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Cai H, Zeng Y, Luo D, Shao Y, Liu M, Wu J, Gao X, Zheng J, Zhou L, Liu F. Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation. Genomics 2024; 116:110879. [PMID: 38851464 DOI: 10.1016/j.ygeno.2024.110879] [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: 12/28/2023] [Revised: 04/08/2024] [Accepted: 06/05/2024] [Indexed: 06/10/2024]
Abstract
OBJECTIVE Although programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conneted, the interplay among various PCD forms remains elusive. In this study, We aimed at identifying independently DN-associated PCD pathways and biomarkers relevant to the related pathogenesis. METHODS We acquired DN-related datasets from the GEO database and identified PCDs independently correlated with DN (DN-PCDs) through single-sample Gene Set Enrichment Analysis (ssGSEA) as well as, univariate and multivariate logistic regression analyses. Subsequently, applying differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz cluster analysis, we filtered the DN-PCDs pertinent to DN onset and progression. The convergence of various machine learning techniques ultimately spotlighted hub genes, substantiated through dataset meta-analyses and experimental validations, thereby confirming hub genes and related pathways expression consistencies. RESULTS We harmonized four DN-related datasets (GSE1009, GSE142025, GSE30528, and GSE30529) post-batch-effect removal for subsequent analyses. Our differential expression analysis yielded 709 differentially expressed genes (DEGs), comprising 446 upregulated and 263 downregulated DEGs. Based on our ssGSEA as well as univariate and multivariate logistic regressions, apoptosis and NETotic cell death were appraised as independent risk factors for DN (Odds Ratio > 1, p < 0.05). Next, we further refined 588 apoptosis- and NETotic cell death-associated genes through WGCNA and Mfuzz analysis, resulting in the identification of 17 DN-PCDs. Integrating protein-protein interaction (PPI) network analyses, network topology, and machine learning, we pinpointed hub genes (e.g., IL33, RPL11, and CX3CR1) as significant DN risk factors with expression corroborating in subsequent meta-analyses and experimental validations. Our GSEA enrichment analysis discerned differential enrichments between DN and control samples within pathways such as IL2/STAT5, IL6/JAK/STAT3, TNF-α via NF-κB, apoptosis, and oxidative phosphorylation, with related proteins such as IL2, IL6, and TNFα, which we subsequently submitted to experimental verification. CONCLUSION Innovatively stemming from from PCD interactions, in this study, we discerned PCDs with an independent impact on DN: apoptosis and NETotic cell death. We further screened DN evolution- and progression-related biomarkers, i.e. IL33, RPL11, and CX3CR1, all of which we empirically validated. This study not only poroposes a PCD-centric perspective for DN studies but also provides evidence for PCD-mediated immune cell infiltration exploration in DN regulation. Our results could motivate further exploration of DN pathogenesis, such as how the inflammatory microenvironment mediates NETotic cell death in DN regulation, representing a promising direction for future research.
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Affiliation(s)
- Huilian Cai
- Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Yi Zeng
- Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Dongqiang Luo
- Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Ying Shao
- The Fourth Hospital of Harbin Medical University, Harbin 150001, China
| | - Manting Liu
- Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Jiayu Wu
- Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Xiaolu Gao
- Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Jiyuan Zheng
- Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Lisi Zhou
- Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Feng Liu
- The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, 510080, China.
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Ali G, Anwar M, Nauman M, Faheem M, Rashid J. Lyme rashes disease classification using deep feature fusion technique. Skin Res Technol 2023; 29:e13519. [PMID: 38009027 PMCID: PMC10628356 DOI: 10.1111/srt.13519] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 10/24/2023] [Indexed: 11/28/2023]
Abstract
Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists' probe and investigate Lyme skin rashes effectively. This paper proposes a new in-depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state-of-the-art models.
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Affiliation(s)
- Ghulam Ali
- Department of Computer ScienceUniversity of OkaraOkaraPakistan
| | - Muhammad Anwar
- Department of Information SciencesDivision of Science and TechnologyUniversity of EducationLahorePakistan
| | - Muhammad Nauman
- Department of Computer ScienceUniversity of OkaraOkaraPakistan
| | - Muhammad Faheem
- School of Technology and InnovationsUniversity of VaasaVaasaFinland
| | - Javed Rashid
- Department of IT ServicesUniversity of OkaraOkaraPakistan
- MLC LabOkaraPakistan
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Rabaan AA, Bakhrebah MA, Alotaibi J, Natto ZS, Alkhaibari RS, Alawad E, Alshammari HM, Alwarthan S, Alhajri M, Almogbel MS, Aljohani MH, Alofi FS, Alharbi N, Al-Adsani W, Alsulaiman AM, Aldali J, Ibrahim FA, Almaghrabi RS, Al-Omari A, Garout M. Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review. J Infect Public Health 2023; 16:1837-1847. [PMID: 37769584 DOI: 10.1016/j.jiph.2023.08.021] [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: 04/11/2023] [Revised: 07/19/2023] [Accepted: 08/27/2023] [Indexed: 10/03/2023] Open
Abstract
Infectious diseases present a global challenge, requiring accurate diagnosis, effective treatments, and preventive measures. Artificial intelligence (AI) has emerged as a promising tool for analysing complex molecular data and improving the diagnosis, treatment, and prevention of infectious diseases. Computer-aided detection (CAD) using convolutional neural networks (CNN) has gained prominence for diagnosing tuberculosis (TB) and other infectious diseases such as COVID-19, HIV, and viral pneumonia. The review discusses the challenges and limitations associated with AI in this field and explores various machine-learning models and AI-based approaches. Artificial neural networks (ANN), recurrent neural networks (RNN), support vector machines (SVM), multilayer neural networks (MLNN), CNN, long short-term memory (LSTM), and random forests (RF) are among the models discussed. The review emphasizes the potential of AI to enhance the accuracy and efficiency of diagnosis, treatment, and prevention of infectious diseases, highlighting the need for further research and development in this area.
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Affiliation(s)
- Ali A Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran 31311, Saudi Arabia; College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; Department of Public Health and Nutrition, The University of Haripur, Haripur 22610, Pakistan.
| | - Muhammed A Bakhrebah
- Life Science and Environment Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Jawaher Alotaibi
- Infectious Diseases Unit, Department of Medicine, King Faisal Specialist Hospital and Research Center, Riyadh 11564, Saudi Arabia
| | - Zuhair S Natto
- Department of Dental Public Health, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Rahaf S Alkhaibari
- Molecular Diagnostic Laboratory, Dammam Regional Laboratory and Blood Bank, Dammam 31411, Saudi Arabia
| | - Eman Alawad
- Adult Infectious Diseases Department, Prince Mohammed Bin Abdulaziz Hospital, Riyadh 11474, Saudi Arabia
| | - Huda M Alshammari
- Clinical Pharmacy Department, College of Pharmacy, Northern Border University, Arar 9280, Saudi Arabia
| | - Sara Alwarthan
- Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
| | - Mashael Alhajri
- Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
| | - Mohammed S Almogbel
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 4030, Saudi Arabia
| | - Maha H Aljohani
- Department of Infectious Diseases, King Fahad Hospital, Madinah 42351, Saudi Arabia
| | - Fadwa S Alofi
- Department of Infectious Diseases, King Fahad Hospital, Madinah 42351, Saudi Arabia
| | - Nada Alharbi
- Department of Basic Medical Sciences, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
| | - Wasl Al-Adsani
- Department of Medicine, Infectious Diseases Hospital, Kuwait City 63537, Kuwait; Department of Infectious Diseases, Hampton Veterans Administration Medical Center, Hampton, VA 23667, USA
| | | | - Jehad Aldali
- Department of Pathology, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh 13317, Saudi Arabia
| | - Fatimah Al Ibrahim
- Infectious Disease Division, Department of Internal Medicine, Dammam Medical Complex, Dammam 32245, Saudi Arabia
| | - Reem S Almaghrabi
- Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia
| | - Awad Al-Omari
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; Research Center, Dr. Sulaiman Al Habib Medical Group, Riyadh 11372, Saudi Arabia
| | - Mohammed Garout
- Department of Community Medicine and Health Care for Pilgrims, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
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Kumar AK, Jain S, Jain S, Ritam M, Xia Y, Chandra R. Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107421. [PMID: 36805280 DOI: 10.1016/j.cmpb.2023.107421] [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: 08/30/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES The use of machine learning methods for modelling bio-systems is becoming prominent which can further improve bio-medical technologies. Physics-informed neural networks (PINNs) can embed the knowledge of physical laws that govern a system during the model training process. PINNs utilise differential equations in the model which traditionally used numerical methods that are computationally complex. METHODS We integrate PINNs with an entangled ladder network for modelling respiratory systems by considering a lungs conduction zone to evaluate the respiratory impedance for different initial conditions. We evaluate the respiratory impedance for the inhalation phase of breathing for a symmetric model of the human lungs using entanglement and continued fractions. RESULTS We obtain the impedance of the conduction zone of the lungs pulmonary airways using PINNs for nine different combinations of velocity and pressure of inhalation. We compare the results from PINNs with the finite element method using the mean absolute error and root mean square error. The results show that the impedance obtained with PINNs contrasts with the conventional forced oscillation test used for deducing the respiratory impedance. The results show similarity with the impedance plots for different respiratory diseases. CONCLUSION We find a decrease in impedance when the velocity of breathing is lowered gradually by 20%. Hence, the methodology can be used to design smart ventilators to the improve flow of breathing.
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Affiliation(s)
- Amit Krishan Kumar
- Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam; State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Snigdha Jain
- Department of Electronics and Communications Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - Shirin Jain
- Department of Electronics and Communications Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - M Ritam
- Department of Chemical Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - Yuanqing Xia
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Rohitash Chandra
- Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney, NSW 2052, Australia.
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Sharma K, Aminian M, Ghosh T, Liu X, Kirby M. Using machine learning to determine the time of exposure to infection by a respiratory pathogen. Sci Rep 2023; 13:5340. [PMID: 37005391 PMCID: PMC10067823 DOI: 10.1038/s41598-023-30306-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/21/2023] [Indexed: 04/04/2023] Open
Abstract
Given an infected host, estimating the time that has elapsed since initial exposure to the pathogen is an important problem in public health. In this paper we use longitudinal gene expression data from human challenge studies of viral respiratory illnesses for building predictive models to estimate the time elapsed since onset of respiratory infection. We apply sparsity driven machine learning to this time-stamped gene expression data to model the time of exposure by a pathogen and subsequent infection accompanied by the onset of the host immune response. These predictive models exploit the fact that the host gene expression profile evolves in time and its characteristic temporal signature can be effectively modeled using a small number of features. Predicting the time of exposure to infection to be in first 48 h after exposure produces BSR in the range of 80-90% on sequestered test data. A variety of machine learning experiments provide evidence that models developed on one virus can be used to predict exposure time for other viruses, e.g., H1N1, H3N2, and HRV. The interferon [Formula: see text] signaling pathway appears to play a central role in keeping time from onset of infection. Successful prediction of the time of exposure to a pathogen has potential ramifications for patient treatment and contact tracing.
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Affiliation(s)
- Kartikay Sharma
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - Manuchehr Aminian
- Department of Mathematics, California State Polytechnic University, Pomona, CA, USA
| | - Tomojit Ghosh
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - Xiaoyu Liu
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Michael Kirby
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA.
- Department of Mathematics, Colorado State University, Fort Collins, CO, USA.
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Sempionatto JR, Lasalde-Ramírez JA, Mahato K, Wang J, Gao W. Wearable chemical sensors for biomarker discovery in the omics era. Nat Rev Chem 2022; 6:899-915. [PMID: 37117704 DOI: 10.1038/s41570-022-00439-w] [Citation(s) in RCA: 93] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2022] [Indexed: 11/16/2022]
Abstract
Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine.
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Precision Medicine Approaches with Metabolomics and Artificial Intelligence. Int J Mol Sci 2022; 23:ijms231911269. [PMID: 36232571 PMCID: PMC9569627 DOI: 10.3390/ijms231911269] [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: 05/18/2022] [Revised: 09/14/2022] [Accepted: 09/20/2022] [Indexed: 11/18/2022] Open
Abstract
Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics–AI systems, limitations thereof and recent tools were also discussed.
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Alcon-Chino MET, De-Simone SG. Recent Advances in the Immunologic Method Applied to Tick-Borne Diseases in Brazil. Pathogens 2022; 11:pathogens11080870. [PMID: 36014992 PMCID: PMC9414916 DOI: 10.3390/pathogens11080870] [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: 05/24/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 12/10/2022] Open
Abstract
Zoonotic-origin infectious diseases are one of the major concerns of human and veterinary health systems. Ticks, as vectors of several zoonotic diseases, are ranked second only to mosquitoes as vectors. Many ticks’ transmitted infections are still endemic in the Americas, Europe, and Africa and represent approximately 17% of their infectious diseases population. Although our scientific capacity to identify and diagnose diseases is increasing, it remains a challenge in the case of tick-borne conditions. For example, in 2017, 160 cases of the Brazilian Spotted Fever (BSF, a tick-borne illness) were confirmed, alarming the notifiable diseases information system. Conversely, Brazilian borreliosis and ehrlichiosis do not require notification. Still, an increasing number of cases in humans and dogs have been reported in southeast and northeastern Brazil. Immunological methods applied to human and dog tick-borne diseases (TBD) show low sensitivity and specificity, cross-reactions, and false IgM positivity. Thus, the diagnosis and management of TBD are hampered by the personal tools and indirect markers used. Therefore, specific and rapid methods urgently need to be developed to diagnose the various types of tick-borne bacterial diseases. This review presents a brief historical perspective on the evolution of serological assays and recent advances in diagnostic tests for TBD (ehrlichiosis, BSF, and borreliosis) in humans and dogs, mainly applied in Brazil. Additionally, this review covers the emerging technologies available in diagnosing TBD, including biosensors, and discusses their potential for future use as gold standards in diagnosing these diseases.
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Affiliation(s)
- Mônica E. T. Alcon-Chino
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), FIOCRUZ, Rio de Janeiro 21040-900, Brazil;
- Post-Graduation Program in Science and Biotechnology, Department of Molecular and Cellular Biology, Biology Institute, Federal Fluminense University, Niterói 22040-036, Brazil
| | - Salvatore G. De-Simone
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), FIOCRUZ, Rio de Janeiro 21040-900, Brazil;
- Post-Graduation Program in Science and Biotechnology, Department of Molecular and Cellular Biology, Biology Institute, Federal Fluminense University, Niterói 22040-036, Brazil
- Laboratory of Epidemiology and Molecular Systematics, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro 21040-900, Brazil
- Correspondence: ; Tel.: +55-21-38658183
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