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Elste J, Saini A, Mejia-Alvarez R, Mejía A, Millán-Pacheco C, Swanson-Mungerson M, Tiwari V. Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions. Biomolecules 2024; 14:911. [PMID: 39199298 PMCID: PMC11352483 DOI: 10.3390/biom14080911] [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: 06/13/2024] [Revised: 07/11/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024] Open
Abstract
A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.
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Affiliation(s)
- James Elste
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
| | - Akash Saini
- Hinsdale Central High School, 5500 S Grant St, Hinsdale, IL 60521, USA;
| | - Rafael Mejia-Alvarez
- Department of Physiology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA;
| | - Armando Mejía
- Departamento de Biotechnology, Universidad Autónoma Metropolitana-Iztapalapa, Ciudad de Mexico 09340, Mexico;
| | - Cesar Millán-Pacheco
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Av. Universidad No. 1001, Col Chamilpa, Cuernavaca 62209, Mexico;
| | - Michelle Swanson-Mungerson
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
| | - Vaibhav Tiwari
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
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Carney M, Pelaia TM, Chew T, Teoh S, Phu A, Kim K, Wang Y, Iredell J, Zerbib Y, McLean A, Schughart K, Tang B, Shojaei M, Short KR. Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study. THE LANCET. MICROBE 2024; 5:e272-e281. [PMID: 38310908 DOI: 10.1016/s2666-5247(23)00363-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 02/06/2024]
Abstract
BACKGROUND Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This study aimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals. METHODS In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set. FINDINGS Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission. INTERPRETATION These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection. FUNDING Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.
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Affiliation(s)
- Meagan Carney
- School of Mathematics and Physics, University of Queensland, Brisbane, QLD, Australia
| | - Tiana Maria Pelaia
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW, Australia
| | - Tracy Chew
- Sydney Informatics Hub, Core Research Facilities, University of Sydney, Sydney, NSW, Australia
| | - Sally Teoh
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW, Australia
| | - Amy Phu
- Faculty of Medicine and Health, Sydney Medical School Westmead, Westmead Hospital, University of Sydney, Sydney, NSW, Australia
| | - Karan Kim
- Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Ya Wang
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW, Australia; The University of Sydney Nepean Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Jonathan Iredell
- Faculty of Medicine and Health, School of Medical Sciences, University of Sydney, Sydney, NSW, Australia; Sydney Institute for Infectious Disease, University of Sydney, Sydney, NSW, Australia; Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Sydney, NSW, Australia; Westmead Hospital, Western Sydney Local Health District, Westmead, NSW, Australia
| | - Yoann Zerbib
- Intensive Care Department, Amiens University Hospital, Amiens, France
| | - Anthony McLean
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW, Australia; The University of Sydney Nepean Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Klaus Schughart
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, USA; Institute of Virology Münster, University of Münster, Münster, Germany
| | - Benjamin Tang
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW, Australia; Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Maryam Shojaei
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW, Australia; The University of Sydney Nepean Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Sydney, NSW, Australia.
| | - Kirsty R Short
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia.
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Ojurongbe TA, Afolabi HA, Bashiru KA, Sule WF, Akinde SB, Ojurongbe O, Adegoke NA. Prediction of malaria positivity using patients' demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment. Trop Dis Travel Med Vaccines 2023; 9:24. [PMID: 38098124 PMCID: PMC10722830 DOI: 10.1186/s40794-023-00208-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 09/28/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein-2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all these limitations. Consequently, the addition of automated detection and classification of malaria using laboratory methods can provide patients with more accurate and faster diagnosis. Therefore, this study used a machine-learning model to predict Plasmodium falciparum (Pf) antigen positivity (presence of malaria) based on sociodemographic behaviour, environment, and clinical features. METHOD Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Outcomes were classified as Pf-positive or Pf-negative, corresponding to the presence or absence of malaria, respectively. RESULTS Among the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic curve for the training set (AUC = 84%; 95% confidence interval [CI]: 75-93%) and test set (AUC = 83%; 95% CI: 63-100%). Increased odds of malaria were associated with higher body weight (adjusted odds ratio (AOR) = 4.50, 95% CI: 2.27 to 8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was not significant, patients with high body temperature had higher odds of testing positive for the Pf antigen than those who did not have high body temperature (AOR = 1.40, 95% CI: 0.99 to 1.91, p = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI: 1.30 to 4.66, p = 0.006) or experienced fever (AOR = 2.10, 95% CI: 0.88 to 4.24, p = 0.099), headache (AOR = 2.07; 95% CI: 0.95 to 3.95, p = 0.068), muscle pain (AOR = 1.49; 95% CI: 0.66 to 3.39, p = 0.333), and vomiting (AOR = 2.32; 95% CI: 0.85 to 6.82, p = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI: 0.41 to 0.90, p = 0.012) and BMI (AOR = 0.47, 95% CI: 0.26 to 0.80, p = 0.006). CONCLUSION Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict Pf antigen positivity may be a valuable tool for clinical decision-making.
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Affiliation(s)
| | | | | | | | | | - Olusola Ojurongbe
- Department of Medical Microbiology and Parasitology, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
- Center for Emerging and Re-emerging Infectious Diseases, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
| | - Nurudeen A Adegoke
- Melanoma Institute Australia, The University of Sydney, Sydney, Australia
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Visaggi P, Barberio B, Gregori D, Azzolina D, Martinato M, Hassan C, Sharma P, Savarino E, de Bortoli N. Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases. Aliment Pharmacol Ther 2022; 55:528-540. [PMID: 35098562 PMCID: PMC9305819 DOI: 10.1111/apt.16778] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/09/2022] [Accepted: 01/09/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs). AIM We performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD. METHODS We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated. RESULTS For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively. CONCLUSIONS AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
| | - Brigida Barberio
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
- Department of Medical ScienceUniversity of FerraraFerraraItaly
| | - Matteo Martinato
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas UniversityVia Rita Levi Montalcini 420072 Pieve Emanuele, MilanItaly
- IRCCS Humanitas Research Hospitalvia Manzoni 5620089 Rozzano, MilanItaly
| | - Prateek Sharma
- University of Kansas School of Medicine and VA Medical CenterKansas CityMissouriUSA
| | - Edoardo Savarino
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Nicola de Bortoli
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
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Cytomegalovirus Diseases of the Gastrointestinal Tract. Viruses 2022; 14:v14020352. [PMID: 35215942 PMCID: PMC8879032 DOI: 10.3390/v14020352] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/31/2022] [Accepted: 02/03/2022] [Indexed: 02/04/2023] Open
Abstract
Cytomegalovirus (CMV) infection of the gastrointestinal (GI) tract can be fatal. However, very few studies have provided comprehensive analyses and specified the differences in symptoms observed in different parts of the GI tract. This study aimed to comprehensively analyze clinical manifestations and management of GI CMV disease. This retrospective cohort study enrolled the patients who had CMV diseases of the GI tract proved by CMV immunohistochemistry stain from the pathology database in a 4000-bed tertiary medical center between January 2000 and May 2021. The patient characteristics, clinical manifestations, endoscopic features, treatments, outcomes, and prognostic factors were analyzed. A total of 356 patients were enrolled, including 46 infected in the esophagus, 76 in the stomach, 30 in the small intestine, and 204 in the colon. In total, 49.4% patients were immunocompromised. The overall in-hospital mortality rate was 20.8%: CMV enteritis had the highest rate (23.3%). Sixty percent of patients received antiviral treatment and 16% were administered both intravenous and oral anti-viral drugs (Combo therapy, minimal and mean treatment duration were 14 and 39.9 ± 25 days). Prognostic factors of in-hospital mortality included age, immune status, albumin level, platelet count, GI bleeding, time-to-diagnosis, and Combo therapy. In the survival analysis, immunocompetent patients receiving Combo therapy had the best survival curve, and immunocompromised patients receiving non-Combo therapy had the worst survival curve. Combo therapy ≥14 days resulted in a better outcome for both immunocompromised and immunocompetent patients. In conclusion, CMV GI diseases affect both immunocompromised and immunocompetent hosts, and a complete treatment course should be considered for patients with poor prognostic factors.
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Visaggi P, de Bortoli N, Barberio B, Savarino V, Oleas R, Rosi EM, Marchi S, Ribolsi M, Savarino E. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. J Clin Gastroenterol 2022; 56:23-35. [PMID: 34739406 PMCID: PMC9988236 DOI: 10.1097/mcg.0000000000001629] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Brigida Barberio
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
| | - Vincenzo Savarino
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, Genoa
| | - Roberto Oleas
- Ecuadorean Institute of Digestive Diseases, Guayaquil, Ecuador
| | - Emma M. Rosi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Santino Marchi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Mentore Ribolsi
- Department of Digestive Diseases, Campus Bio Medico University of Rome, Roma, Italy
| | - Edoardo Savarino
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
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