accepted: 04/17/2023] [Indexed: 06/03/2023] Open
Abstract
Non-infectious chronic diseases, especially inflammatory bowel diseases (IBDs), hypertension, and diabetes mellitus, are characterized by a prolonged and multisystemic course, and their incidence increases annually, usually causing serious economic burden and psychological stress for patients. Therefore, these diseases deserve scientific and consistent disease management. In addition, the lack of a comprehensive "early disease clues tracking-personalized treatment system-follow-up" model in hospitals also exacerbates this dilemma. Based on these facts, we propose an individualized prediction management system for IBDs based on chronic diseases, focusing on the established IBDs-related prediction models and summarizing their advantages and disadvantages. We call on researchers to pay attention to the integration of models with clinical practice and the continuous correction of models to achieve truly individualized medical treatment for chronic diseases, thus providing substantial value for the rapid diagnosis and adequate treatment of chronic diseases such as IBDs, which follow the "relapse-remission" disease model, and realizing long-term drug use and precise disease management for patients. The goal is to achieve a new level of chronic disease management by scientifically improving long-term medication, precise disease management, and individualized medical treatment, effectively prolonging the remission period and reducing morbidity and disability rates.
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Affiliation(s)
- Xi Guo
- Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- School of Rehabilitation Sciences, Southern Medical University, Guangzhou, Guangdong, China
- The Second Clinical School of Southern Medical University, Guangzhou, Guangdong, China
| | - Liyang Cai
- Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- The Second Clinical School of Southern Medical University, Guangzhou, Guangdong, China
| | - Yuchen Cao
- Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- The Second Clinical School of Southern Medical University, Guangzhou, Guangdong, China
- Plastic Surgery Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zining Liu
- The First Clinical School of Southern Medical University, Guangzhou, Guangdong, China
| | - Jiexin Zhang
- The Third Clinical School of Southern Medical University, Guangzhou, Guangdong, China
| | - Danni Liu
- Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- The Second Clinical School of Southern Medical University, Guangzhou, Guangdong, China
| | - Zhujun Jiang
- The Second Clinical Medical College, Tianjin Medical University, Tianjin, China
| | - Yanxia Chen
- Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Min Fu
- Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- The Second Clinical School of Southern Medical University, Guangzhou, Guangdong, China
| | - Zhaoxia Xia
- The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Guoguo Yi
- The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
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Yu S, Zhang M, Ye Z, Wang Y, Wang X, Chen YG. Development of a 32-gene signature using machine learning for accurate prediction of inflammatory bowel disease. CELL REGENERATION (LONDON, ENGLAND) 2023; 12:8. [PMID: 36600111 PMCID: PMC9813306 DOI: 10.1186/s13619-022-00143-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/09/2022] [Indexed: 01/06/2023]
Abstract
Inflammatory bowel disease (IBD) is a chronic inflammatory condition caused by multiple genetic and environmental factors. Numerous genes are implicated in the etiology of IBD, but the diagnosis of IBD is challenging. Here, XGBoost, a machine learning prediction model, has been used to distinguish IBD from healthy cases following elaborative feature selection. Using combined unsupervised clustering analysis and the XGBoost feature selection method, we successfully identified a 32-gene signature that can predict IBD occurrence in new cohorts with 0.8651 accuracy. The signature shows enrichment in neutrophil extracellular trap formation and cytokine signaling in the immune system. The probability threshold of the XGBoost-based classification model can be adjusted to fit personalized lifestyle and health status. Therefore, this study reveals potential IBD-related biomarkers that facilitate an effective personalized diagnosis of IBD.
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Affiliation(s)
- Shicheng Yu
- grid.9227.e0000000119573309Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, 190 Kaiyuan Avenue, Guangzhou Science Park, Luogang District, Guangzhou, 510530 China ,Guangzhou Laboratory, Guangzhou, 510700 China
| | - Mengxian Zhang
- grid.12527.330000 0001 0662 3178The State Key Laboratory of Membrane Biology, Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, 100084 China
| | - Zhaofeng Ye
- grid.12527.330000 0001 0662 3178School of Medicine, Tsinghua University, Beijing, 100084 China
| | - Yalong Wang
- grid.9227.e0000000119573309Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, 190 Kaiyuan Avenue, Guangzhou Science Park, Luogang District, Guangzhou, 510530 China ,Guangzhou Laboratory, Guangzhou, 510700 China
| | - Xu Wang
- Guangzhou Laboratory, Guangzhou, 510700 China
| | - Ye-Guang Chen
- Guangzhou Laboratory, Guangzhou, 510700 China ,grid.12527.330000 0001 0662 3178The State Key Laboratory of Membrane Biology, Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, 100084 China ,grid.260463.50000 0001 2182 8825School of Basic Medicine, Nanchang University, Nanchang, 330031 China
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Li H, Sun X, Li Z, Zhao R, Li M, Hu T. Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients. Front Cardiovasc Med 2023; 9:1059543. [PMID: 36684609 PMCID: PMC9846646 DOI: 10.3389/fcvm.2022.1059543] [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: 10/01/2022] [Accepted: 12/08/2022] [Indexed: 01/06/2023] Open
Abstract
Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.
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Affiliation(s)
- Hongyu Li
- Medical College of Soochow University, The People’s Liberation Army of China (PLA) Rocket Force Characteristic Medical Center, Beijing, China,Department of Cardiovascular Medicine, Baotou Central Hospital, Institute of Cardiovascular Diseases, Translational Medicine Center, Baotou, China
| | - Xinti Sun
- Department of Thoracic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Zesheng Li
- Key Laboratory of Post-Neuroinjury Neuro-Repair and Regeneration in Central Nervous System, Tianjin Medical University General Hospital, Tianjin, China
| | - Ruiping Zhao
- Department of Cardiovascular Medicine, Baotou Central Hospital, Institute of Cardiovascular Diseases, Translational Medicine Center, Baotou, China
| | - Meng Li
- Department of Cardiovascular Medicine, Baotou Central Hospital, Institute of Cardiovascular Diseases, Translational Medicine Center, Baotou, China,*Correspondence: Meng Li,
| | - Taohong Hu
- Medical College of Soochow University, The People’s Liberation Army of China (PLA) Rocket Force Characteristic Medical Center, Beijing, China,Taohong Hu,
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Li X, Yan L, Wang X, Ouyang C, Wang C, Chao J, Zhang J, Lian G. Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation. Front Med (Lausanne) 2022; 9:1043412. [PMID: 36619650 PMCID: PMC9810755 DOI: 10.3389/fmed.2022.1043412] [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: 09/15/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
Background Endoscopic disease activity monitoring is important for the long-term management of patients with ulcerative colitis (UC), there is currently no widely accepted non-invasive method that can effectively predict endoscopic disease activity. We aimed to develop and validate machine learning (ML) models for predicting it, which are desired to reduce the frequency of endoscopic examinations and related costs. Methods The patients with a diagnosis of UC in two hospitals from January 2016 to January 2021 were enrolled in this study. Thirty nine clinical and laboratory variables were collected. All patients were divided into four groups based on MES or UCEIS scores. Logistic regression (LR) and four ML algorithms were applied to construct the prediction models. The performance of models was evaluated in terms of accuracy, sensitivity, precision, F1 score, and area under the receiver-operating characteristic curve (AUC). Then Shapley additive explanations (SHAP) was applied to determine the importance of the selected variables and interpret the ML models. Results A total of 420 patients were entered into the study. Twenty four variables showed statistical differences among the groups. After synthetic minority oversampling technique (SMOTE) oversampling and RFE variables selection, the random forests (RF) model with 23 variables in MES and the extreme gradient boosting (XGBoost) model with 21 variables in USEIS, had the greatest discriminatory ability (AUC = 0.8192 in MES and 0.8006 in UCEIS in the test set). The results obtained from SHAP showed that albumin, rectal bleeding, and CRP/ALB contributed the most to the overall model. In addition, the above three variables had a more balanced contribution to each classification under the MES than the UCEIS according to the SHAP values. Conclusion This proof-of-concept study demonstrated that the ML model could serve as an effective non-invasive approach to predicting endoscopic disease activity for patients with UC. RF and XGBoost, which were first introduced into data-based endoscopic disease activity prediction, are suitable for the present prediction modeling.
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Affiliation(s)
- Xiaojun Li
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China
| | - Lamei Yan
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China,Department of Gastroenterology, The First Affiliated Hospital of Shaoyang College, Shaoyang, Hunan, China
| | - Xuehong Wang
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China
| | - Chunhui Ouyang
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China
| | - Chunlian Wang
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China
| | - Jun Chao
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China,Hunan Aicortech Intelligent Research Institute Co., Changsha, Hunan, China
| | - Jie Zhang
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China,*Correspondence: Jie Zhang,
| | - Guanghui Lian
- Department of Gastroenterology, Xiangya Hospital of Central South University, Changsha, Hunan, China,Guanghui Lian,
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Xiong T, Chen Y, Han S, Zhang TC, Pu L, Fan YX, Fan WC, Zhang YY, Li YX. Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks. Front Cardiovasc Med 2022; 9:913776. [PMID: 36531717 PMCID: PMC9751025 DOI: 10.3389/fcvm.2022.913776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 11/07/2022] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND Although advanced surgical and interventional treatments are available for advanced aortic valve calcification (AVC) with severe clinical symptoms, early diagnosis, and intervention is critical in order to reduce calcification progression and improve patient prognosis. The aim of this study was to develop therapeutic targets for improving outcomes for patients with AVC. MATERIALS AND METHODS We used the public expression profiles of individuals with AVC (GSE12644 and GSE51472) to identify potential diagnostic markers. First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. Subsequently, artificial neural networks were employed to filter and model the diagnostic characteristics for AVC incidence. The diagnostic values were determined using the receiver operating characteristic (ROC) curves. Furthermore, CIBERSORT immune infiltration analysis was used to determine the expression of different immune cells in the AVC. Finally, the CMap database was used to predict candidate small compounds as prospective AVC therapeutics. RESULTS A total of 78 strong DEGs were identified. The leukocyte migration and pid integrin 1 pathways were highly enriched for AVC-specific DEGs. CXCL16, GPM6A, BEX2, S100A9, and SCARA5 genes were all regarded diagnostic markers for AVC. The model was effectively constructed using a molecular diagnostic score system with significant diagnostic value (AUC = 0.987) and verified using the independent dataset GSE83453 (AUC = 0.986). Immune cell infiltration research revealed that B cell naive, B cell memory, plasma cells, NK cell activated, monocytes, and macrophage M0 may be involved in the development of AVC. Additionally, all diagnostic characteristics may have varying degrees of correlation with immune cells. The most promising small molecule medicines for reversing AVC gene expression are Doxazosin and Terfenadine. CONCLUSION It was identified that CXCL16, GPM6A, BEX2, S100A9, and SCARA5 are potentially beneficial for diagnosing and treating AVC. A diagnostic model was constructed based on a molecular prognostic score system using machine learning. The aforementioned immune cell infiltration may have a significant influence on the development and incidence of AVC.
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Affiliation(s)
- Tao Xiong
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yan Chen
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Shen Han
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Tian-Chen Zhang
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Lei Pu
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yu-Xin Fan
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Wei-Chen Fan
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Ya-Yong Zhang
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Ya-Xiong Li
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
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Wang S, Liu W, Ye Z, Xia X, Guo M. Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest. Front Genet 2022; 13:957718. [PMCID: PMC9585230 DOI: 10.3389/fgene.2022.957718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: Papillary thyroid carcinoma (PTC) accounts for 80% of thyroid malignancy, and the occurrence of PTC is increasing rapidly. The present study was conducted with the purpose of identifying novel and important gene panels and developing an early diagnostic model for PTC by combining artificial neural network (ANN) and random forest (RF).Methods and results: Samples were searched from the Gene Expression Omnibus (GEO) database, and gene expression datasets (GSE27155, GSE60542, and GSE33630) were collected and processed. GSE27155 and GSE60542 were merged into the training set, and GSE33630 was defined as the validation set. Differentially expressed genes (DEGs) in the training set were obtained by “limma” of R software. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis as well as immune cell infiltration analysis were conducted based on DEGs. Important genes were identified from the DEGs by random forest. Finally, an artificial neural network was used to develop a diagnostic model. Also, the diagnostic model was validated by the validation set, and the area under the receiver operating characteristic curve (AUC) value was satisfactory.Conclusion: A diagnostic model was established by a joint of random forest and artificial neural network based on a novel gene panel. The AUC showed that the diagnostic model had significantly excellent performance.
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A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2679050. [PMID: 36213574 PMCID: PMC9534672 DOI: 10.1155/2022/2679050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 09/11/2022] [Accepted: 09/12/2022] [Indexed: 11/17/2022]
Abstract
Background Asthma significantly impacts human life and health as a chronic disease. Traditional treatments for asthma have several limitations. Artificial intelligence aids in cancer treatment and may also accelerate our understanding of asthma mechanisms. We aimed to develop a new clinical diagnosis model for asthma using artificial neural networks (ANN). Methods Datasets (GSE85566, GSE40576, and GSE13716) were downloaded from Gene Expression Omnibus (GEO) and identified differentially expressed CpGs (DECs) enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models. In addition, two external validation datasets (GSE40576 and GSE137716) were used to validate the diagnostic ability of the model. Results The methylation analysis tool (ChAMP) considered DECs that were up-regulated (n =121) and down-regulated (n =20). GO results showed enrichment of actin cytoskeleton organization and cell-substrate adhesion, shigellosis, and serotonergic synapses. RF (random forest) analysis identified 10 crucial DECs (cg05075579, cg20434422, cg03907390, cg00712106, cg05696969, cg22862094, cg11733958, cg00328720, and cg13570822). ANN constructed the clinical model according to 10 DECs. In two external validation datasets (GSE40576 and GSE137716), the Area Under Curve (AUC) for GSE137716 was 1.000, and AUC for GSE40576 was 0.950, confirming the reliability of the model. Conclusion Our findings provide new methylation markers and clinical diagnostic models for asthma diagnosis and treatment.
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Yang Y, Xu L, Qiao Y, Wang T, Zheng Q. Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn’s disease. Front Genet 2022; 13:976578. [PMID: 36186439 PMCID: PMC9520627 DOI: 10.3389/fgene.2022.976578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 08/16/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: Crohn’s disease (CD), a chronic recurrent illness, is a type of inflammatory bowel disease whose incidence and prevalence rates are gradually increasing. However, there is no universally accepted criterion for CD diagnosis. The aim of this study was to create a diagnostic prediction model for CD and identify immune cell infiltration features in CD. Methods: In this study, gene expression microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) between 178 CD and 38 control cases. Enrichment analysis of DEGs was also performed to explore the biological role of DEGs. Moreover, the “randomForest” package was applied to select core genes that were used to create a neural network model. Finally, in the training cohort, we used CIBERSORT to evaluate the immune landscape between the CD and normal groups. Results: The results of enrichment analysis revealed that these DEGs may be involved in biological processes associated with immunity and inflammatory responses. Moreover, the top 3 hub genes in the protein-protein interaction network were IL-1β, CCL2, and CXCR2. The diagnostic model allowed significant discrimination with an area under the ROC curve of 0.984 [95% confidence interval: 0.971–0.993]. A validation cohort (GSE36807) was utilized to ensure the reliability and applicability of the model. In addition, the immune infiltration analysis indicated nine different immune cell types were significantly different between the CD and healthy control groups. Conclusion: In summary, this study offers a novel insight into the diagnosis of CD and provides potential biomarkers for the precise treatment of CD.
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He Y, Cong L, He Q, Feng N, Wu Y. Development and validation of immune-based biomarkers and deep learning models for Alzheimer’s disease. Front Genet 2022; 13:968598. [PMID: 36072674 PMCID: PMC9441688 DOI: 10.3389/fgene.2022.968598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 07/22/2022] [Indexed: 12/30/2022] Open
Abstract
Background: Alzheimer’s disease (AD) is the most common form of dementia in old age and poses a severe threat to the health and life of the elderly. However, traditional diagnostic methods and the ATN diagnostic framework have limitations in clinical practice. Developing novel biomarkers and diagnostic models is necessary to complement existing diagnostic procedures. Methods: The AD expression profile dataset GSE63060 was downloaded from the NCBI GEO public database for preprocessing. AD-related differentially expressed genes were screened using a weighted co-expression network and differential expression analysis, and functional enrichment analysis was performed. Subsequently, we screened hub genes by random forest, analyzed the correlation between hub genes and immune cells using ssGSEA, and finally built an AD diagnostic model using an artificial neural network and validated it. Results: Based on the random forest algorithm, we screened a total of seven hub genes from AD-related DEGs, based on which we confirmed that hub genes play an essential role in the immune microenvironment and successfully established a novel diagnostic model for AD using artificial neural networks, and validated its effectiveness in the publicly available datasets GSE63060 and GSE97760. Conclusion: Our study establishes a reliable model for screening and diagnosing AD that provides a theoretical basis for adding diagnostic biomarkers for the AD gene.
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Affiliation(s)
| | | | | | | | - Yun Wu
- *Correspondence: Yun Wu, ; Nianping Feng,
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Huang F, Zhang S, Li X, Huang Y, He S, Luo L. STAT3-mediated ferroptosis is involved in ulcerative colitis. Free Radic Biol Med 2022; 188:375-385. [PMID: 35779691 DOI: 10.1016/j.freeradbiomed.2022.06.242] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/16/2022] [Accepted: 06/27/2022] [Indexed: 12/13/2022]
Abstract
Ferroptosis is a form of iron-dependent lipid peroxidation cell death that plays an important role in inflammation. However, the mechanism of ferroptosis in ulcerative colitis (UC) remains to be further investigated. In the present study, we merged the differentially expressed genes (DEGs) of UC in GEO database with the ferroptosis-related genes of FerrDb for bioinformatics analysis and successfully screened out the ferroptosis-related hub gene STAT3 (signal transducer and activator of transcription 3). Then we further validated the role of STAT3-mediated ferroptosis in vitro and in vivo models of colitis. The results showed that ferroptosis was increased in DSS-induced colitis, salmonella typhimurium (S. Tm) colitis and H2O2-induced IEC-6 cells. And the phosphorylation level of the hub gene STAT3 was down-regulated in IEC-6 cells treated with H2O2, while Fer-1, an ferroptosis inhibitor, reactivated the phosphorylation level of STAT3. In addition, co-treatment of cells with H2O2 and STAT3 inhibitor (stattic) showed an additive effect on the extent of ferroptosis. Taken together, these findings suggest that ferroptosis is closely associated with the development of colitis and ferroptosis-related gene STAT3 could serve as a potential biomarker for diagnosis and treatment of ulcerative colitis.
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Affiliation(s)
- Fangfang Huang
- Graduate School, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China; Department of Pediatrics, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Suzhou Zhang
- The First Clinical College, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Xiaoling Li
- Experimental Animal Center, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Yuge Huang
- Department of Pediatrics, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, 524023, China.
| | - Shasha He
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Chinese Medicine, Beijing, 100000, China.
| | - Lianxiang Luo
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, Guangdong, 524023, China.
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Sun D, Peng H, Wu Z. Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network. Front Aging Neurosci 2022; 14:921906. [PMID: 35847663 PMCID: PMC9280980 DOI: 10.3389/fnagi.2022.921906] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 06/01/2022] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition that causes cognitive decline over time. Because existing diagnostic approaches for AD are limited, improving upon previously established diagnostic models based on genetic biomarkers is necessary. Firstly, four AD gene expression datasets were collected from the Gene Expression Omnibus (GEO) database. Two datasets were used to establish diagnostic models, and the other two datasets were used to verify the model effect. We merged GSE5281 with GSE44771 as the training dataset and found 120 DEGs. Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples. The weights of these key genes were measured, and a diagnostic model was created using an artificial neural network (ANN). The area under the curve (AUC) of the model is 0.953, while the accuracy is 0.914. In the final step, two validation datasets were utilized to assess AUC performance. In GSE109887, our model had an AUC of 0.854, and in GSE132903, it had an AUC of 0.810. To summarize, we successfully identified key gene biomarkers and developed a new AD diagnostic model.
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Wu Y, Chen H, Li L, Zhang L, Dai K, Wen T, Peng J, Peng X, Zheng Z, Jiang T, Xiong W. Construction of Novel Gene Signature-Based Predictive Model for the Diagnosis of Acute Myocardial Infarction by Combining Random Forest With Artificial Neural Network. Front Cardiovasc Med 2022; 9:876543. [PMID: 35694667 PMCID: PMC9174464 DOI: 10.3389/fcvm.2022.876543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/26/2022] [Indexed: 11/19/2022] Open
Abstract
Background Acute myocardial infarction (AMI) is one of the most common causes of mortality around the world. Early diagnosis of AMI contributes to improving prognosis. In our study, we aimed to construct a novel predictive model for the diagnosis of AMI using an artificial neural network (ANN), and we verified its diagnostic value via constructing the receiver operating characteristic (ROC). Methods We downloaded three publicly available datasets (training sets GSE48060, GSE60993, and GSE66360) from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between 87 AMI and 78 control samples. We applied the random forest (RF) and ANN algorithms to further identify novel gene signatures and construct a model to predict the possibility of AMI. Besides, the diagnostic value of our model was further validated in the validation sets GSE61144 (7 AMI patients and 10 controls), GSE34198 (49 AMI patients and 48 controls), and GSE97320 (3 AMI patients and 3 controls). Results A total of 71 DEGs were identified, of which 68 were upregulated and 3 were downregulated. Firstly, 11 key genes in 71 DEGs were screened with RF classifier for the classification of AMI and control samples. Then, we calculated the weight of each key gene using ANN. Furthermore, the diagnostic model was constructed and named neuralAMI, with significant predictive power (area under the curve [AUC] = 0.980). Finally, our model was validated with the independent datasets GSE61144 (AUC = 0.900), GSE34198 (AUC = 0.882), and GSE97320 (AUC = 1.00). Conclusion Machine learning was used to develop a reliable predictive model for the diagnosis of AMI. The results of our study provide potential gene biomarkers for early disease screening.
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Affiliation(s)
- Yanze Wu
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Hui Chen
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lei Li
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Liuping Zhang
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Kai Dai
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tong Wen
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jingtian Peng
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaoping Peng
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zeqi Zheng
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Jiang
- Department of Hospital Infection Control, The First Affiliated Hospital of Nanchang University, Nanchang, China
- *Correspondence: Ting Jiang,
| | - Wenjun Xiong
- Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Wenjun Xiong,
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Vadapalli S, Abdelhalim H, Zeeshan S, Ahmed Z. Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. Brief Bioinform 2022; 23:6590150. [PMID: 35595537 DOI: 10.1093/bib/bbac191] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/02/2022] [Accepted: 04/26/2022] [Indexed: 12/16/2022] Open
Abstract
Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.
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Affiliation(s)
- Sreya Vadapalli
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St, New Brunswick, NJ, USA
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA.,Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA
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She J, Su D, Diao R, Wang L. A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis. Front Genet 2022; 13:848116. [PMID: 35350240 PMCID: PMC8957986 DOI: 10.3389/fgene.2022.848116] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 01/28/2022] [Indexed: 11/13/2022] Open
Abstract
Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs.
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Affiliation(s)
- Jiajie She
- Reproductive Medicine Centre, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China.,Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Danna Su
- Reproductive Medicine Centre, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Ruiying Diao
- Reproductive Medicine Centre, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Liping Wang
- Reproductive Medicine Centre, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
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Feng J, Chen Y, Feng Q, Ran Z, Shen J. Novel Gene Signatures Predicting Primary Non-response to Infliximab in Ulcerative Colitis: Development and Validation Combining Random Forest With Artificial Neural Network. Front Med (Lausanne) 2021; 8:678424. [PMID: 34650991 PMCID: PMC8505970 DOI: 10.3389/fmed.2021.678424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 09/02/2021] [Indexed: 12/20/2022] Open
Abstract
Background: While infliximab has revolutionized the treatment of ulcerative colitis, primary non-response is difficult to predict, which limits effective disease management. The study aimed to establish a novel genetic model to predict primary non-response to infliximab in patients with ulcerative colitis. Methods: Publicly available mucosal expression profiles of infliximab-treated ulcerative colitis patients (GSE16879, GSE12251) were utilized to identify potential predictive gene panels. The random forest algorithm and artificial neural network were applied to further screen for predictive signatures and establish a model to predict primary non-response to infliximab. Results: A total of 28 downregulated and 2 upregulated differentially expressed genes were identified as predictors. The novel model was successfully established on the basis of the molecular prognostic score system, with a significantly predictive value (AUC = 0.93), and was validated with an independent dataset GSE23597 (AUC = 0.81). Conclusion: Machine learning was used to construct a predictive model based on the molecular prognostic score system. The novel model can predict primary non-response to infliximab in patients with ulcerative colitis, which aids in clinical-decision making.
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Affiliation(s)
- Jing Feng
- Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Inflammatory Bowel Disease Research Center, Shanghai, China.,Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Institute of Digestive Disease, Shanghai, China
| | - Yueying Chen
- Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Inflammatory Bowel Disease Research Center, Shanghai, China.,Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Institute of Digestive Disease, Shanghai, China
| | - Qi Feng
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhihua Ran
- Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Inflammatory Bowel Disease Research Center, Shanghai, China.,Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Institute of Digestive Disease, Shanghai, China
| | - Jun Shen
- Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Inflammatory Bowel Disease Research Center, Shanghai, China.,Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Institute of Digestive Disease, Shanghai, China
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22
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Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications. Genes (Basel) 2021; 12:genes12091438. [PMID: 34573420 PMCID: PMC8466305 DOI: 10.3390/genes12091438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/21/2021] [Accepted: 09/14/2021] [Indexed: 12/14/2022] Open
Abstract
Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.
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Identification of Diagnostic Biomarkers and Their Correlation with Immune Infiltration in Age-Related Macular Degeneration. Diagnostics (Basel) 2021; 11:diagnostics11061079. [PMID: 34204836 PMCID: PMC8231534 DOI: 10.3390/diagnostics11061079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/05/2021] [Accepted: 06/10/2021] [Indexed: 01/09/2023] Open
Abstract
Age-related macular degeneration (AMD) is a progressive neurodegenerative disease of the central retina, with no suitable biomarkers for early diagnosis and treatment. This study aimed to find potential diagnostic biomarker candidates for AMD and investigate their immune-related roles in this pathology. Weight gene correlation analysis was first performed based on data from the Gene Expression Omnibus database and 20 hub genes were identified. The functional enrichment analyses showed that the innate immune response, inflammatory response, and complement activation were key pathways associated with AMD. Complement C1s (C1S), adrenomedullin (ADM), and immediate early response 5 like (IER5L) were identified as the crucial genes with favorable diagnostic values for AMD by using LASSO analysis and multiple logistic regression. Furthermore, a 3-gene model was constructed and proved to be of good diagnostic and predictive performance for AMD (AUC = 0.785, 0.840, and 0.810 in training, test, and validation set, respectively). Finally, CIBERSORT was used to evaluate the infiltration of immune cells in AMD tissues. The results showed that the NK cells, CD4 memory T cell activation, and macrophage polarization may be involved in the AMD process. C1S, ADM, and IER5L were correlated with the infiltration of the above immune cells. In conclusion, our study suggests that C1S, ADM, and IER5L are promising diagnostic biomarker candidates for AMD and may regulate the infiltration of immune cells in the occurrence and progression of AMD.
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Gubatan J, Levitte S, Patel A, Balabanis T, Wei MT, Sinha SR. Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions. World J Gastroenterol 2021; 27:1920-1935. [PMID: 34007130 PMCID: PMC8108036 DOI: 10.3748/wjg.v27.i17.1920] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/04/2021] [Accepted: 04/13/2021] [Indexed: 02/06/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.
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Affiliation(s)
- John Gubatan
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Steven Levitte
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Akshar Patel
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Tatiana Balabanis
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Mike T Wei
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Sidhartha R Sinha
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
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