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Molena Seraphim D, Camargo Guassu RA, Alvarez M, Bannwart Mendes M, Tasca KI, Naime Barbosa A, Vacavant A, Castelo Branco Fortaleza CM, Rodrigues de Pina D. Prognostic implications of regional lung impairment evaluation in quantitative computed tomography imaging of COVID-19. Clin Radiol 2025; 81:106779. [PMID: 39793302 DOI: 10.1016/j.crad.2024.106779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2024] [Revised: 11/23/2024] [Accepted: 12/12/2024] [Indexed: 01/13/2025]
Abstract
AIM To enhance the understanding of COVID-19 regional lung damage pattern by analyzing the organ in subregions, beyond the typical lobe segmentation. MATERIALS AND METHODS This study used semiautomatic computed tomography (CT) imaging segmentation and quantification to investigate regional lung impairments in patients with COVID-19. Each lung was divided into 12 regions, and the anatomical impairments obtained from the CT image (emphysema, ground glass opacity, and collapsed tissue) were quantified. Then, the results for every region were correlated with clinical outcomes. This research encompassed 333 individuals, both COVID positive (n = 190) and COVID negative (n = 143), whose medical reports were checked for the need for ventilatory support and outcome (cure or deceased). RESULTS Findings indicate a strong association between the extent of lung damage and COVID-19 diagnosis, the level of ventilatory assistance required, and patient survival rates. Notably, the medial posterior lung region exhibited increased opacities and collapse in COVID-positive patients (p < 0.05), particularly those requiring invasive ventilation or who succumbed to the illness. CONCLUSION The results expand the knowledge of COVID-19 regional impact beyond typical lobe segmentation and indicate that COVID-19 impairments in the lungs are localized. The most affected region identified was the medial posterior of both right and left lungs. Early detection of quantifiable lung damage can serve as a valuable prognostic tool, helping to pinpoint patients at heightened risk of severe complications or mortality.
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Affiliation(s)
- D Molena Seraphim
- São Paulo State University, Institute of Biosciences, Botucatu, Brazil
| | | | - M Alvarez
- São Paulo State University, Institute of Biosciences, Botucatu, Brazil
| | - M Bannwart Mendes
- São Paulo State University (UNESP), Medical School, Botucatu, Brazil
| | - K I Tasca
- São Paulo State University (UNESP), Medical School, Botucatu, Brazil
| | - A Naime Barbosa
- São Paulo State University (UNESP), Medical School, Botucatu, Brazil
| | - A Vacavant
- Institut Universitaire de Technologie, Le Puy en Velay, France
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Ahmed IA, Kharboush TG, Al-Amodi HS, Kamel HFM, Darwish E, Mosbeh A, Galbt HA, Abdel-Kareim AM, Abdelsattar S. Interleukin-1 Beta rs16944 and rs1143634 and Interleukin-6 Receptor rs12083537 Single Nucleotide Polymorphisms as Potential Predictors of COVID-19 Severity. Pathogens 2024; 13:915. [PMID: 39452786 PMCID: PMC11510688 DOI: 10.3390/pathogens13100915] [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: 08/23/2024] [Revised: 10/08/2024] [Accepted: 10/15/2024] [Indexed: 10/26/2024] Open
Abstract
Host genetic variation has been recognized as a key predictor of diverse clinical sequelae among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients. Insights into the link between the Interleukin-6 receptor (IL-6R) and Interleukin-1 beta (IL-1β) genetic variation and severe coronavirus disease 2019 (COVID-19) are crucial for developing new predictors and therapeutic targets. We aimed to investigate the association of IL-6R rs12083537, IL-1β rs16944, and IL-1β rs1143634 SNPs with the severity of COVID-19. Our study was conducted on 300 COVID-19-negative individuals (control group) and 299 COVID-19-positive cases, classified into mild, moderate, and severe subgroups. Analyses of IL-1β (rs16944, rs1143634) and IL-6R (rs12083537) SNPs' genotypes were performed using qPCR genotyping assays. The IL-1β (rs16944) CC genotype and IL-6R (rs12083537) GG genotype were substantially related to COVID-19 severity, which was also associated with comorbidities and some laboratory parameters (p < 0.001). The IL-1β (rs1143634) TT genotype was found to be protective. Likewise, the IL-1β (rs16944) CC genotype was associated with increased mortality. IL-1β rs16944 and IL-6R rs12083537 SNPs are promising potential predictors of SARS-CoV-2 disease severity. Meanwhile, the rs1143634 SNP T allele was protective against severity and mortality risk.
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Affiliation(s)
- Inas A. Ahmed
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Benha University, Benha 13518, Egypt
- Central Laboratory for Research, Faculty of Medicine, Benha University, Benha 13518, Egypt
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Benha National University, El-Obour 11828, Egypt
| | - Taghrid G. Kharboush
- Department of Medical Microbiology and Immunology, Faculty of Medicine, Benha University, Benha 13518, Egypt;
| | - Hiba S. Al-Amodi
- Department of Biochemistry, Faculty of Medicine, Umm Al-Qura University, P.O. Box 715, Makkah 21955, Saudi Arabia; (H.S.A.-A.); (H.F.M.K.)
| | - Hala F. M. Kamel
- Department of Biochemistry, Faculty of Medicine, Umm Al-Qura University, P.O. Box 715, Makkah 21955, Saudi Arabia; (H.S.A.-A.); (H.F.M.K.)
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Ain Shams University, Cairo 11566, Egypt
| | - Ehab Darwish
- Department of Tropical Medicine, Faculty of Medicine, Zagazig University, Zagazig 44511, Egypt;
- Department of Internal Medicine, Faculty of Medicine, King Faisal University, AI-Ahsa 31982, Saudi Arabia
| | - Asmaa Mosbeh
- Department of Pathology, National Liver Institute, Menoufia University, Menoufia 32511, Egypt;
| | - Hossam A. Galbt
- Department of Clinical Pathology, National Liver Institute, Menoufia University, Menoufia 32511, Egypt;
| | - Amal M. Abdel-Kareim
- Department of Zoology, Faculty of Science, Benha University, Benha 13518, Egypt;
| | - Shimaa Abdelsattar
- Department of Clinical Biochemistry and Molecular Diagnostics, National Liver Institute, Menoufia University, Menoufia 32511, Egypt;
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Jiang X, Hu J, Jiang Q, Zhou T, Yao F, Sun Y, Liu Q, Zhou C, Shi K, Lin X, Li J, Li Y, Jin Q, Tu W, Zhou X, Wang Y, Xin X, Liu S, Fan L. Lung field-based severity score (LFSS): a feasible tool to identify COVID-19 patients at high risk of progressing to critical disease. J Thorac Dis 2024; 16:5591-5603. [PMID: 39444869 PMCID: PMC11494559 DOI: 10.21037/jtd-24-544] [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: 04/02/2024] [Accepted: 07/12/2024] [Indexed: 10/25/2024]
Abstract
Background Coronavirus disease 2019 (COVID-19) still poses a threat to people's physical and mental health. We proposed a new semi-quantitative visual classification method for COVID-19, and this study aimed to evaluate the clinical usefulness and feasibility of lung field-based severity score (LFSS). Methods This retrospective study included 794 COVID-19 patients from two hospitals in China between December 2022 and January 2023. Six lung fields on the axial computed tomography (CT) were defined. LFSS and eighteen clinical characteristics were evaluated. LFSS was based on summing up the parenchymal opacification involving each lung field, which was scored as 0 (0%), 1 (1-24%), 2 (25-49%), 3 (50-74%), or 4 (75-100%), respectively (range of LFSS from 0 to 24). Total pneumonia burden (TPB) was calculated using the U-net model. The correlation between LFSS and TPB was analyzed. After performing logistic regression analysis, an LFSS-based model, clinical-based model and combined model were developed. Receiver operating characteristic curves were used to evaluate and compare the performance of three models. Results LFSS, age, chronic liver disease, chronic kidney disease, white blood cell, neutrophils, lymphocytes and C-reactive protein differed significantly between the non-critical and critical group (all P<0.05). There was a strong positive correlation of LFSS and TPB (Pearson correlation coefficient =0.767, P<0.001). The area under curves of LFSS-based model, clinical-based model and combined model were 0.799 [95% confidence interval (CI): 0.770-0.827], 0.758 (95% CI: 0.727-0.788), and 0.848 (95% CI: 0.821-0.872), respectively. Conclusions The LFSS derived from chest CT may be a potential new tool to help identify COVID-19 patients at high risk of progressing to critical disease.
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Affiliation(s)
- Xin’ang Jiang
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Jun Hu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Qinling Jiang
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Taohu Zhou
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
- School of Medical Imaging, Weifang Medical University, Weifang, China
| | - Fei Yao
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
- School of Medicine, Shanghai University, Shanghai, China
| | - Yi Sun
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Qingyang Liu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Chao Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Kang Shi
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiaoqing Lin
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Jie Li
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yueze Li
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Qianxi Jin
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Wenting Tu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Xiuxiu Zhou
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yun Wang
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Xiaoyan Xin
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Shiyuan Liu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Li Fan
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
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Khanam SJ, Rana MS, Islam MM, Khan MN. COVID-19 vaccine uptake in individuals with functional difficulty, disability, and comorbid conditions: insights from a national survey in Bangladesh. BMC Public Health 2024; 24:2531. [PMID: 39289678 PMCID: PMC11409621 DOI: 10.1186/s12889-024-20096-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 09/16/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND COVID-19 vaccine uptake among individuals with disabilities is crucial for safeguarding their health and well-being. However, the extent of vaccine uptake among this group remains largely unknown in low- and middle-income countries. This study aims to assess the COVID-19 vaccine uptake among persons with functional difficulty, disability and/or comorbidity in Bangladesh and their associated factors. METHODS Data from 9,370 respondents extracted from the 2021 National Household Survey on Persons with Disability were analysed. The outcome variable was the uptake of at least one dose of the COVID-19 vaccine (yes, no). Key explanatory variables included the presence of disability (yes, no), comorbidity (yes, no), and both comorbidity and disability (yes, no) among persons with functional difficulty. The relationship between the outcome and explanatory variables was determined using mixed-effects multilevel logistic regressions adjusted for covariates. RESULTS The overall uptake of at least one dose of the COVID-19 vaccine among persons with functional difficulty was 57.37%, among persons with functional difficulty and disability was 48.63% and among persons with functional difficulty and single (57.85%) or multi-comorbidity (60.37%). Compared to the respondents with functional difficulty only, the adjusted odds ratio (aOR) of not receiving any dose of the COVID-19 vaccine for individuals with both functional difficulty and disability was 1.37 (95% CI, 1.22-1.53), and for individuals with functional difficulty, disability and one or more comorbid conditions was 1.30 (95% CI, 1.15-1.47). The aOR of receiving at least one dose of the COVID-19 vaccine among individuals with functional difficulty and one or more comorbid conditions was significantly higher than among those with functional difficulty only. CONCLUSION In Bangladesh, COVID-19 vaccine uptake was relatively low among individuals with disabilities. The existing COVID-19 vaccine rollout programs and similar future programs should prioritise individuals with disabilities and include targeted strategies to reach them.
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Affiliation(s)
- Shimlin Jahan Khanam
- Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2220, Bangladesh
| | - Md Shohel Rana
- Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2220, Bangladesh
| | - M Mofizul Islam
- Department of Public Health, La Trobe University, Melbourne, 3086, Australia
| | - Md Nuruzzaman Khan
- Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2220, Bangladesh.
- Nossal Institute for Global Health, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
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Nardi C, Magnini A, Rastrelli V, Zantonelli G, Calistri L, Lorini C, Luzzi V, Gori L, Ciani L, Morecchiato F, Simonetti V, Peired AJ, Landini N, Cavigli E, Yang G, Guiot J, Tomassetti S, Colagrande S. Laboratory data and broncho-alveolar lavage on Covid-19 patients with no intensive care unit admission: Correlation with chest CT features and clinical outcomes. Medicine (Baltimore) 2024; 103:e39028. [PMID: 39029011 PMCID: PMC11398758 DOI: 10.1097/md.0000000000039028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 07/01/2024] [Indexed: 07/21/2024] Open
Abstract
Broncho-alveolar lavage (BAL) is indicated in cases of uncertain diagnosis but high suspicion of Sars-Cov-2 infection allowing to collect material for microbiological culture to define the presence of coinfection or super-infection. This prospective study investigated the correlation between chest computed tomography (CT) findings, Covid-19 Reporting and Data System score, and clinical outcomes in Coronavirus disease 2019 (Covid-19) patients who underwent BAL with the aim of predicting outcomes such as lung coinfection, respiratory failure, and hospitalization length based on chest CT abnormalities. Study population included 34 patients (range 38-90 years old; 20 males, 14 females) with a positive nucleic acid amplification test for Covid-19 infection, suitable BAL examination, and good quality chest CT scan in the absence of lung cancer history. Pulmonary coinfections were found in 20.6% of patients, predominantly caused by bacteria. Specific correlations were found between right middle lobe involvement and pulmonary co-infections. Severe lung injury (PaO2/FiO2 ratio of 100-200) was associated with substantial involvement of right middle, right upper, and left lower lobes. No significant correlation was found between chest CT findings and inflammatory markers (C-reactive protein, procalcitonin) or hospitalization length of stay. Specific chest CT patterns, especially in right middle lobe, could serve as indicators for the presence of co-infections and disease severity in noncritically ill Covid-19 patients, aiding clinicians in timely interventions and personalized treatment strategies.
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Affiliation(s)
- Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Andrea Magnini
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Vieri Rastrelli
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Giulia Zantonelli
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Linda Calistri
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Chiara Lorini
- Department of Health Science, University of Florence, Florence, Italy
| | - Valentina Luzzi
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Florence, Italy
| | - Leonardo Gori
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Florence, Italy
| | - Luca Ciani
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Florence, Italy
| | - Fabio Morecchiato
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- Clinical Microbiology and Virology Unit, Florence Careggi University Hospital, Florence, Italy
| | | | - Anna Julie Peired
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Florence, Italy
| | - Nicholas Landini
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I Hospital, “Sapienza” Rome University, Rome, Italy
| | - Edoardo Cavigli
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, London, UK
| | - Julien Guiot
- Department of Respiratory Medicine, University Hospital of Liège, Liège, Belgium
| | - Sara Tomassetti
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Florence, Italy
| | - Stefano Colagrande
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
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Liu X, Sun Y, Song H, Zhang W, Liu T, Chu Z, Gu X, Ma Z, Jin W. Nanomaterials-based electrochemical biosensors for diagnosis of COVID-19. Talanta 2024; 274:125994. [PMID: 38547841 DOI: 10.1016/j.talanta.2024.125994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 03/15/2024] [Accepted: 03/24/2024] [Indexed: 05/04/2024]
Abstract
Since the outbreak of corona virus disease 2019 (COVID-19), this pandemic has caused severe death and infection worldwide. Owing to its strong infectivity, long incubation period, and nonspecific symptoms, the early diagnosis is essential to reduce risk of the severe illness. The electrochemical biosensor, as a fast and sensitive technique for quantitative analysis of body fluids, has been widely studied to diagnose different biomarkers caused at different infective stages of COVID-19 virus (SARS-CoV-2). Recently, many reports have proved that nanomaterials with special architectures and size effects can effectively promote the biosensing performance on the COVID-19 diagnosis, there are few comprehensive summary reports yet. Therefore, in this review, we will pay efforts on recent progress of advanced nanomaterials-facilitated electrochemical biosensors for the COVID-19 detections. The process of SARS-CoV-2 infection in humans will be briefly described, as well as summarizing the types of sensors that should be designed for different infection processes. Emphasis will be supplied to various functional nanomaterials which dominate the biosensing performance for comparison, expecting to provide a rational guidance on the material selection of biosensor construction for people. Finally, we will conclude the perspective on the design of superior nanomaterials-based biosensors facing the unknown virus in future.
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Affiliation(s)
- Xinxin Liu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Yifan Sun
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Huaiyu Song
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Wei Zhang
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China
| | - Tao Liu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China.
| | - Zhenyu Chu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Xiaoping Gu
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China.
| | - Zhengliang Ma
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China
| | - Wanqin Jin
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China.
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Al-Momani H. A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis. Tomography 2024; 10:935-948. [PMID: 38921948 PMCID: PMC11209112 DOI: 10.3390/tomography10060071] [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: 04/04/2024] [Revised: 06/09/2024] [Accepted: 06/11/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19. METHOD A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost. FINDINGS In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review. CONCLUSION RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.
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Affiliation(s)
- Hafez Al-Momani
- Department of Microbiology, Pathology and Forensic Medicine, Faculty of Medicine, The Hashemite University, Zarqa 1133, Jordan
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Nimer NA, Nimer SN. Immunization against Medically Important Human Coronaviruses of Public Health Concern. THE CANADIAN JOURNAL OF INFECTIOUS DISEASES & MEDICAL MICROBIOLOGY = JOURNAL CANADIEN DES MALADIES INFECTIEUSES ET DE LA MICROBIOLOGIE MEDICALE 2024; 2024:9952803. [PMID: 38938549 PMCID: PMC11208815 DOI: 10.1155/2024/9952803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/14/2024] [Accepted: 05/20/2024] [Indexed: 06/29/2024]
Abstract
SARS-CoV-2 is a virus that affects the human immune system. It was observed to be on the rise since the beginning of 2020 and turned into a life-threatening pandemic. Scientists have tried to develop a possible preventive and therapeutic drug against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and other related coronaviruses by assessing COVID-19-recovered persons' immunity. This study aims to review immunization against SARS-CoV-2, along with exploring the interventions that have been developed for the prevention of SARS-CoV-2. This study also highlighted the role of phototherapy in treating SARS-CoV infection. The study adopted a review approach to gathering the information available and the progress that has been made in the treatment and prevention of COVID-19. Various vaccinations, including nucleotide, subunit, and vector-based vaccines, as well as attenuated and inactivated forms that have already been shown to have prophylactic efficacy against the Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV, have been summarized. Neutralizing and non-neutralizing antibodies are all associated with viral infections. Because there is no specific antiviral vaccine or therapies for coronaviruses, the main treatment strategy is supportive care, which is reinforced by combining broad-spectrum antivirals, convalescent plasma, and corticosteroids. COVID-19 has been a challenge to keep reconsidering the usual approaches to regulatory evaluation as a result of getting mixed and complicated findings on the vaccines, as well as licensing procedures. However, it is observed that medicinal herbs also play an important role in treating infection of the upper respiratory tract, the principal symptom of SARS-CoV due to their natural bioactive composite. However, some Traditional Chinese Medicines contain mutagens and nephrotoxins and the toxicological properties of the majority of Chinese herbal remedies are unknown. Therefore, to treat the COVID-19 infection along with conventional treatment, it is recommended that herb-drug interaction be examined thoroughly.
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Affiliation(s)
- Nabil A. Nimer
- Faculty of Pharmacy, Philadelphia University, Amman, Jordan
| | - Seema N. Nimer
- School of Medicine, The University of Jordan, Amman, Jordan
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Cheng FC, Li YH, Wei YF, Chen CJ, Chen MH, Chiang CP. The usage of dental cone-beam computed tomography during the COVID-19 pandemic (from 2020 to 2022): A survey of a regional hospital in the northern Taiwan. J Dent Sci 2024; 19:795-803. [PMID: 38618131 PMCID: PMC11010694 DOI: 10.1016/j.jds.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Indexed: 04/16/2024] Open
Abstract
Background/purpose In Taiwan, cone-beam computed tomography (CBCT) has already widely used in dentistry. This study explored preliminarily the usage of dental CBCT during the COVID-19 pandemic (from 2020 to 2022) through a survey of a regional hospital in the northern Taiwan. Materials and methods This study used purposeful sampling to select a regional hospital in the northern Taiwan to survey its usage of dental CBCT during the COVID-19 pandemic. Results In the surveyed hospital, the number of patients' visits for the usage of dental CBCT increased from 355 in 2020 to 449 in 2021 and further to 488 in 2022 with a growth rate of 37.46 %, while the growth rates compared to the previous year were 26.48 % in 2021 and 8.69 % in 2022, respectively. There were a total of 1292 patients' visits for the dental CBCT. The ages of the 1292 patients (573 males and 719 females) ranged from 4 to 89 years. The 50-59-year age group had the highest number of patients' visits (371, 28.72 %), followed in a descending order by the 60-69-year (293, 22.68 %) and 40-49-year (206, 15.94 %) age groups. The dental CBCT was used mainly for the assessment of dental implants, accounting for 1148 (78.85 %) of the total 1456 irradiations. Conclusion During the COVID-19 pandemic, the medical services for dental care and treatments in Taiwan are still maintained normally, and the dental CBCT is also used widely and popularly by the dental patients of all ages, various dental procedures, and various dental specialties.
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Affiliation(s)
- Feng-Chou Cheng
- Chia-Te Dental Clinic, New Taipei City, Taiwan
- School of Life Science, College of Science, National Taiwan Normal University, Taipei, Taiwan
- Science Education Center, National Taiwan Normal University, Taipei, Taiwan
| | - Yu-Hung Li
- Department of Radiology Technology, Lotung Poh-Ai Hospital, Yilan, Taiwan
| | - Yuh-Fen Wei
- Department of Medical Imaging, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu County, Taiwan
| | - Chien-Jung Chen
- Department of Nuclear Medicine, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Mu-Hsiung Chen
- Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chun-Pin Chiang
- Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Clinical Dentistry, School of Dentistry, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Oral Biology, School of Dentistry, National Taiwan University, Taipei, Taiwan
- Department of Dentistry, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
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10
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Crombé A, Lecomte JC, Seux M, Banaste N, Gorincour G. Using the Textual Content of Radiological Reports to Detect Emerging Diseases: A Proof-of-Concept Study of COVID-19. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:620-632. [PMID: 38343242 PMCID: PMC11031522 DOI: 10.1007/s10278-023-00949-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/02/2023] [Accepted: 10/04/2023] [Indexed: 04/20/2024]
Abstract
Changes in the content of radiological reports at population level could detect emerging diseases. Herein, we developed a method to quantify similarities in consecutive temporal groupings of radiological reports using natural language processing, and we investigated whether appearance of dissimilarities between consecutive periods correlated with the beginning of the COVID-19 pandemic in France. CT reports from 67,368 consecutive adults across 62 emergency departments throughout France between October 2019 and March 2020 were collected. Reports were vectorized using time frequency-inverse document frequency (TF-IDF) analysis on one-grams. For each successive 2-week period, we performed unsupervised clustering of the reports based on TF-IDF values and partition-around-medoids. Next, we assessed the similarities between this clustering and a clustering from two weeks before according to the average adjusted Rand index (AARI). Statistical analyses included (1) cross-correlation functions (CCFs) with the number of positive SARS-CoV-2 tests and advanced sanitary index for flu syndromes (ASI-flu, from open-source dataset), and (2) linear regressions of time series at different lags to understand the variations of AARI over time. Overall, 13,235 chest CT reports were analyzed. AARI was correlated with ASI-flu at lag = + 1, + 5, and + 6 weeks (P = 0.0454, 0.0121, and 0.0042, respectively) and with SARS-CoV-2 positive tests at lag = - 1 and 0 week (P = 0.0057 and 0.0001, respectively). In the best fit, AARI correlated with the ASI-flu with a lag of 2 weeks (P = 0.0026), SARS-CoV-2-positive tests in the same week (P < 0.0001) and their interaction (P < 0.0001) (adjusted R2 = 0.921). Thus, our method enables the automatic monitoring of changes in radiological reports and could help capturing disease emergence.
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Affiliation(s)
- Amandine Crombé
- IMADIS, Lyon, France.
- SARCOTARGET Team, University of Bordeaux, Inserm, UMR1312, BRIC, BoRdeaux Institute of Oncology, 146 Rue Léo Saignat, Bordeaux, F-33076, France.
- Department of Radiology, Pellegrin University Hospital, CHU Bordeaux, Place Amélie Raba-Léon, Bordeaux, F-33076, France.
| | - Jean-Christophe Lecomte
- IMADIS, Lyon, France
- Centre Aquitain d'Imagerie médicale, Mérignac, France
- Centre Hospitalier de Saintes, Saintes, France
- Clinique Mutualiste Bordeaux Pessac, Pessac, France
| | | | - Nathan Banaste
- IMADIS, Lyon, France
- Clinique Convert, Ramsay, Bourg en Bresse, France
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11
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Mjokane N, Akintemi EO, Sabiu S, Gcilitshana OMN, Albertyn J, Pohl CH, Sebolai OM. Aspergillus fumigatus secretes a protease(s) that displays in silico binding affinity towards the SARS-CoV-2 spike protein and mediates SARS-CoV-2 pseudovirion entry into HEK-293T cells. Virol J 2024; 21:58. [PMID: 38448991 PMCID: PMC10919004 DOI: 10.1186/s12985-024-02331-z] [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: 08/15/2023] [Accepted: 02/27/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND The novel coronavirus disease of 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Data from the COVID-19 clinical control case studies showed that this disease could also manifest in patients with underlying microbial infections such as aspergillosis. The current study aimed to determine if the Aspergillus (A.) fumigatus culture media (i.e., supernatant) possessed protease activity that was sufficient to activate the SARS-CoV-2 spike protein. METHODS The supernatant was first analysed for protease activity. Thereafter, it was assessed to determine if it possessed proteolytic activity to cleave a fluorogenic mimetic peptide of the SARS-CoV-2 spike protein that contained the S1/S2 site and a full-length spike protein contained in a SARS-CoV-2 pseudovirion. To complement this, a computer-based tool, HADDOCK, was used to predict if A. fumigatus alkaline protease 1 could bind to the SARS-CoV-2 spike protein. RESULTS We show that the supernatant possessed proteolytic activity, and analyses of the molecular docking parameters revealed that A. fumigatus alkaline protease 1 could bind to the spike protein. To confirm the in silico data, it was imperative to provide experimental evidence for enzymatic activity. Here, it was noted that the A. fumigatus supernatant cleaved the mimetic peptide as well as transduced the HEK-293T cells with SARS-CoV-2 pseudovirions. CONCLUSION These results suggest that A. fumigatus secretes a protease(s) that activates the SARS-CoV-2 spike protein. Importantly, should these two infectious agents co-occur, there is the potential for A. fumigatus to activate the SARS-CoV-2 spike protein, thus aggravating COVID-19 development.
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Affiliation(s)
- Nozethu Mjokane
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Eric O Akintemi
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Saheed Sabiu
- Department of Biotechnology and Food Science, Faculty of Applied Science, Durban University of Technology, 4000, Durban, P.O. Box 1334, South Africa
| | - Onele M N Gcilitshana
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Jacobus Albertyn
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Carolina H Pohl
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Olihile M Sebolai
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa.
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12
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Sarmini, Lailiyah F, Suprapto, Faidah M. The important issue of awareness of disaster response to the COVID-19. Heliyon 2024; 10:e23880. [PMID: 38226289 PMCID: PMC10788434 DOI: 10.1016/j.heliyon.2023.e23880] [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: 08/27/2021] [Revised: 12/04/2023] [Accepted: 12/14/2023] [Indexed: 01/17/2024] Open
Abstract
This study aimed to analyze the community's disaster response awareness during the Covid-19 pandemic during the implementation of The Large-Scale Social Restrictions (LSSR) in Gresik. Self-awareness was observed using Peter L. Berger and Thomas Luck man's Social Construction Theory through a dialectical process of internalization, objectification, and externalization. The results showed that there had been no good awareness in efforts to prevent the spread of Covid-19 in Gresik. Socially, the community had not taken the dangers of Covid-19 seriously. There was also an inconsistency between knowledge and reality related to disaster response. In coping with the dialectical process, the community had not implemented a disaster-aware culture by obeying the existing regulations. At least, the sociocultural environment determined a person's construction for self-identification, interaction, and adjustment to the social changes that occurred. Hence, the sociocultural construction of the community had never made the disease outbreak a serious problem and considered it as well as God's reminder even though infected cases continued to increase. A situation was indeed difficult for the Task Force to succeed in the Large-Scale Social Restrictions (LSSR) to Community Activities Restrictions Enforcement (CARE) suppressed the increase of Covid-19 positive cases in Gresik, Indonesia. This research sees that the government's policies in handling Covid- 19 are not enough, it needs more optimal involvement of community and religious leaders, to provide education on the importance of maintaining health protocols and building self-awareness of the dangers of Covid.
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Affiliation(s)
- Sarmini
- Universitas Negeri Surabaya, Indonesia
| | | | - Suprapto
- Universitas Negeri Surabaya, Indonesia
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13
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Kataoka Y, Tanabe N, Shirata M, Hamao N, Oi I, Maetani T, Shiraishi Y, Hashimoto K, Yamazoe M, Shima H, Ajimizu H, Oguma T, Emura M, Endo K, Hasegawa Y, Mio T, Shiota T, Yasui H, Nakaji H, Tsuchiya M, Tomii K, Hirai T, Ito I. Artificial intelligence-based analysis of the spatial distribution of abnormal computed tomography patterns in SARS-CoV-2 pneumonia: association with disease severity. Respir Res 2024; 25:24. [PMID: 38200566 PMCID: PMC10777587 DOI: 10.1186/s12931-024-02673-w] [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: 09/11/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND The substantial heterogeneity of clinical presentations in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia still requires robust chest computed tomography analysis to identify high-risk patients. While extension of ground-glass opacity and consolidation from peripheral to central lung fields on chest computed tomography (CT) might be associated with severely ill conditions, quantification of the central-peripheral distribution of ground glass opacity and consolidation in assessments of SARS-CoV-2 pneumonia remains unestablished. This study aimed to examine whether the central-peripheral distributions of ground glass opacity and consolidation were associated with severe outcomes in patients with SARS-CoV-2 pneumonia independent of the whole-lung extents of these abnormal shadows. METHODS This multicenter retrospective cohort included hospitalized patients with SARS-CoV-2 pneumonia between January 2020 and August 2021. An artificial intelligence-based image analysis technology was used to segment abnormal shadows, including ground glass opacity and consolidation. The area ratio of ground glass opacity and consolidation to the whole lung (GGO%, CON%) and the ratio of ground glass opacity and consolidation areas in the central lungs to those in the peripheral lungs (GGO(C/P)) and (CON(C/P)) were automatically calculated. Severe outcome was defined as in-hospital death or requirement for endotracheal intubation. RESULTS Of 512 enrolled patients, the severe outcome was observed in 77 patients. GGO% and CON% were higher in patients with severe outcomes than in those without. Multivariable logistic models showed that GGO(C/P), but not CON(C/P), was associated with the severe outcome independent of age, sex, comorbidities, GGO%, and CON%. CONCLUSION In addition to GGO% and CON% in the whole lung, the higher the ratio of ground glass opacity in the central regions to that in the peripheral regions was, the more severe the outcomes in patients with SARS-CoV-2 pneumonia were. The proposed method might be useful to reproducibly quantify the extension of ground glass opacity from peripheral to central lungs and to estimate prognosis.
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Affiliation(s)
- Yusuke Kataoka
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
| | - Masahiro Shirata
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Nobuyoshi Hamao
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
- Department of Internal Medicine, Sugita Genpaku Memorial Obama Municipal Hospital, Obama, Japan
| | - Issei Oi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
- Department of Internal Medicine, Sugita Genpaku Memorial Obama Municipal Hospital, Obama, Japan
| | - Tomoki Maetani
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yusuke Shiraishi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Kentaro Hashimoto
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Masatoshi Yamazoe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Hiroshi Shima
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Hitomi Ajimizu
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Tsuyoshi Oguma
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
- Department of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan
| | - Masahito Emura
- Department of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan
| | - Kazuo Endo
- Department of Respiratory Medicine, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki, Japan
| | - Yoshinori Hasegawa
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Tadashi Mio
- Division of Respiratory Medicine, Center for Respiratory Diseases, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | | | - Hiroaki Yasui
- Department of Internal Medicine, Horikawa Hospital, Kyoto, Japan
| | - Hitoshi Nakaji
- Department of Respiratory Medicine, Toyooka Hospital, Toyooka, Japan
| | - Michiko Tsuchiya
- Department of Respiratory Medicine, Rakuwakai Otowa Hospital, Kyoto, Japan
| | - Keisuke Tomii
- Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Isao Ito
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
- Department of Internal Medicine, Sugita Genpaku Memorial Obama Municipal Hospital, Obama, Japan.
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14
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Staudner ST, Leininger SB, Vogel MJ, Mustroph J, Hubauer U, Meindl C, Wallner S, Lehn P, Burkhardt R, Hanses F, Zimmermann M, Scharf G, Hamer OW, Maier LS, Hupf J, Jungbauer CG. Dipeptidyl-peptidase 3 and IL-6: potential biomarkers for diagnostics in COVID-19 and association with pulmonary infiltrates. Clin Exp Med 2023; 23:4919-4935. [PMID: 37733154 PMCID: PMC10725357 DOI: 10.1007/s10238-023-01193-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/08/2023] [Indexed: 09/22/2023]
Abstract
Coronavirus SARS-CoV-2 spread worldwide, causing a respiratory disease known as COVID-19. The aim of the present study was to examine whether Dipeptidyl-peptidase 3 (DPP3) and the inflammatory biomarkers IL-6, CRP, and leucocytes are associated with COVID-19 and able to predict the severity of pulmonary infiltrates in COVID-19 patients versus non-COVID-19 patients. 114 COVID-19 patients and 35 patients with respiratory infections other than SARS-CoV-2 were included in our prospective observational study. Blood samples were collected at presentation to the emergency department. 102 COVID-19 patients and 28 non-COVID-19 patients received CT imaging (19 outpatients did not receive CT imaging). If CT imaging was available, artificial intelligence software (CT Pneumonia Analysis) was used to quantify pulmonary infiltrates. According to the median of infiltrate (14.45%), patients who obtained quantitative CT analysis were divided into two groups (> median: 55 COVID-19 and nine non-COVID-19, ≤ median: 47 COVID-19 and 19 non-COVID-19). DPP3 was significantly elevated in COVID-19 patients (median 20.85 ng/ml, 95% CI 18.34-24.40 ng/ml), as opposed to those without SARS-CoV-2 (median 13.80 ng/ml, 95% CI 11.30-17.65 ng/ml; p < 0.001, AUC = 0.72), opposite to IL-6, CRP (each p = n.s.) and leucocytes (p < 0.05, but lower levels in COVID-19 patients). Regarding binary logistic regression analysis, higher DPP3 concentrations (OR = 1.12, p < 0.001) and lower leucocytes counts (OR = 0.76, p < 0.001) were identified as significant and independent predictors of SARS-CoV-2 infection, as opposed to IL-6 and CRP (each p = n.s.). IL-6 was significantly increased in patients with infiltrate above the median compared to infiltrate below the median both in COVID-19 (p < 0.001, AUC = 0.78) and in non-COVID-19 (p < 0.05, AUC = 0.81). CRP, DPP3, and leucocytes were increased in COVID-19 patients with infiltrate above median (each p < 0.05, AUC: CRP 0.82, DPP3 0.70, leucocytes 0.67) compared to infiltrate below median, opposite to non-COVID-19 (each p = n.s.). Regarding multiple linear regression analysis in COVID-19, CRP, IL-6, and leucocytes (each p < 0.05) were associated with the degree of pulmonary infiltrates, as opposed to DPP3 (p = n.s.). DPP3 showed the potential to be a COVID-19-specific biomarker. IL-6 might serve as a prognostic marker to assess the extent of pulmonary infiltrates in respiratory patients.
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Affiliation(s)
- Stephan T Staudner
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany.
| | - Simon B Leininger
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Manuel J Vogel
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Julian Mustroph
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Ute Hubauer
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Christine Meindl
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Stefan Wallner
- Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Petra Lehn
- Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Ralph Burkhardt
- Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Frank Hanses
- Emergency Department, University Hospital Regensburg, Regensburg, Germany
- Department of Infection Prevention and Infectious Diseases, University Hospital Regensburg, Regensburg, Germany
| | - Markus Zimmermann
- Emergency Department, University Hospital Regensburg, Regensburg, Germany
| | - Gregor Scharf
- Department of Radiology, University Hospital Regensburg, Regensburg, Germany
| | - Okka W Hamer
- Department of Radiology, University Hospital Regensburg, Regensburg, Germany
| | - Lars S Maier
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Julian Hupf
- Emergency Department, University Hospital Regensburg, Regensburg, Germany
| | - Carsten G Jungbauer
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
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15
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Nardi C, Magnini A, Calistri L, Cavigli E, Peired AJ, Rastrelli V, Carlesi E, Zantonelli G, Smorchkova O, Cinci L, Orlandi M, Landini N, Berillo E, Lorini C, Mencarini J, Colao MG, Gori L, Luzzi V, Lazzeri C, Cipriani E, Bonizzoli M, Pieralli F, Nozzoli C, Morettini A, Lavorini F, Bartoloni A, Rossolini GM, Matucci-Cerinic M, Tomassetti S, Colagrande S. Doubts and concerns about COVID-19 uncertainties on imaging data, clinical score, and outcomes. BMC Pulm Med 2023; 23:472. [PMID: 38007479 PMCID: PMC10675953 DOI: 10.1186/s12890-023-02763-3] [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: 08/10/2023] [Accepted: 11/15/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND COVID-19 is a pandemic disease affecting predominantly the respiratory apparatus with clinical manifestations ranging from asymptomatic to respiratory failure. Chest CT is a crucial tool in diagnosing and evaluating the severity of pulmonary involvement through dedicated scoring systems. Nonetheless, many questions regarding the relationship of radiologic and clinical features of the disease have emerged in multidisciplinary meetings. The aim of this retrospective study was to explore such relationship throughout an innovative and alternative approach. MATERIALS AND METHODS This study included 550 patients (range 25-98 years; 354 males, mean age 66.1; 196 females, mean age 70.9) hospitalized for COVID-19 with available radiological and clinical data between 1 March 2021 and 30 April 2022. Radiological data included CO-RADS, chest CT score, dominant pattern, and typical/atypical findings detected on CT examinations. Clinical data included clinical score and outcome. The relationship between such features was investigated through the development of the main four frequently asked questions summarizing the many issues arisen in multidisciplinary meetings, as follows 1) CO-RADS, chest CT score, clinical score, and outcomes; 2) the involvement of a specific lung lobe and outcomes; 3) dominant pattern/distribution and severity score for the same chest CT score; 4) additional factors and outcomes. RESULTS 1) If CT was suggestive for COVID, a strong correlation between CT/clinical score and prognosis was found; 2) Middle lobe CT involvement was an unfavorable prognostic criterion; 3) If CT score < 50%, the pattern was not influential, whereas if CT score > 50%, crazy paving as dominant pattern leaded to a 15% increased death rate, stacked up against other patterns, thus almost doubling it; 4) Additional factors usually did not matter, but lymph-nodes and pleural effusion worsened prognosis. CONCLUSIONS This study outlined those radiological features of COVID-19 most relevant towards disease severity and outcome with an innovative approach.
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Affiliation(s)
- Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Andrea Magnini
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Linda Calistri
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Edoardo Cavigli
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Anna Julie Peired
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Vieri Rastrelli
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Edoardo Carlesi
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Giulia Zantonelli
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Olga Smorchkova
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Lorenzo Cinci
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Martina Orlandi
- Department of Experimental and Clinical Medicine, Division of Rheumatology, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Nicholas Landini
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I Hospital, "Sapienza" Rome University, Rome, Italy
| | - Edoardo Berillo
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Chiara Lorini
- Department of Health Sciences, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Jessica Mencarini
- Department of Experimental and Clinical Medicine, Infectious and Tropical Diseases Unit, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Maria Grazia Colao
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
- Clinical Microbiology and Virology Unit, Florence Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Leonardo Gori
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Valentina Luzzi
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Chiara Lazzeri
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Elisa Cipriani
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Manuela Bonizzoli
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Filippo Pieralli
- Intermediate Care Unit, University Hospital Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Carlo Nozzoli
- Internal Medicine Unit 1, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Alessandro Morettini
- Internal Medicine Unit 2, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Federico Lavorini
- Department of Experimental and Clinical Medicine, Division of Pulmonology, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Alessandro Bartoloni
- Department of Experimental and Clinical Medicine, Infectious and Tropical Diseases Unit, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Gian Maria Rossolini
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
- Clinical Microbiology and Virology Unit, Florence Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Marco Matucci-Cerinic
- Department of Experimental and Clinical Medicine, Division of Rheumatology, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Sara Tomassetti
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Stefano Colagrande
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
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16
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Chu WT, Castro MA, Reza S, Cooper TK, Bartlinski S, Bradley D, Anthony SM, Worwa G, Finch CL, Kuhn JH, Crozier I, Solomon J. Novel machine-learning analysis of SARS-CoV-2 infection in a subclinical nonhuman primate model using radiomics and blood biomarkers. Sci Rep 2023; 13:19607. [PMID: 37950044 PMCID: PMC10638262 DOI: 10.1038/s41598-023-46694-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023] Open
Abstract
Detection of the physiological response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is challenging in the absence of overt clinical signs but remains necessary to understand a full subclinical disease spectrum. In this study, our objective was to use radiomics (from computed tomography images) and blood biomarkers to predict SARS-CoV-2 infection in a nonhuman primate model (NHP) with inapparent clinical disease. To accomplish this aim, we built machine-learning models to predict SARS-CoV-2 infection in a NHP model of subclinical disease using baseline-normalized radiomic and blood sample analyses data from SARS-CoV-2-exposed and control (mock-exposed) crab-eating macaques. We applied a novel adaptation of the minimum redundancy maximum relevance (mRMR) feature-selection technique, called mRMR-permute, for statistically-thresholded and unbiased feature selection. Through performance comparison of eight machine-learning models trained on 14 feature sets, we demonstrated that a logistic regression model trained on the mRMR-permute feature set can predict SARS-CoV-2 infection with very high accuracy. Eighty-nine percent of mRMR-permute selected features had strong and significant class effects. Through this work, we identified a key set of radiomic and blood biomarkers that can be used to predict infection status even in the absence of clinical signs. Furthermore, we proposed and demonstrated the utility of a novel feature-selection technique called mRMR-permute. This work lays the foundation for the prediction and classification of SARS-CoV-2 disease severity.
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Affiliation(s)
- Winston T Chu
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Marcelo A Castro
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Syed Reza
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Timothy K Cooper
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Sean Bartlinski
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Dara Bradley
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Scott M Anthony
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Gabriella Worwa
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Courtney L Finch
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Jens H Kuhn
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Ian Crozier
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Jeffrey Solomon
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
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17
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Cheng FC, Tang LH, Lee KJ, Wei YF, Liu BL, Chen MH, Chiang CP. Online courses for dentist continuing education: A new trend after the COVID-19 pandemic. J Dent Sci 2023; 18:1812-1821. [PMID: 37795131 PMCID: PMC10307533 DOI: 10.1016/j.jds.2023.06.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Indexed: 10/06/2023] Open
Abstract
Background/purpose Online courses have been widely used in all levels of education during the COVID-19 pandemic. This study explored the effectiveness of a dentist continuing education (DCE) course through the online devices in Taiwan. Materials and methods The practicing dentists who participated in the online course of dental radiation technology for DCE offered by the Taiwan Dental Association (TWDA) in October 2022 and in March 2023 were enrolled in this study. The composition of participating dentists was confirmed by the public inquiry system and their learning effectiveness was evaluated by a questionnaire-based survey after the online DCE class. Results All participating dentists (132 in October 2022 and 117 in March 2023) obtained consistent good learning outcomes in this online DCE course. Of these 249 dentists, there were 170 (68.27%) males and 79 (31.73%) females, 127 (51.00%) dental specialists and 122 (49.00%) general dentists, as well as 50 (20.08%) hospital dentists and 199 (79.92%) clinic dentists. The participation rates for this course of practicing dentists in non-municipalities (4.70%), counties (3.88%), eastern region (8.08%), and outlying islands (3.60%) were much higher than those in municipalities (0.79%), cities (1.16%), and the western region including the northern region (0.88%), central region (1.96%), and southern region (1.94%), respectively. Conclusion The participating dentists express positive feedback on the online DCE courses, and the online DCE courses can reduce the urban-rural gap in dental education resources. The use of online DCE courses in dental education will be a future trend.
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Affiliation(s)
- Feng-Chou Cheng
- Chia-Te Dental Clinic, New Taipei City, Taiwan
- School of Life Science, National Taiwan Normal University, Taipei, Taiwan
- Science Education Center, National Taiwan Normal University, Taipei, Taiwan
| | - Li-Hua Tang
- Department of Nuclear Medicine, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Kou-Jung Lee
- Hong-Yuan Dental Clinic, Chiayi County, Taiwan
- Dental Radiation Safety Committee, Taiwan Dental Association, Taipei, Taiwan
| | - Yuh-Fen Wei
- Department of Medical Imaging, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu County, Taiwan
| | - Bo-Lin Liu
- Department of Medical Imaging, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Mu-Hsiung Chen
- Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chun-Pin Chiang
- Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Clinical Dentistry, School of Dentistry, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Oral Biology, School of Dentistry, National Taiwan University, Taipei, Taiwan
- Department of Dentistry, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
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18
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Malaekeh-Nikouei A, Shokri-Naei S, Karbasforoushan S, Bahari H, Baradaran Rahimi V, Heidari R, Askari VR. Metformin beyond an anti-diabetic agent: A comprehensive and mechanistic review on its effects against natural and chemical toxins. Biomed Pharmacother 2023; 165:115263. [PMID: 37541178 DOI: 10.1016/j.biopha.2023.115263] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/24/2023] [Accepted: 07/31/2023] [Indexed: 08/06/2023] Open
Abstract
In addition to the anti-diabetic effect of metformin, a growing number of studies have shown that metformin has some exciting properties, such as anti-oxidative capabilities, anticancer, genomic stability, anti-inflammation, and anti-fibrosis, which have potent, that can treat other disorders other than diabetes mellitus. We aimed to describe and review the protective and antidotal efficacy of metformin against biologicals, chemicals, natural, medications, pesticides, and radiation-induced toxicities. A comprehensive search has been performed from Scopus, Web of Science, PubMed, and Google Scholar databases from inception to March 8, 2023. All in vitro, in vivo, and clinical studies were considered. Many studies suggest that metformin affects diseases other than diabetes. It is a radioprotective and chemoprotective drug that also affects viral and bacterial diseases. It can be used against inflammation-related and apoptosis-related abnormalities and against toxins to lower their effects. Besides lowering blood sugar, metformin can attenuate the effects of toxins on body weight, inflammation, apoptosis, necrosis, caspase-3 activation, cell viability and survival rate, reactive oxygen species (ROS), NF-κB, TNF-α, many interleukins, lipid profile, and many enzymes activity such as catalase and superoxide dismutase. It also can reduce the histopathological damages induced by many toxins on the kidneys, liver, and colon. However, clinical trials and human studies are needed before using metformin as a therapeutic agent against other diseases.
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Affiliation(s)
- Amirhossein Malaekeh-Nikouei
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Sina Shokri-Naei
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Sobhan Karbasforoushan
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hossein Bahari
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Vafa Baradaran Rahimi
- Department of Cardiovascular Diseases, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Reza Heidari
- Medical Biotechnology Research Center, AJA University of Medical Sciences, Tehran, Iran; Research Center for Cancer Screening and Epidemiology, AJA University of Medical Sciences, Tehran, Iran
| | - Vahid Reza Askari
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran; Pharmacological Research Center of Medicinal Plants, Mashhad University of Medical Sciences, Mashhad, Iran.
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19
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Fukui S, Inui A, Komatsu T, Ogura K, Ozaki Y, Sugita M, Saita M, Kobayashi D, Naito T. A Predictive Rule for COVID-19 Pneumonia Among COVID-19 Patients: A Classification and Regression Tree (CART) Analysis Model. Cureus 2023; 15:e45199. [PMID: 37720137 PMCID: PMC10500617 DOI: 10.7759/cureus.45199] [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] [Accepted: 09/13/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND In this study, we aimed to identify predictive factors for coronavirus disease 2019 (COVID-19) patients with complicated pneumonia and determine which COVID-19 patients should undergo computed tomography (CT) using classification and regression tree (CART) analysis. METHODS This retrospective cross-sectional survey was conducted at a university hospital. We recruited patients diagnosed with COVID-19 between January 1 and December 31, 2020. We extracted clinical information (e.g., vital signs, symptoms, laboratory results, and CT findings) from patient records. Factors potentially predicting COVID-19 pneumonia were analyzed using Student's t-test, the chi-square test, and a CART analysis model. RESULTS Among 221 patients (119 men (53.8%); mean age, 54.59±18.61 years), 160 (72.4%) had pneumonia. The CART analysis revealed that patients were at high risk of pneumonia if they had C-reactive protein (CRP) levels of >1.60 mg/dL (incidence of pneumonia: 95.7%); CRP levels of ≤1.60 mg/dL + age >35.5 years + lactate dehydrogenase (LDH)>225.5 IU/L (incidence of pneumonia: 95.5%); and CRP levels of ≤1.60 mg/dL + age >35.5 years + LDH≤225.5 IU/L + hemoglobin ≤14.65 g/dL (incidence of pneumonia: 69.6%). The area of the curve of the receiver operating characteristic of the model was 0.860 (95% CI: 0.804-0.915), indicating sufficient explanatory power. CONCLUSIONS The present results are useful for deciding whether to perform CT in COVID-19 patients. High-risk patients such as those mentioned above should undergo CT.
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Affiliation(s)
- Sayato Fukui
- Department of General Medicine, Faculty of Medicine, Juntendo University, Tokyo, JPN
| | - Akihiro Inui
- Department of General Medicine, Faculty of Medicine, Juntendo University, Tokyo, JPN
| | - Takayuki Komatsu
- Department of Emergency and Critical Care Medicine, Juntendo University Nerima Hospital, Tokyo, JPN
| | - Kanako Ogura
- Department of Diagnostic Pathology, Juntendo University Nerima Hospital, Tokyo, JPN
| | - Yutaka Ozaki
- Department of Diagnostic Radiology, Juntendo University Nerima Hospital, Tokyo, JPN
| | - Manabu Sugita
- Department of Emergency and Critical Care Medicine, Juntendo University Nerima Hospital, Tokyo, JPN
| | - Mizue Saita
- Department of General Medicine, Faculty of Medicine, Juntendo University, Tokyo, JPN
| | - Daiki Kobayashi
- Department of General Internal Medicine, Tokyo Medical University Ibaraki Medical Center, Inashiki, JPN
| | - Toshio Naito
- Department of General Medicine, Faculty of Medicine, Juntendo University, Tokyo, JPN
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20
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Naz M, Shah MA, Khattak HA, Wahid A, Asghar MN, Rauf HT, Khan MA, Ameer Z. Multi‐branch sustainable convolutional neural network for disease classification. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2023; 33:1621-1633. [DOI: 10.1002/ima.22884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 03/18/2023] [Indexed: 08/25/2024]
Abstract
AbstractPandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease‐19 (COVID‐19), brain stroke, and cancer are at their peak. Different machine learning and deep learning‐based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double‐branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi‐branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID‐19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K‐nearest neighbor (K‐NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID‐19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%).
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Affiliation(s)
- Maria Naz
- Department of Computer Science COMSATS University Islamabad Islamabad Pakistan
| | - Munam Ali Shah
- Department of Computer Science COMSATS University Islamabad Islamabad Pakistan
| | - Hasan Ali Khattak
- School of Electrical Engineering & Computer Science (SEECS) National University of Sciences and Technology (NUST) 44500 Islamabad Pakistan
| | - Abdul Wahid
- School of Electrical Engineering & Computer Science (SEECS) National University of Sciences and Technology (NUST) 44500 Islamabad Pakistan
- School of Computer Science University of Birmingham Dubai United Arab Emirates
| | | | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity Staffordshire University ST4 2DE Stoke‐on‐Trent UK
| | | | - Zoobia Ameer
- Shaheed Benazir Bhutto Women University Peshawar Peshawar Pakistan
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21
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Castellanos-Bermejo JE, Cervantes-Guevara G, Cervantes-Pérez E, Cervantes-Cardona GA, Ramírez-Ochoa S, Fuentes-Orozco C, Delgado-Hernández G, Tavares-Ortega JA, Gómez-Mejía E, Chejfec-Ciociano JM, Flores-Prado JA, Barbosa-Camacho FJ, González-Ojeda A. Diagnostic Efficacy of Chest Computed Tomography with a Dual-Reviewer Approach in Patients Diagnosed with Pneumonia Secondary to Severe Acute Respiratory Syndrome Coronavirus 2. Tomography 2023; 9:1617-1628. [PMID: 37736982 PMCID: PMC10514805 DOI: 10.3390/tomography9050129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023] Open
Abstract
To compare the diagnostic effectiveness of chest computed tomography (CT) utilizing a single- versus a dual-reviewer approach in patients with pneumonia secondary to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we conducted a retrospective observational study of data from a cross-section of 4809 patients with probable SARS-CoV-2 from March to November 2020. All patients had a CT radiological report and reverse-transcription polymerase chain reaction (PCR) results. A dual-reviewer approach was applied to two groups while conducting a comparative examination of the data. Reviewer 1 reported 108 patients negative and 374 patients positive for coronavirus disease 2019 (COVID-19) in group A, and 266 negative and 142 positive in group B. Reviewer 2 reported 150 patients negative and 332 patients positive for COVID-19 in group A, and 277 negative and 131 positive in group B. The consensus result reported 87 patients negative and 395 positive for COVID-19 in group A and 274 negative and 134 positive in group B. These findings suggest that a dual-reviewer approach improves chest CT diagnosis compared to a conventional single-reviewer approach.
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Affiliation(s)
- Jaime E. Castellanos-Bermejo
- Departamento de Radiología e Imagen, Hospital General Regional 110, Instituto Mexicano del Seguro Social, Guadalajara 44716, Mexico;
| | - Gabino Cervantes-Guevara
- Departamento de Bienestar y Desarrollo Sustentable, Centro Universitario del Norte, Universidad de Guadalajara, Colotlán 46200, Mexico;
- Departamento de Gastroenterología, Hospital Civil de Guadalajara Fray Antonio Alcalde, Universidad de Guadalajara, Guadalajara 44280, Mexico
| | - Enrique Cervantes-Pérez
- Departamento de Medicina Interna, Hospital Civil de Guadalajara Fray Antonio Alcalde, Guadalajara 44280, Mexico; (E.C.-P.)
- Centro Universitario de Tlajomulco, Universidad de Guadalajara, Tlajomulco de Zúñiga 45641, Mexico
| | - Guillermo A. Cervantes-Cardona
- Departamento de Disciplinas Filosóficas, Metodológicas e Instrumentales, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Mexico;
| | - Sol Ramírez-Ochoa
- Departamento de Gastroenterología, Hospital Civil de Guadalajara Fray Antonio Alcalde, Universidad de Guadalajara, Guadalajara 44280, Mexico
| | - Clotilde Fuentes-Orozco
- Unidad de Investigación Biomédica 02, Unidad Médica de alta especialidad, Hospital de Especialidades Centro Médico Nacional de Occidente, Instituto Mexicano del Seguro Social, Guadalajara 44329, Mexico; (C.F.-O.); (G.D.-H.); (J.A.T.-O.); (E.G.-M.); (J.M.C.-C.); (J.A.F.-P.)
| | - Gonzalo Delgado-Hernández
- Unidad de Investigación Biomédica 02, Unidad Médica de alta especialidad, Hospital de Especialidades Centro Médico Nacional de Occidente, Instituto Mexicano del Seguro Social, Guadalajara 44329, Mexico; (C.F.-O.); (G.D.-H.); (J.A.T.-O.); (E.G.-M.); (J.M.C.-C.); (J.A.F.-P.)
| | - Jaime A. Tavares-Ortega
- Unidad de Investigación Biomédica 02, Unidad Médica de alta especialidad, Hospital de Especialidades Centro Médico Nacional de Occidente, Instituto Mexicano del Seguro Social, Guadalajara 44329, Mexico; (C.F.-O.); (G.D.-H.); (J.A.T.-O.); (E.G.-M.); (J.M.C.-C.); (J.A.F.-P.)
| | - Erika Gómez-Mejía
- Unidad de Investigación Biomédica 02, Unidad Médica de alta especialidad, Hospital de Especialidades Centro Médico Nacional de Occidente, Instituto Mexicano del Seguro Social, Guadalajara 44329, Mexico; (C.F.-O.); (G.D.-H.); (J.A.T.-O.); (E.G.-M.); (J.M.C.-C.); (J.A.F.-P.)
| | - Jonathan M. Chejfec-Ciociano
- Unidad de Investigación Biomédica 02, Unidad Médica de alta especialidad, Hospital de Especialidades Centro Médico Nacional de Occidente, Instituto Mexicano del Seguro Social, Guadalajara 44329, Mexico; (C.F.-O.); (G.D.-H.); (J.A.T.-O.); (E.G.-M.); (J.M.C.-C.); (J.A.F.-P.)
| | - Juan A. Flores-Prado
- Unidad de Investigación Biomédica 02, Unidad Médica de alta especialidad, Hospital de Especialidades Centro Médico Nacional de Occidente, Instituto Mexicano del Seguro Social, Guadalajara 44329, Mexico; (C.F.-O.); (G.D.-H.); (J.A.T.-O.); (E.G.-M.); (J.M.C.-C.); (J.A.F.-P.)
| | - Francisco J. Barbosa-Camacho
- Departamento de Psiquiatría, Hospital Civil de Guadalajara Fray Antonio Alcalde, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44280, Mexico;
| | - Alejandro González-Ojeda
- Unidad de Investigación Biomédica 02, Unidad Médica de alta especialidad, Hospital de Especialidades Centro Médico Nacional de Occidente, Instituto Mexicano del Seguro Social, Guadalajara 44329, Mexico; (C.F.-O.); (G.D.-H.); (J.A.T.-O.); (E.G.-M.); (J.M.C.-C.); (J.A.F.-P.)
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22
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Park D, Jang R, Chung MJ, An HJ, Bak S, Choi E, Hwang D. Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias. Sci Rep 2023; 13:13420. [PMID: 37591967 PMCID: PMC10435445 DOI: 10.1038/s41598-023-40506-w] [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: 06/10/2023] [Accepted: 08/11/2023] [Indexed: 08/19/2023] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related to infectious respiratory diseases (IRDs) can emerge unpredictably. Therefore, we aimed to develop and validate a severity assessment model for IRDs, including COVID-19, influenza, and novel influenza, using CT images on a multi-centre data set. Of the 805 COVID-19 patients collected from a single centre, 649 were used for training and 156 were used for internal validation (D1). Additionally, three external validation sets were obtained from 7 cohorts: 1138 patients with COVID-19 (D2), and 233 patients with influenza and novel influenza (D3). A hybrid model, referred to as Hybrid-DDM, was constructed by combining two deep learning models and a machine learning model. Across datasets D1, D2, and D3, the Hybrid-DDM exhibited significantly improved performance compared to the baseline model. The areas under the receiver operating curves (AUCs) were 0.830 versus 0.767 (p = 0.036) in D1, 0.801 versus 0.753 (p < 0.001) in D2, and 0.774 versus 0.668 (p < 0.001) in D3. This study indicates that the Hybrid-DDM model, trained using COVID-19 patient data, is effective and can also be applicable to patients with other types of viral pneumonia.
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