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Nair AV, Ramanathan S, Venugopalan P. Chest imaging in pregnant patients with COVID-19: Recommendations, justification, and optimization. Acta Radiol Open 2022; 11:20584601221077394. [PMID: 35284094 PMCID: PMC8905047 DOI: 10.1177/20584601221077394] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 01/16/2022] [Indexed: 01/11/2023] Open
Abstract
Evaluation of COVID-19 related complication is challenging in pregnancy, due to concerns about ionizing radiation risk to mother and the fetus. Although there are instances when diagnostic imaging is clinically warranted for COVID-19 evaluation despite the minimal risks of radiation exposure, often there are concerns raised by the patients and sometimes by the attending physicians. This article reviews the current recommendations on indications of chest imaging in pregnant patients with COVID-19, the dose optimization strategies, and the risks related to imaging exposure during pregnancy. In clinical practice, these imaging strategies are key in addressing the complex obstetrical complications associated with COVID-19 pneumonia.
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Affiliation(s)
| | - Subramaniyan Ramanathan
- Department of Clinical Imaging, Al-Wakra Hospital, Hamad Medical Corporation, Doha, Qatar
- Department of Radiology, Weill Cornell Medicine, Doha, Qatar
| | - Prasanna Venugopalan
- Department of Obstetrics and Gynaecology, Travancore Medical College, Kollam, Kerala, India
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Ke Z, Li L, Wang L, Liu H, Lu X, Zeng F, Zha Y. Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19). Acta Radiol 2022; 63:319-327. [PMID: 33601893 DOI: 10.1177/0284185121994695] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. PURPOSE To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. MATERIAL AND METHODS The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. RESULTS Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. CONCLUSION The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses.
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Affiliation(s)
- Zan Ke
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Liang Li
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Li Wang
- Department of Infection Prevention and Control, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Huan Liu
- GE Healthcare, Shanghai, PR China
| | - Xuefang Lu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Feifei Zeng
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
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203
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Bartoli A, Fournel J, Maurin A, Marchi B, Habert P, Castelli M, Gaubert JY, Cortaredona S, Lagier JC, Million M, Raoult D, Ghattas B, Jacquier A. Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2022; 1:100003. [PMID: 37520010 PMCID: PMC8939894 DOI: 10.1016/j.redii.2022.100003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 03/02/2022] [Accepted: 03/09/2022] [Indexed: 12/23/2022]
Abstract
Objectives 1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification. Methods This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy. Results The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; p<0.0001). Conclusions A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.
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Key Words
- ACE, angiotensin-converting enzyme
- Artificial intelligence
- BMI, body mass index
- CNN, convolutional neural network
- COVID-19
- COVID-19, coronavirus disease 2019
- CT-SS, chest tomography severity score
- Cons, consolidation
- DL, deep learning
- DSC, Dice similarity coefficient
- Deep learning
- Diagnostic imaging
- GGO, ground-glass opacity
- ICU, intensive care unit
- LDCT, low-dose computed tomography
- MAE, mean absolute error
- MVSF, mean volume similarity fraction
- Multidetector computed tomography
- ROC, receiver operating characteristic
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Affiliation(s)
- Axel Bartoli
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- CRMBM - UMR CNRS 7339, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Joris Fournel
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- CRMBM - UMR CNRS 7339, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Arnaud Maurin
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
| | - Baptiste Marchi
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
| | - Paul Habert
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- LIEE, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
- CERIMED, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Maxime Castelli
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
| | - Jean-Yves Gaubert
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- LIEE, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
- CERIMED, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Sebastien Cortaredona
- Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
- IRD, VITROME, Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
| | - Jean-Christophe Lagier
- Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
- IRD, MEPHI, Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
| | - Matthieu Million
- Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
- IRD, MEPHI, Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
| | - Didier Raoult
- Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
- IRD, MEPHI, Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
| | - Badih Ghattas
- I2M - UMR CNRS 7373, Aix-Marseille University. CNRS, Centrale Marseille, 13453 Marseille, France
| | - Alexis Jacquier
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- CRMBM - UMR CNRS 7339, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
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Astinchap B, Ghanbaripour H, Amuzgar R. Multifractal analysis of chest CT images of patients with the 2019 novel coronavirus disease (COVID-19). CHAOS, SOLITONS, AND FRACTALS 2022; 156:111820. [PMID: 35095221 PMCID: PMC8784535 DOI: 10.1016/j.chaos.2022.111820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 08/30/2021] [Accepted: 01/15/2022] [Indexed: 06/14/2023]
Abstract
The present study was done to evaluate chest computed tomography (CT) images of patients with 2019 novel coronavirus disease (COVID-19) by multifractal technique as a new method to find a way for comparing lung infection quantitatively and identifying progression pattern of the disease. The multifractal spectra extracted from analysis of CT images showed that these spectra were correlated with lung infection amount and disease progression so that, multifractal parameters (αmin, αmax, ∆α, f(αmin), f(αmax), ∆f(α), α(q = 0), and f(α) max) were strongly dependent on amount of lung infection. The results demonstrated that multifractality of chest CT images was increased with the increase in lung infection in patients. The interesting and promising result was that capacity dimension (D0) as a new diagnostic parameter varied linearly with progression and reduction of lung infection. A critical value was found for D0, according to which patients with D0 lower than 1.4 can be healed by treatment. Therefore, herein, a way was found for quantitative assessment of lung infection of patients with COVID-19 by analyzing chest CT images using the multifractal method. This method can be very effective for physicians in diagnosis and treatment of pneumonia caused by COVID-19 and timely identification of therapeutic effects.
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Affiliation(s)
- Bandar Astinchap
- Department of Physics, Faculty of Science, University of Kurdistan, Sanandaj, Kurdistan 66177-15175, Iran
- Research Center for Nanotechnology, University of Kurdistan, Sanandaj, Kurdistan 66177-15175, Iran
| | - Hamta Ghanbaripour
- Department of Physics, Faculty of Science, University of Kurdistan, Sanandaj, Kurdistan 66177-15175, Iran
- Physics Department, Faculty of Science, Razi University, Kermanshah, Iran
| | - Raziye Amuzgar
- Kermanshah University of Medical Sciences, School of the Medicine, Kermanshah, Iran
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205
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Zhou X, Pu Y, Zhang D, Xia Y, Guan Y, Liu S, Fan L. CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis. Acta Radiol 2022; 63:291-310. [PMID: 33631941 DOI: 10.1177/0284185121992655] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Quick screening patients with COVID-19 is the most important way of controlling transmission by isolation and medical treatment. Chest computed tomography (CT) has been widely used during the initial screening process, including pneumonia diagnosis, severity assessment, and differential diagnosis of COVID-19. The course of COVID-19 changes rapidly. Serial CT imaging could observe the distribution, density, and range of lesions dynamically, monitor the changes, and then guide towards appropriate treatment. The aim of the review was to explore the chest CT findings and dynamic CT changes of COVID-19 using systematic evaluation methods, instructing the clinical imaging diagnosis. A systematic literature search was performed. The quality of included literature was evaluated with a quality assessment tool, followed by data extraction and meta-analysis. Homogeneity and publishing bias were analyzed. A total of 109 articles were included, involving 2908 adults with COVID-19. The lesions often occurred in bilateral lungs (74%) and were multifocal (77%) with subpleural distribution (81%). Lesions often showed ground-glass opacity (GGO) (68%), followed by GGO with consolidation (48%). The thickening of small vessels (70%) and thickening of intralobular septum (53%) were also common. The dynamic changes of chest CT manifestations showed that lesions were absorbed and improved gradually after reaching the peak (80%), had progressive deterioration (55%), were absorbed and improved gradually (46%), fluctuated (22%), or remained stable (26%). The review showed the common and key CT features and the dynamic imaging change patterns of COVID-19, helping with timely management during COVID-19 pandemic.
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Affiliation(s)
- Xiuxiu Zhou
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Yu Pu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Di Zhang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Yi Xia
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Yu Guan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
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Rosas J, Liaño FP, Cantó ML, Barea JMC, Beser AR, Rabasa JTA, Adsuar FM, Auli BV, López IF, Sainz AMG, Ramis PE, Pérez LR, Rebollo MLN, Lorido RH, Escolar LG. Experience With the Use of Baricitinib and Tocilizumab Monotherapy or Combined, in Patients With Interstitial Pneumonia Secondary to Coronavirus COVID19: A Real-World Study. REUMATOLOGIA CLINICA 2022; 18:150-156. [PMID: 35277213 PMCID: PMC8902856 DOI: 10.1016/j.reumae.2020.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 10/28/2020] [Indexed: 01/08/2023]
Abstract
Objective To describe the experience of treatment with baricitinib (BARI) and/or tocilizumab (TCZ), in monotherapy or combined, in patients admitted for interstitial pneumonia secondary to COVID19, and for 30 days after discharge. Methods Medical records of patients admitted with COVID19 and IP with PaO2/FiO2 < 300, treated with BARI and/or TCZ, and compared with patients who did not, were retrospectively reviewed. Results Sixty patients were included; 43 (72%) are males, mean age 67 (SD: 14) years (<50 years: 17%; 51–70: 30%; >70: 53%), with 8.5 (SD: 1) days of symptoms. Sixteen (27%) patients required ICU (94% in <70 years). Fifteen (25%) patients died, 67% in >70 years; 11 (18%) patients died in the first 15 days of admission and 4 (7%) between days 16 to 30. Twenty-three (38%) patients received BARI, 12 (52%) monotherapy (Group 1), during 6 (SD: 2.6) days on average, none required ICU and 2 (17%) died. Thirty-one (52%) patients received TCZ, 20 (33%) as monotherapy (Group 2), 16 (52%) patients required ICU and 4 (20%) died. In the 11 (18%) patients who received BARI (2.8 [SD: 2.5] days average) and TCZ combined (Group 3), 3 (27%) required ICU and died. There were no severe side effects in BARI or TCZ patients. In the 17 (28%) patients who received neither BARI nor TCZ (Group 4), none required ICU and 6 (35%) died. Mean (SD) PaO2/FiO2 at admission between groups was respectively: 167 (82.3), 221 (114.9), 236 (82.3), 276 (83.2). Conclusion Treatment with BARI and TCZ did not cause serious side effects. They could be considered early in patients with NI secondary to COVID19 and impaired PaO2/PaFi.
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Affiliation(s)
- José Rosas
- Rheumatology Department, Marina Baixa Hospital, Villajoyosa, Alicante, Spain.
| | | | | | | | - Amparo Raga Beser
- Pharmacy Department, Marina Baixa Hospital, Villajoyosa, Alicante, Spain
| | | | | | - Brian Vila Auli
- Pneumology Department, Marina Baixa Hospital, Villajoyosa, Alicante, Spain
| | | | | | - Pere Esquerdo Ramis
- Internal Medicine Department, Marina Baixa Hospital, Villajoyosa, Alicante, Spain
| | - Laura Ruiz Pérez
- Intensive Care Department, Marina Baixa Hospital, Villajoyosa, Alicante, Spain
| | | | | | - Laura Gómez Escolar
- Gastroenterology Department, Marina Baixa Hospital, Villajoyosa, Alicante, Spain
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Awassa L, Jdey I, Dhahri H, Hcini G, Mahmood A, Othman E, Haneef M. Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights. SENSORS (BASEL, SWITZERLAND) 2022; 22:1890. [PMID: 35271037 PMCID: PMC8915023 DOI: 10.3390/s22051890] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/12/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.
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Affiliation(s)
- Lamia Awassa
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
| | - Imen Jdey
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
| | - Habib Dhahri
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
- Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (A.M.); (E.O.)
| | - Ghazala Hcini
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
| | - Awais Mahmood
- Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (A.M.); (E.O.)
| | - Esam Othman
- Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (A.M.); (E.O.)
| | - Muhammad Haneef
- Department of Electrical Engineering, Foundation University Islamabad, Islamabad 44000, Pakistan;
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208
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Enshaei N, Oikonomou A, Rafiee MJ, Afshar P, Heidarian S, Mohammadi A, Plataniotis KN, Naderkhani F. COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images. Sci Rep 2022; 12:3212. [PMID: 35217712 PMCID: PMC8881477 DOI: 10.1038/s41598-022-06854-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 01/21/2022] [Indexed: 11/09/2022] Open
Abstract
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.
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Affiliation(s)
- Nastaran Enshaei
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
| | - Moezedin Javad Rafiee
- Department of Medicine and Diagnostic Radiology, McGill University, Montreal, QC, Canada
| | - Parnian Afshar
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| | - Shahin Heidarian
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| | | | - Farnoosh Naderkhani
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
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209
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Abstract
Recognition of COVID-19 is a challenging task which consistently requires taking a gander at clinical images of patients. In this paper, the transfer learning technique has been applied to clinical images of different types of pulmonary diseases, including COVID-19. It is found that COVID-19 is very much similar to pneumonia lung disease. Further findings are made to identify the type of pneumonia similar to COVID-19. Transfer Learning makes it possible for us to find out that viral pneumonia is same as COVID-19. This shows the knowledge gained by model trained for detecting viral pneumonia can be transferred for identifying COVID-19. Transfer Learning shows significant difference in results when compared with the outcome from conventional classifications. It is obvious that we need not create separate model for classifying COVID-19 as done by conventional classifications. This makes the herculean work easier by using existing model for determining COVID-19. Second, it is difficult to detect the abnormal features from images due to the noise impedance from lesions and tissues. For this reason, texture feature extraction is accomplished using Haralick features which focus only on the area of interest to detect COVID-19 using statistical analyses. Hence, there is a need to propose a model to predict the COVID-19 cases at the earliest possible to control the spread of disease. We propose a transfer learning model to quicken the prediction process and assist the medical professionals. The proposed model outperforms the other existing models. This makes the time-consuming process easier and faster for radiologists and this reduces the spread of virus and save lives.
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210
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Nazir T, Nawaz M, Javed A, Malik KM, Saudagar AKJ, Khan MB, Abul Hasanat MH, AlTameem A, AlKathami M. COVID-DAI: A novel framework for COVID-19 detection and infection growth estimation using computed tomography images. Microsc Res Tech 2022; 85:2313-2330. [PMID: 35194866 PMCID: PMC9088346 DOI: 10.1002/jemt.24088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 02/01/2022] [Accepted: 02/14/2022] [Indexed: 12/18/2022]
Abstract
The COVID‐19 pandemic is spreading at a fast pace around the world and has a high mortality rate. Since there is no proper treatment of COVID‐19 and its multiple variants, for example, Alpha, Beta, Gamma, and Delta, being more infectious in nature are affecting millions of people, further complicates the detection process, so, victims are at the risk of death. However, timely and accurate diagnosis of this deadly virus can not only save the patients from life loss but can also prevent them from the complex treatment procedures. Accurate segmentation and classification of COVID‐19 is a tedious job due to the extensive variations in its shape and similarity with other diseases like Pneumonia. Furthermore, the existing techniques have hardly focused on the infection growth estimation over time which can assist the doctors to better analyze the condition of COVID‐19‐affected patients. In this work, we tried to overcome the shortcomings of existing studies by proposing a model capable of segmenting, classifying the COVID‐19 from computed tomography images, and predicting its behavior over a certain period. The framework comprises four main steps: (i) data preparation, (ii) segmentation, (iii) infection growth estimation, and (iv) classification. After performing the pre‐processing step, we introduced the DenseNet‐77 based UNET approach. Initially, the DenseNet‐77 is used at the Encoder module of the UNET model to calculate the deep keypoints which are later segmented to show the coronavirus region. Then, the infection growth estimation of COVID‐19 per patient is estimated using the blob analysis. Finally, we employed the DenseNet‐77 framework as an end‐to‐end network to classify the input images into three classes namely healthy, COVID‐19‐affected, and pneumonia images. We evaluated the proposed model over the COVID‐19‐20 and COVIDx CT‐2A datasets for segmentation and classification tasks, respectively. Furthermore, unlike existing techniques, we performed a cross‐dataset evaluation to show the generalization ability of our method. The quantitative and qualitative evaluation confirms that our method is robust to both COVID‐19 segmentation and classification and can accurately predict the infection growth in a certain time frame.
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Affiliation(s)
- Tahira Nazir
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
| | - Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
| | - Ali Javed
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
| | - Khalid Mahmood Malik
- Department of Computer Science and Engineering, Oakland University, Rochester, Michigan, USA
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Muhammad Badruddin Khan
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Mozaherul Hoque Abul Hasanat
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Abdullah AlTameem
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Mohammad AlKathami
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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Evaluation of the Levels of Peripheral CD3 +, CD4 +, and CD8 + T Cells and IgG and IgM Antibodies in COVID-19 Patients at Different Stages of Infection. Microbiol Spectr 2022; 10:e0084521. [PMID: 35196808 PMCID: PMC8865559 DOI: 10.1128/spectrum.00845-21] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects the stimulatory levels of cellular-mediated immunity, which plays an essential role in controlling SARS-CoV-2 infection. In fact, several studies have shown the association of lymphopenia with severe COVID-19 in patients. The aim of this study is to investigate the response of the immune system, including cell-mediated immunity and antibody production, during different stages of SARS-CoV-2 infection. Peripheral blood and serum samples were collected from patients with moderate infection, patients under medication (hospitalized), patients who had recovered, and healthy individuals (n = 80). Flow cytometry analysis was performed on peripheral blood samples to determine the cellular immunity profile of each patient. The data showed a significant reduction in the levels of CD3+, CD4+, and CD8+ T cells and CD45+ cells in the moderate and under-medication groups, suggesting lymphopenia in those patients. Also, enzyme-linked immunosorbent assay (ELISA) was conducted on the serum samples to measure the levels of antibodies, including IgM and IgG, in each patient. The results revealed a significant increase in the levels of IgM in the moderate infection and under-medication patients, thus indicating the production of IgM during the first week of infection. Furthermore, changes in the levels of IgG were significantly detected among recovered patients, indicating therefore a remarkable increase during the recovery stage of SARS-CoV-2 infection and thus a strong humoral-mediated immunity. In summary, the results of this study may help us to understand the main role of the cellular immune responses, including CD3+, CD4+, and CD8+ T cells, against SARS-CoV-2 infection. This understanding might support the development of SARS-CoV-2 treatments and vaccines in the near future. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 in China. This virus is a serious threat to people not only in China but also worldwide, where it has been detected in over 222 countries. It has been reported that ∼3.4% of SARS-CoV-2-infected patients have died. The significance of our study relies on the fact that an enzyme-linked immunosorbent assay and flow cytometry were used to measure the levels of antibodies and cellular immune response, respectively, from clinical samples of patients infected with SARS-CoV-2.
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212
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Zhang X, Zheng J, Qian E, Xue L, Liu X. The association of clinical features and laboratory findings of COVID-19 infection with computed pneumonia volume. Medicine (Baltimore) 2022; 101:e28856. [PMID: 35363187 PMCID: PMC9282109 DOI: 10.1097/md.0000000000028856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 01/27/2022] [Indexed: 01/04/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) was first detected in China in December 2019, and declared as a pandemic by the World Health Organization (WHO) on March 11, 2020.To study the clinical features of patients with COVID-19, we analyzed the correlation between some inflammation-related indicators in patients' serum and the severity of the disease, especially PV (pneumonia volume under CT scan) and pneumonia volume ratio (PVR).Sixty-six COVID-19 patients in Huai'an, China were selected as the research subjects. We collected the clinical data, including general characteristics, clinical symptoms, serum test results and CT performance, explored the relationship between inflammation-related indexes, oxygenation index, PV, PVR, while indicators of mild to moderate patients and severe patients were compared.The most prominent manifestations of COVID-19 patients were fever (47, 71.2%); cough (41, 62.1%), with or without respiratory and other systemic symptoms; There was no difference in gender (P = .567) and age (P = .865) between mild to moderate and severe groups. High sensitivity C-reactive protein (hs-CRP), erythrocyte sedimentation rate (ESR), and interleukin-6 (IL-6) of overall patients were higher than the normal range (P < .001, respectively). hs-CRP was negatively correlated with oxygenation index (OI) (r = -0.55), whereas positively correlated with PV, PVR and ESR (r = 0.89; r = 0.87; r = 0.47, respectively); ESR was negatively correlated with OI (r = -0.45), meanwhile it was positively correlated with PV and PVR (r = 0.44; r = 0.46, respectively). OI was negatively correlated with PV and PVR (r = -0.6, respectively). PV had a clear correlation with PVR (r = 1). Severe patients' hs-CRP, PV, PVR were higher than mild to moderate group (P = .006; P = .001; P < .001, respectively), but OI was lower (P < .001).The clinical features of COVID-19 were similar to general viral pneumonia. hs-CRP, ESR showed a certain correlation with the PV and PVR, which might play a certain role in assessing the severity of COVID-19.
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Affiliation(s)
- Xin Zhang
- Department of Radiology, Huaian Fourth People's Hospital, China
| | - Jingjing Zheng
- Clinical Laboratory, Huaian Fourth People's Hospital, China
| | - Eryan Qian
- Intensive Care Unit, Huaian Fourth People's Hospital, China
| | - Leyang Xue
- Intensive Care Unit, Huaian Fourth People's Hospital, China
| | - Xingxiang Liu
- Clinical Laboratory, Huaian Fourth People's Hospital, China
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213
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Savastano MC, Larici AR, Crincoli E, De Filippis A, Cicchetti G, Gambini G, Savastano A, Marano R, Natale L, Rizzo S. Retinal vascular impairment matched to the pulmonary damage in early post-COVID 19 patients. Eur J Ophthalmol 2022; 32:3574-3583. [PMID: 35174719 DOI: 10.1177/11206721221079153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Endothelium damage is a crucial element in the pathogenesis of SARS-Cov-2 infection. Most casualties in critical COVID-19 cases are due to ARDS, diffuse coagulopathy and cytokine storm. ARDS itself is a consequence of pulmonary endothelial cells damage. Damage to retinal capillary microcirculation in post-infective period has been investigated through Optical Coherence Tomography Angiography (OCTA). The aim of the present study is to find a correlation between signs of retinal vascular damage and pulmonary impairment. METHODS Patients admitted to hospital and subsequently recovered from COVID-19 infection were summoned 1 month later to undergo coherence tomography (CT) scan and OCTA examination. RESULTS The study population included 87 COVID-19 patients with a mean age of 54.28 ± 14.44 years. Oxygen therapy, non-invasive and invasive mechanical ventilation were necessary in 33, 11 and 4 patients respectively to provide respiratory support during the acute course of the disease. Pulmonary involvement interested 54 patients (62.1%). Peripheral (27.6%) or diffuse (29.9%) involvement and ground glass (GG) opacities (47.1%) represented the prevalent radiological finding. A reduced RCPI FI was independently correlated with the presence of reticulation pattern in CT scan (p = .019). Also, RNFL and GCC were thinner in patients who displayed reticulation pattern (respectively p = .025 and p = .015). CONCLUSIONS A reduction in RPCP-FI and RNFL and GCC thickness were independently correlated to the presence of CT reticulation pattern. This association can reflect cytokine induced remodeling in both organs as a consequence of systemic endothelial damage and inflammation.
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Affiliation(s)
- Maria Cristina Savastano
- Ophthalmology Unit, 18654"Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy.,60234Catholic University of "Sacro Cuore", Rome, Italyù
| | - Anna Rita Larici
- 60234Catholic University of "Sacro Cuore", Rome, Italyù.,Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.,Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Emanuele Crincoli
- Ophthalmology Unit, 18654"Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy.,60234Catholic University of "Sacro Cuore", Rome, Italyù
| | - Alessandro De Filippis
- Ophthalmology Unit, 18654"Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy.,60234Catholic University of "Sacro Cuore", Rome, Italyù
| | - Giuseppe Cicchetti
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.,Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gloria Gambini
- Ophthalmology Unit, 18654"Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy.,60234Catholic University of "Sacro Cuore", Rome, Italyù
| | - Alfonso Savastano
- Ophthalmology Unit, 18654"Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy.,60234Catholic University of "Sacro Cuore", Rome, Italyù
| | - Riccardo Marano
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.,Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luigi Natale
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.,Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Stanislao Rizzo
- Ophthalmology Unit, 18654"Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy.,60234Catholic University of "Sacro Cuore", Rome, Italyù.,"Consiglio Nazionale delle Ricerche, Istituto di Neuroscienze", Pisa, Italy
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214
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Günçkan Ö, Öztürk Ö, Ayyıldız VA, Bağlan V, Çakır M, Akkaya A. Uma Apresentação Rara de COVID-19 com Embolia Pulmonar. Arq Bras Cardiol 2022; 118:525-529. [PMID: 35262591 PMCID: PMC8856685 DOI: 10.36660/abc.20210350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/28/2021] [Indexed: 11/18/2022] Open
Abstract
A doença de coronavírus 2019 (COVID-19) foi relatada em quase todos os países do mundo desde dezembro de 2019. A infecção por SARS-CoV-2 é frequentemente assintomática ou com sintomas leves, mas também pode levar à hipóxia, um estado hiperinflamatório e coagulopatia. Os parâmetros de coagulação anormais estão associados a complicações trombóticas, incluindo embolia pulmonar na COVID-19, mas pouco se sabe sobre os mecanismos. A semelhança dos sintomas iniciais de ambas as doenças também pode ser confusa, portanto, os médicos devem estar cientes do potencial para condições concomitantes. Apresentamos aqui um caso que não apresentava opacidades em vidro fosco nos pulmões, mas apresentava embolia pulmonar e derrame pleural em associação com infecção por COVID-19.
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215
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Usefulness of Selected Peripheral Blood Counts in Predicting Death in Patients with Severe and Critical COVID-19. J Clin Med 2022; 11:jcm11041011. [PMID: 35207281 PMCID: PMC8878821 DOI: 10.3390/jcm11041011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/05/2022] [Accepted: 02/13/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Immune dysregulation and hypoxemia are two important pathophysiological problems in patients with COVID-19 that affect peripheral blood count parameters. We hypothesized that assessment of the neutrophil-lymphocyte ratio (NLR) and red blood cell distribution width index (RDW-SD) could predict death in patients with severe and critical COVID-19. METHODS Seventy patients admitted to the intensive care unit (ICU) for COVID-19 acute respiratory failure were included in the study. RDW-SD and NLR on the day of ICU admission and peak values during the entire hospitalization were assessed. The primary endpoint was death before ICU discharge. RESULTS Patients who died had higher NLR on admission (20.3, IQR 15.3-30.2 vs. 11.0, IQR 6.8-16.9; p = 0.003) and higher RDW-SD (48.1 fL; IQR 43.1-50.5 vs. 43.9 fL; IQR 40.9-47.3, p = 0.01) than patients discharged from the ICU. NLR and RDW-SD values on ICU admission accurately predicted death in 76% (AUC = 0.76; 95%CI 0.65-0.86; p = 0.001; cut-off > 14.38) and 72% of cases (AUC = 0.72; 95%CI 0.60-0.82; p = 0.003; cut-off > 44.7 fL), respectively. Multivariable analysis confirmed that NLR > 14.38 on the day of ICU admission was associated with a 12-fold increased risk of death (logOR 12.43; 95%CI 1.61-96.29, p = 0.02), independent of other blood counts, clinical and demographic parameters. CONCLUSIONS Neutrophil-lymphocyte ratio determined on the day of ICU admission may be a useful biomarker predicting death in patients with severe and critical COVID-19.
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216
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Ahmadi J, Kahkeshpour F, Farahmand H, Esmaeili Nadimi A, Ghadimi K, Bazmandegan G, Kamiab Z. Evaluation of chest CT scan finding in the patients with acute respiratory symptoms following positive results of RT-PCR-COVID19. INTERNATIONAL JOURNAL OF PHYSIOLOGY, PATHOPHYSIOLOGY AND PHARMACOLOGY 2022; 14:48-54. [PMID: 35310865 PMCID: PMC8918605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Reverse transcription-polymerase chain reaction (RT-PCR) is a standard technique for diagnosing coronavirus disease 2019 (COVID-19). The parameters for the diagnosis of COVID-19 included the history of exposure to positive COVID-19 patients, clinical signs and symptoms related to the disease, inflammation factors in the blood test or positive antigen-antibody test, and chest computed tomography (CT) findings. The current study evaluated the chest CT scan findings in patients with respiratory problems following positive RT-PCR of COVID 19. MATERIALS AND METHODS This cross-sectional study was performed on 120 patients referred to Ali Ibn-Abi Talib Hospital in Rafsanjan, Kerman Province, Iran, with respiratory symptoms between Dec-2019 to Dec-2020. Two radiologists reviewed the chest CT scans of these patients using the checklist that included parameters such as the types of involvement (consolidation/grand-glass/crazy paving, etc.) and the patterns of involvement (central/peripheral), and the pleural findings. RESULTS The CT scan was conducted in 107 patients with a typical condition and 11 patients with an atypical form of the disease. The frequency of the typical CT image of COVID-19 in the male group was significantly higher than that in the female group (P=0.004). The frequency of reverse halo sign, septal thickening, cardiomegaly, and crazy paving was significantly higher in males than in females (P≤0.05). Also, there was a significant difference between age groups based on the number of involved lobes (P=0.04). CONCLUSION Chest CT scan is an important diagnostic method for COVID 19 with high sensitivity. The parameters in the CT scan are beneficial for the diagnosis of COVID 19. In addition, some characters in CT scans in the male gender are more specific.
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Affiliation(s)
- Jafar Ahmadi
- Department of Radiology, School of Medicine, Rafsanjan University of Medical SciencesKerman 7718175911, Iran
| | - Firoozeh Kahkeshpour
- Department of Radiology, School of Medicine, Rafsanjan University of Medical SciencesKerman 7718175911, Iran
| | - Habib Farahmand
- Department of Radiology, School of Medicine, Rafsanjan University of Medical SciencesKerman 7718175911, Iran
| | - Ali Esmaeili Nadimi
- Department of Cardiology, School of Medicine, Rafsanjan University of Medical SciencesKerman 7718175911, Iran
| | - Keyvan Ghadimi
- School of Medicine, Isfahan University of Medical SciencesIsfahan 8174673461, Iran
| | - Gholamreza Bazmandegan
- Department of Physiology and Pharmacology, Rafsanjan University of Medical SciencesKerman 7718175911, Iran
| | - Zahra Kamiab
- Department of Community Medicine, Rafsanjan University of Medical SciencesKerman 7718175911, Iran
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217
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Chen Y, Liu C, Wang T, Qi J, Jia X, Zeng X, Bai J, Lu W, Deng Y, Zhong B, He W, Xing Y, Lian Z, Zhou H, Yan J, Yang X, Yu H, Zhou J, Zhou D, Qiu L, Zhong N, Wang J. Efficacy and safety of Bufei Huoxue capsules in the management of convalescent patients with COVID-19 infection: A multicentre, double-blind, and randomised controlled trial. JOURNAL OF ETHNOPHARMACOLOGY 2022; 284:114830. [PMID: 34763045 PMCID: PMC8575540 DOI: 10.1016/j.jep.2021.114830] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/17/2021] [Accepted: 11/06/2021] [Indexed: 05/28/2023]
Abstract
BACKGROUND As of September 17, 2021, coronavirus disease 2019 (COVID-19) has infected more than 226 million people in a worldwide pandemic, with conservative estimates suggesting that there are more than 204 million convalescent patients with COVID-19. Previous studies have indicated that patients in the recovery phase exhibit decreased function of multiple organs. In China, traditional Chinese medicine (TCM) treatment is recommended in the rehabilitation period of COVID-19; however, the safety and efficacy of such treatment remain to be confirmed. AIM OF STUDY The present study aimed to evaluate the efficacy and safety of Bufei Huoxue (BFHX) in restoring the functional status and exercise tolerance of patients recovering from COVID-19. METHODS A total of 131 patients in the rehabilitation period of COVID-19 infection were randomly divided into a Bufei Huoxue (BFHX) group (n = 66) and a placebo group (n = 65). BFHX or placebo was given orally three times a day (1.4 g/dose) for 90 days. The primary outcomes was to evaluate improvements in exercise tolerance and imaging manifestations on chest computed tomography (CT). RESULTS After the exclusion of two patients who withdrew prior to receiving any medications, 129 patients were recruited, including 64 patients in the BFHX group and 65 patients in the placebo group. After 3 months of treatment, the BFHX group exhibited greater attenuation of pneumonia lesions on chest CT than the placebo group (P<0.05). Improvements in 6-min walk distance (6MWD) relative to baseline were also significantly better in the BFHX group than in the placebo group (P<0.01). Scores on the Fatigue Assessment Inventory (FAI) were lower in the BFHX group than in the placebo group (P<0.05). Although the rate of adverse events was higher in the BFHX group than in the placebo group (9.38% vs. 4.62%), the difference was not significant (P=0.3241). CONCLUSIONS BFHX may exert strong rehabilitative effects on physiological activity in patients recovering from COVID-19, which may in turn attenuate symptoms of fatigue and improve exercise tolerance.
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Affiliation(s)
- Yuqin Chen
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Chunli Liu
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Tingping Wang
- Department of Out-patient and Emergency, Wuhan Institute for Tuberculosis Control, Wuhan Pulmonary Hospital, Wuhan, Hubei, China
| | - Jingjing Qi
- Department of Respiratory and Critical Care Medicine, Xiangzhou District People's Hospital, Xiangyang, Hubei, China
| | - Xiaoqing Jia
- Department of Respiratory, Third Hospital of Baotou City, Baotou, Inner Mongolia, China
| | - Xiansheng Zeng
- Department of Respiratory and Critical Care Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Jianling Bai
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenju Lu
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yu Deng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Bihua Zhong
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Wenjun He
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yue Xing
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zhan Lian
- Department of Out-patient and Emergency, Wuhan Institute for Tuberculosis Control, Wuhan Pulmonary Hospital, Wuhan, Hubei, China
| | - Haohao Zhou
- Department of Respiratory and Critical Care Medicine, Xiangzhou District People's Hospital, Xiangyang, Hubei, China
| | - Junping Yan
- Department of Respiratory, Third Hospital of Baotou City, Baotou, Inner Mongolia, China
| | - Xuejiao Yang
- Department of Respiratory and Critical Care Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Hao Yu
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiawei Zhou
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Dansha Zhou
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Lixia Qiu
- Hangzhou YITU Healthcare Technology Co., Ltd., Hangzhou, Zhejiang, China
| | - Nanshan Zhong
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
| | - Jian Wang
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
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Yao HY, Wan WG, Li X. A deep adversarial model for segmentation-assisted COVID-19 diagnosis using CT images. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2022; 2022:10. [PMID: 35194421 PMCID: PMC8830991 DOI: 10.1186/s13634-022-00842-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/27/2022] [Indexed: 06/14/2023]
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) is spreading rapidly around the world, resulting in a global pandemic. Imaging techniques such as computed tomography (CT) play an essential role in the diagnosis and treatment of the disease since lung infection or pneumonia is a common complication. However, training a deep network to learn how to diagnose COVID-19 rapidly and accurately in CT images and segment the infected regions like a radiologist is challenging. Since the infectious area is difficult to distinguish manually annotation, the segmentation results are time-consuming. To tackle these problems, we propose an efficient method based on a deep adversarial network to segment the infection regions automatically. Then, the predicted segment results can assist the diagnostic network in identifying the COVID-19 samples from the CT images. On the other hand, a radiologist-like segmentation network provides detailed information of the infectious regions by separating areas of ground-glass, consolidation, and pleural effusion, respectively. Our method can accurately predict the COVID-19 infectious probability and provide lesion regions in CT images with limited training data. Additionally, we have established a public dataset for multitask learning. Extensive experiments on diagnosis and segmentation show superior performance over state-of-the-art methods.
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Affiliation(s)
- Hai-yan Yao
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Anyang Institute of Technology, Anyang, China
| | - Wang-gen Wan
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Xiang Li
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
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219
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Ma Q, Yan J, Zhang J, Yu Q, Zhao Y, Liang C, Di D. Cost-Sensitive Uncertainty Hypergraph Learning for Identification of Lymph Node Involvement With CT Imaging. Front Med (Lausanne) 2022; 9:840319. [PMID: 35223932 PMCID: PMC8866560 DOI: 10.3389/fmed.2022.840319] [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: 12/21/2021] [Accepted: 01/17/2022] [Indexed: 12/09/2022] Open
Abstract
Lung adenocarcinoma (LUAD) is the most common type of lung cancer. Accurate identification of lymph node (LN) involvement in patients with LUAD is crucial for prognosis and making decisions of the treatment strategy. CT imaging has been used as a tool to identify lymph node involvement. To tackle the shortage of high-quality data and improve the sensitivity of diagnosis, we propose a Cost-Sensitive Uncertainty Hypergraph Learning (CSUHL) model to identify the lymph node based on the CT images. We design a step named "Multi-Uncertainty Measurement" to measure the epistemic and the aleatoric uncertainty, respectively. Given the two types of attentional uncertainty weights, we further propose a cost-sensitive hypergraph learning to boost the sensitivity of diagnosing, targeting task-driven optimization of the clinical scenarios. Extensive qualitative and quantitative experiments on the real clinical dataset demonstrate our method is capable of accurately identifying the lymph node and outperforming state-of-the-art methods across the board.
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Affiliation(s)
- Qianli Ma
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Jielong Yan
- The School of Software, Tsinghua University, Beijing, China
| | | | - Qiduo Yu
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Yue Zhao
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Chaoyang Liang
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Donglin Di
- The School of Software, Tsinghua University, Beijing, China
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Chest CT severity score: assessment of COVID‑19 severity and short-term prognosis in hospitalized Iranian patients. Wien Med Wochenschr 2022; 172:77-83. [PMID: 35133531 PMCID: PMC8824536 DOI: 10.1007/s10354-022-00914-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/05/2022] [Indexed: 11/05/2022]
Abstract
Background The aim of this study was to evaluate the value of chest computed tomography (CT) severity score in the assessment of coronavirus disease 2019 (COVID‑19) severity and short-term prognosis. Methods In this cross-sectional study, we evaluated all patients who were referred to our university hospital, from 21 May 2020 to 22 June 2020 with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcription-polymerase chain reaction (RT-PCR) test. The patients suspected of having other respiratory diseases including influenza, according to an infectious disease specialist, and those without chest CT scan were excluded. A chest CT was obtained for all patients between days 4 and 7 days after symptom onset. Chest CT severity score was also calculated based on the degree of involvement of the lung lobes as 0%, (0 points), 1–25% (1 point), 26–50% (2 points), 51–75% (3 points), and 76–100% (4 points). The CT severity score was quantified by summing the 5 lobe indices (range 0–20). The ROC curve analysis was performed for the clinical value of CT scores in distinguishing the patients based on the severity of disease (mild/moderate group versus severe group), ICU admission, intubation requirement, and mortality. Results Of the 148 patients included, 93 patients recovered, while 55 patients died (mortality rate 37%). The area under the curve of CT score for discriminating of recovered patients from deceased individuals was 0.726, and the optimal CT score threshold was 15.5 with 61.8% sensitivity and 76.3% specificity. The best CT score cut-off for discriminating of patients based on the severity of disease was 12.5 with 68.3% sensitivity and 72.7% specificity. In addition, with CT score cut-off of 15.5, sensitivities of 70.8% and 51.6% and specificities of 78% and 72.6% were observed for intubation and ICU admission, respectively. Conclusion CT scan and semiquantitative scoring method could be beneficial and applicable in predicting the patient’s condition.
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Cieszanowski A, Czekajska E, Giżycka B, Gruszczyńska K, Oronowicz-Jaśkowiak AS, Podgórska J, Serafin Z, Szurowska E, Walecki J. Indications for imaging studies in SARS-CoV-2 infected patients - recommendations of the Polish Medical Society of Radiology. Pol J Radiol 2022; 87:e63-e68. [PMID: 35280949 PMCID: PMC8906178 DOI: 10.5114/pjr.2022.113263] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 01/11/2022] [Indexed: 11/17/2022] Open
Abstract
The pandemic involving COVID-19 caused by the SARS-CoV-2 coronavirus, due to its severe symptoms and high transmission rate, has gone on to pose a control challenge for healthcare systems all around the world. We present the third version of the recommendations of the Polish Medical Society of Radiology (PMSR), presuming that our knowledge on COVID-19 will advance further rapidly, to the extent that further supplementation and modification will prove necessary. These recommendations involve rules of conduct, procedures, and safety measures that should be introduced in radiology departments, as well as indications for imaging studies.
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Affiliation(s)
- Andrzej Cieszanowski
- Department of Radiology I, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | | | - Barbara Giżycka
- Department of Diagnostic Imaging, Multispeciality Hospital Megrez Sp. z o.o., Tychy, Poland
| | - Katarzyna Gruszczyńska
- Department of Radiology and Nuclear Medicine, Faculty of Medical Science in Katowice, Medical University of Silesia, Poland
| | | | - Joanna Podgórska
- Department of Radiology I, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
| | - Edyta Szurowska
- 2 Department of Radiology, Medical University of Gdansk, Gdansk, Poland
| | - Jerzy Walecki
- Department of Diagnostic Imaging, Centre of Postgraduate Medical Education, Warsaw, Poland
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Muhammad K, Ullah H, Khan ZA, Saudagar AKJ, AlTameem A, AlKhathami M, Khan MB, Abul Hasanat MH, Mahmood Malik K, Hijji M, Sajjad M. WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments. Front Oncol 2022; 11:811355. [PMID: 35186717 PMCID: PMC8847175 DOI: 10.3389/fonc.2021.811355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/01/2021] [Indexed: 01/09/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has caused a major outbreak around the world with severe impact on health, human lives, and economy globally. One of the crucial steps in fighting COVID-19 is the ability to detect infected patients at early stages and put them under special care. Detecting COVID-19 from radiography images using computational medical imaging method is one of the fastest ways to diagnose the patients. However, early detection with significant results is a major challenge, given the limited available medical imaging data and conflicting performance metrics. Therefore, this work aims to develop a novel deep learning-based computationally efficient medical imaging framework for effective modeling and early diagnosis of COVID-19 from chest x-ray and computed tomography images. The proposed work presents "WEENet" by exploiting efficient convolutional neural network to extract high-level features, followed by classification mechanisms for COVID-19 diagnosis in medical image data. The performance of our method is evaluated on three benchmark medical chest x-ray and computed tomography image datasets using eight evaluation metrics including a novel strategy of cross-corpse evaluation as well as robustness evaluation, and the results are surpassing state-of-the-art methods. The outcome of this work can assist the epidemiologists and healthcare authorities in analyzing the infected medical chest x-ray and computed tomography images, management of the COVID-19 pandemic, bridging the early diagnosis, and treatment gap for Internet of Medical Things environments.
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Affiliation(s)
- Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, South Korea
| | - Hayat Ullah
- Department of Software, Sejong University, Seoul, South Korea
| | | | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Abdullah AlTameem
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Mohammed AlKhathami
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Muhammad Badruddin Khan
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Mozaherul Hoque Abul Hasanat
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Khalid Mahmood Malik
- Department of Computer Science & Engineering, Oakland University, Rochester, MI, United States
| | - Mohammad Hijji
- Faculty of Computers & Information Technology, Computer Science Department, University of Tabuk, Tabuk, Saudi Arabia
| | - Muhammad Sajjad
- Digital Image Processing Laboratory, Islamia College Peshawar, Peshawar, Pakistan
- Color and Visual Computing Lab, Department of Computer Science, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway
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Fang G, Liu H, Wu K, Wei T, Wang Q. Changing legislative thinking in China to better protect wild animals and human health. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2022; 36:e13837. [PMID: 34585442 DOI: 10.1111/cobi.13837] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/29/2021] [Accepted: 07/05/2021] [Indexed: 06/13/2023]
Abstract
The COVID-19 pandemic has exposed the inadequacy of China's legal protection of wildlife. Long-standing illegal wildlife trade in China and worldwide increases the risk of zoonotic infectious diseases. We sought to improve the understanding of China' legal system for the protection of wildlife, which has at its core the wildlife protection law, by systematically evaluating the laws and regulations of China's Wildlife Protection Framework. We examined how existing legal documents (e.g., the Wildlife Protection Law 2018) are directly or indirectly related to wildlife conservation. The inherent defects of wildlife protection legislation include a narrow scope of protection, insufficient public participation, and inconsistent enforcement among responsible agencies. Solutions to improve China's Wildlife Protection Law include expanding the legal protection of wildlife, and improving monitoring of wildlife protection. Strengthening legislation will be the basis for effective regulation of the use of wild animals. We advocate the establishment of a sound wildlife protection legal system for resolving conflicts between humans and wild animals and preventing zoonotic disease, such a system will have a profound impact on the sustainable development of China's wildlife resources.
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Affiliation(s)
- Guirong Fang
- College of Law and Political Science, Zhejiang Normal University, Jinhua, China
| | - Hong Liu
- College of Law and Political Science, Zhejiang Normal University, Jinhua, China
| | - Ka Wu
- College of Law and Political Science, Zhejiang Normal University, Jinhua, China
| | - Tongqing Wei
- College of Law and Political Science, Zhejiang Normal University, Jinhua, China
| | - Qing Wang
- College of Law and Political Science, Zhejiang Normal University, Jinhua, China
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Saify Nabiabad H, Amini M, Demirdas S. Specific delivering of RNAi using Spike's aptamer-functionalized lipid nanoparticles for targeting SARS-CoV-2: A strong anti-Covid drug in a clinical case study. Chem Biol Drug Des 2022; 99:233-246. [PMID: 34714580 PMCID: PMC8653378 DOI: 10.1111/cbdd.13978] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/24/2021] [Accepted: 10/24/2021] [Indexed: 12/15/2022]
Abstract
Coronavirus (SARS-CoV-2) as a global pandemic has attracted the attention of many scientific centers to find the right treatment. We expressed and purified the recombinant receptor-binding domain (RBD) of the SARS-CoV-2 spike (S) protein, and specific RBD aptamers were designed using SELEX method. RNAi targeting nucleocapsid phosphoprotein was synthesized and human lung cells were inoculated with aptamer-functionalized lipid nanoparticles (LNPs) containing RNAi. The results demonstrated that RBD aptamer having KD values of 0.290 nm possessed good affinity. Based on molecular docking and efficacy prediction analysis, siRNA molecule was showed the best action. LNPs were appropriately functionalized by aptamer and contained RNAi molecules. Antiviral assay using q-PCR and ELISA demonstrated that LNP functionalized with 35 µm Apt and containing 30 nm RNAi/ml of cell culture had the best antiviral activity compared to other concentrations. Applied aptamer in the nanocarrier has two important functions. First, it can deliver the drug (RNAi) to the surface of epithelial cells. Second, by binding to the SARS-CoV-2 spike protein, it inhibits the virus entrance into cells. Our data reveal an interaction between the aptamer and the virus, and RNAi targeted the virus RNA. CT scan and the clinical laboratory tests in a clinical case study, a 36-year old man who presented with severe SARS-CoV-2, demonstrated that inhalation of 10 mg Apt-LNPs-RNAi nebulized/day for six days resulted in an improvement in consolidation and ground-glass opacity in lungs on the sixth day of treatment. Our findings suggest the treatment of SARS-CoV-2 infection through inhalation of Aptamer-LNPs-RNAi.
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Affiliation(s)
| | - Massoume Amini
- Department of BiotechnologyBu‐Ali Sina UniversityHamadanIran
| | - Serwet Demirdas
- Department of Clinical GeneticsErasmus Medical CentreRotterdamthe Netherlands
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225
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Miyashita N, Nakamori Y, Ogata M, Fukuda N, Yamura A, Ishiura Y, Nomura S. Early identification of novel coronavirus (COVID-19) pneumonia using clinical and radiographic findings. J Infect Chemother 2022; 28:718-721. [PMID: 35190258 PMCID: PMC8828417 DOI: 10.1016/j.jiac.2022.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/16/2021] [Accepted: 02/07/2022] [Indexed: 11/15/2022]
Abstract
Introduction The Japanese Respiratory Society (JRS) scoring system is a useful tool for identifying Mycoplasma pneumoniae pneumonia. Most COVID-19 pneumonia in non-elderly patients (aged <60 years) are classified as atypical pneumonia using the JRS scoring system. We evaluated whether physicians could distinguish between COVID-19 pneumonia and M. pneumoniae pneumonia using chest computed tomography (CT) findings. In addition, we investigated chest CT findings if there is a difference between the variant and non-variant strain. Methods This study was conducted at five institutions and assessed a total of 823 patients with COVID-19 pneumonia (335 had lineage B.1.1.7.) and 100 patients with M. pneumoniae pneumonia. Results In COVID-19 pneumonia, at the first CT examination, peripheral, bilateral ground-glass opacity (GGO) with or without consolidation or crazy-paving pattern was observed frequently. GGO frequently had a round morphology (39.2%). No differences were observed in the radiological findings between the non-B.1.1.7 groups and B.1.1.7 groups. The frequency of pleural effusion, lymphadenopathy, bronchial wall thickening and nodules (tree-in-bud and centrilobular) was low. In contrast to COVID-19 pneumonia, bronchial wall thickening (84%) was observed most frequently, followed by nodules (81%) in M. pneumoniae pneumonia. These findings were significantly higher in M. pneumoniae pneumonia than COVID-19 pneumonia. Conclusions Our results demonstrated that a combination of the JRS scoring system and chest CT findings is useful for the rapid presumptive diagnosis of COVID-19 pneumonia in patients aged <60 years. However, this clinical and radiographic diagnosis is not adapted to elderly people.
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226
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Liu X, Yuan Q, Gao Y, He K, Wang S, Tang X, Tang J, Shen D. Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images. PATTERN RECOGNITION 2022; 122:108341. [PMID: 34565913 PMCID: PMC8452156 DOI: 10.1016/j.patcog.2021.108341] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/23/2021] [Accepted: 09/18/2021] [Indexed: 05/19/2023]
Abstract
Segmentation of infections from CT scans is important for accurate diagnosis and follow-up in tackling the COVID-19. Although the convolutional neural network has great potential to automate the segmentation task, most existing deep learning-based infection segmentation methods require fully annotated ground-truth labels for training, which is time-consuming and labor-intensive. This paper proposed a novel weakly supervised segmentation method for COVID-19 infections in CT slices, which only requires scribble supervision and is enhanced with the uncertainty-aware self-ensembling and transformation-consistent techniques. Specifically, to deal with the difficulty caused by the shortage of supervision, an uncertainty-aware mean teacher is incorporated into the scribble-based segmentation method, encouraging the segmentation predictions to be consistent under different perturbations for an input image. This mean teacher model can guide the student model to be trained using information in images without requiring manual annotations. On the other hand, considering the output of the mean teacher contains both correct and unreliable predictions, equally treating each prediction in the teacher model may degrade the performance of the student network. To alleviate this problem, the pixel level uncertainty measure on the predictions of the teacher model is calculated, and then the student model is only guided by reliable predictions from the teacher model. To further regularize the network, a transformation-consistent strategy is also incorporated, which requires the prediction to follow the same transformation if a transform is performed on an input image of the network. The proposed method has been evaluated on two public datasets and one local dataset. The experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.
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Affiliation(s)
- Xiaoming Liu
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China
| | - Quan Yuan
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China
| | - Yaozong Gao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Kelei He
- Medical School, Nanjing University, Nanjing, China
- National Institute of Healthcare Data Science at Nanjing University, Nanjing, China
| | - Shuo Wang
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China
| | - Xiao Tang
- Department of Medical Imaging, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Jinshan Tang
- Department of Health Administration and Policy, George Mason University, Fairfax, VA, 22030, USA
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
- Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea
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227
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Rai DK, Priyadarshi RK, Karmaker S. Study to assess aetiology, clinical and imaging characteristics of post Covid-19 pulmonary cavitation. J Family Med Prim Care 2022; 11:739-743. [PMID: 35360787 PMCID: PMC8963656 DOI: 10.4103/jfmpc.jfmpc_1425_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 12/10/2021] [Accepted: 12/16/2021] [Indexed: 12/03/2022] Open
Abstract
Objective The aim of this study is to determine the aetiology and characteristics of pulmonary cavities that developed in patients recovering from COVID-19 infection. Materials and Methods Between 1st May 2021 and 30st June 2021, we found 9 post COVID-19 patients who developed lung cavities on chest radiograph or CT during the follow-up period. These patients underwent routine blood examination, sputum examination and bronchoscopy to identify the aetiologies for the lung cavities. Results The duration from the onset of COVID-19 symptoms to the detection of lung cavities ranged from 18 to 82 days. Out of 7 patients, 4 had recovered from severe COVID-19 disease, 2 from moderate and 1 from mild disease. After the diagnostic workup, 5 patients were found to have COVID-19 associated pulmonary aspergillosis (CAPA), 1 patient with mucormycosis and 1 patient with mycobacterium infection. Two patients with CAPA also had bacterial infection; sputum culture from both these patients grew Klebsiella pneumonia. Conclusion Lung cavities can develop in patients recovering from COVID-19 pneumonia and fungal infection is the most common cause for such cavities.
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228
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Liao X, Li D, Liu Z, Ma Z, Zhang L, Dong J, Shi Y, Gu X, Zheng G, Huang L, Yuan L, Cao J, Shu D, Yang X, He Q, Li G, Zhang Z, Liu L. Pulmonary Sequelae in Patients After Recovery From Coronavirus Disease 2019: A Follow-Up Study With Chest CT. Front Med (Lausanne) 2022; 8:686878. [PMID: 35096849 PMCID: PMC8794727 DOI: 10.3389/fmed.2021.686878] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: The pulmonary sequelae of coronavirus disease 2019 (COVID-19) have not been comprehensively evaluated. We performed a follow-up study analyzing chest computed tomography (CT) findings of COVID-19 patients at 3 and 6 months after hospital discharge. Methods: Between February 2020 and May 2020, a total of 273 patients with COVID-19 at the Shenzhen Third People's Hospital were recruited and followed for 6 months after discharge. Chest CT scanning was performed with the patient in the supine position at end-inspiration. A total of 957 chest CT scans was obtained at different timepoints. A semi-quantitative score was used to assess the degree of lung involvement. Results: Most chest CT scans showed bilateral lung involvement with peripheral location at 3 and 6 months follow-up. The most common CT findings were ground-glass opacity and parenchymal band, which were found in 136 (55.3%) and 94 (38.2%) of the 246 patients at 3 months follow-up, and 82 (48.2%) and 76 (44.7%) of 170 patients at 6 months follow-up, respectively. The number of lobes involved and the total CT severity score declined over time. The total CT score gradually increased with the increasement of disease severity at both 3 months follow-up (trend test P < 0.001) and 6 months follow-up (trend test P < 0.001). Patients with different disease severity represented diverse CT patterns over time. Conclusions: The most common CT findings were ground-glass opacity and parenchymal bands at the 3 and 6 months follow-up. Patients with different disease severity represent diverse CT manifestations, indicating the necessary for long-term follow-up monitoring of patients with severe and critical conditions.
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Affiliation(s)
- Xuejiao Liao
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China.,National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Dapeng Li
- National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China.,School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Zhi Liu
- Department of the Third Pulmonary Disease, Shenzhen Third People's Hospital, Shenzhen, China
| | - Zhenghua Ma
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Lina Zhang
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Jingke Dong
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Yirong Shi
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Xiaowen Gu
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Guangping Zheng
- Department of Radiology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Ling Huang
- Department of the Third Pulmonary Disease, Shenzhen Third People's Hospital, Shenzhen, China
| | - Lijun Yuan
- School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Jing Cao
- School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Dan Shu
- National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Xiangyi Yang
- National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Qing He
- School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Guobao Li
- Department of the Third Pulmonary Disease, Shenzhen Third People's Hospital, Shenzhen, China
| | - Zheng Zhang
- National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China.,School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Lei Liu
- School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
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Tavakolifard N, Moeini M, Haddadpoor A, Heidari K, Rezaee M, Amini Z. Clinical Symptoms of COVID-19 and Their Association with Disease Outcome. Adv Biomed Res 2022; 11:2. [PMID: 35284355 PMCID: PMC8906090 DOI: 10.4103/abr.abr_79_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/15/2021] [Accepted: 05/24/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND COVID-19 pandemic placed immense pressure on health systems. The current study aimed to evaluate the symptoms of COVID-19 in Isfahan province and their association with disease outcome. MATERIALS AND METHODS In this cross-sectional study, which was conducted on patients with a definite diagnosis of COVID-19, as a part of the investigations performed by the Deputy for Public Health of the Isfahan University of Medical Sciences, the frequency of underlying diseases, and general, respiratory, gastrointestinal, neurological, renal, cardiac, dermal, hearing symptoms are assessed using a checklist. The participants were followed up 1 month after definitive diagnosis, and the outcome of the disease (recovery or death) was recorded. RESULTS Of 300 patients, 143 (47.4%) were male and 157 (52.6%) were female. The mean age of patients was 43 ± 17.84 years. Muscle pain and fatigue were the most common early symptoms (63% and 60.3%, respectively). Age, sex, level of education, and occupation of patients and general, respiratory, and gastrointestinal symptoms and underlying diseases of respiratory disease showed a statistically significant association with the disease outcome (P < 0.05). CONCLUSION General, respiratory, and gastrointestinal symptoms were associated with an increased risk of death due to COVID-19. General, visual, and hearing symptoms, and diabetes, cardiovascular diseases, and respiratory disease had a statistically significant association with the hospitalization of patients.
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Affiliation(s)
- Negah Tavakolifard
- Department of Community and Family Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mina Moeini
- Vice Chancellery for Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Asefeh Haddadpoor
- Vice Chancellery for Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Kamal Heidari
- Department of Community and Family Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran,Vice Chancellery for Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mostafa Rezaee
- Vice Chancellery for Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zahra Amini
- Department of Community and Family Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran,Address for correspondence: Dr. Zahra Amini, Department of Community and Family Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. E-mail:
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Ajmera P, Kharat A, Dhirawani S, Khaladkar SM, Kulkarni V, Duddalwar V, Lamghare P, Rathi S. Evaluating the Association Between Comorbidities and COVID-19 Severity Scoring on Chest CT Examinations Between the Two Waves of COVID-19: An Imaging Study Using Artificial Intelligence. Cureus 2022; 14:e21656. [PMID: 35233327 PMCID: PMC8881892 DOI: 10.7759/cureus.21656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 11/25/2022] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS). Methodology This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis. Results The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis. Conclusions This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous.
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Zhang L, Li M, Wang Z, Sun P, Wei S, Zhang C, Wu H, Bai H. Cardiovascular Risk After SARS-CoV-2 Infection Is Mediated by IL18/IL18R1/HIF-1 Signaling Pathway Axis. Front Immunol 2022; 12:780804. [PMID: 35069552 PMCID: PMC8766743 DOI: 10.3389/fimmu.2021.780804] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/16/2021] [Indexed: 01/10/2023] Open
Abstract
Objectives Currently, cardiovascular risk associated with COVID-19 has been brought to people's attention, but the mechanism is not clear. The aim of this study is to elucidate the mechanisms based on multiple omics data. Methodology Weighted gene co-expression network analysis (WGCNA) was used to identify key pathways. Combination analysis with aneurysm and atherosclerosis related pathways, hypoxia induced factor-1 (HIF-1) signaling were identified as key pathways of the increased cardiovascular risk associated with COVID-19. ScMLnet algorithm based on scRNA-seq was used to explore the regulation of HIF-1 pathway by intercellular communication. Proteomic analysis was used to detect the regulatory mechanisms between IL18 and HIF-1 signaling pathway. Pseudo time locus analysis was used to study the regulation of HIF1 signaling pathway in macrophages and vascular smooth muscle cells (VSMC) phenotypic transformation. The Virtual Inference of protein-activity by Enriched Regulon (VIPER) analysis was used to study the activity of regulatory proteins. Epigenetic analysis based on methylation revealed epigenetic changes in PBMC after SARS-CoV-2 infection. Potential therapeutic compounds were explored by using Cmap algorithm. Results HIF-1 signaling pathway is a common key pathway for aneurysms, atherosclerosis and SARS-CoV-2 infection. Intercellular communication analysis showed that macrophage-derived interleukin-18 (IL-18) activates the HIF-1 signaling pathway through IL18R1. Proteomic analysis showed that IL18/IL18R1 promote NF-κB entry into the nucleus, and activated the HIF-1 signaling pathway. Macrophage-derived IL18 promoted the M1 polarization of macrophages and the syntactic phenotype transformation of VSMCs. MAP2K1 mediates the functional regulation of HIF-1 signaling pathway in various cell types. Epigenetic changes in PBMC after COVID-19 infection are characterized by activation of the type I interferon pathway. MEK inhibitors are the promising compounds for the treatment of HIF-1 overactivation. Conclusions The IL18/IL18R1/HIF1A axis is expected to be an therapeutic target for cardiovascular protection after SARS-CoV-2 infection. MEK inhibitors may be an choice for cardiovascular protection after SARS-COV-2 infection.
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Affiliation(s)
- Liwei Zhang
- Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mingxing Li
- Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhiwei Wang
- Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peng Sun
- Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shunbo Wei
- Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Cong Zhang
- Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Haoliang Wu
- Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hualong Bai
- Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Vascular Physiology and Applied Research Laboratory of Zhengzhou City, Zhengzhou, China
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Huang Y, Guo L, Chen J, Wu M, Zhang C, Liu Z, Li J, Li K, Xiong Z, Wu Q, Li Z, Luo K, Yuan W, Wu X. Serum Lactate Dehydrogenase Level as a Prognostic Factor for COVID-19: A Retrospective Study Based on a Large Sample Size. Front Med (Lausanne) 2022; 8:671667. [PMID: 35059407 PMCID: PMC8763698 DOI: 10.3389/fmed.2021.671667] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 11/29/2021] [Indexed: 01/08/2023] Open
Abstract
Background: In this study, we investigated the relationship between serum lactate dehydrogenase (LDH) level and disease progression and prognosis of patients with COVID-19. Methods: We retrospectively reviewed the information of 1,751 patients with COVID-19 from Leishenshan Hospital in Wuhan, China. Univariate and multivariate Cox regression analyses as well as Logistics regression analyses, and Kaplan-Meier curves were used to determine the association between LDH levels and the prognosis of COVID-19 patients. Results: LDH was an independent risk factor for in-hospital death no matter it was taken as classified variable and continuous variable (all P = 0.001) but not for severe or critical illness status. The Kaplan-Meier curves for LDH level showed that an elevated level of LDH was associated with in-hospital death. Conclusions: In patients with COVID-19, the increased LDH level is associated with a higher risk of negative clinical prognosis and higher mortality. This will provide a reference for clinicians and researchers to understand, diagnose, and treat patients with COVID-19. Further prospective studies with larger sample sizes are needed to verify these findings.
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Affiliation(s)
- Yihui Huang
- Department of Plastic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Liang Guo
- Department of Plastic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jiwei Chen
- Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Meng Wu
- Department of Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Chao Zhang
- Department of Cardiovascular Surgery, Tongji Medical College, Union Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Zeming Liu
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinpeng Li
- Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Kun Li
- Department of Hepatobiliary and Pancreatic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zhongwei Xiong
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Qian Wu
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zhengwei Li
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Kuan Luo
- Department of Neurosurgery, Wuhan Puren Hospital, Wuhan, China
| | - Weiwei Yuan
- Department of Plastic and Cosmetic Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Xiaohui Wu
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
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233
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Li L, Ma CJ, Chang YF, Yang SY, Tang YX, Wang LH. The characteristics of overseas imported COVID-19 cases and the effectiveness of screening strategies in Beijing, China. BMC Infect Dis 2022; 22:59. [PMID: 35039000 PMCID: PMC8762985 DOI: 10.1186/s12879-021-06998-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 12/21/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND In March 2020, the WHO declared the novel coronavirus outbreak a global pandemic. While great success in coronavirus disease 2019 (COVID-19) control has been achieved in China, imported cases have become a major challenge. This study aimed to describe the epidemiological and clinical characteristics of imported COVID-19 cases and to assess the effectiveness of screening strategies in Beijing, China. METHODS This retrospective study included all imported cases transferred to Beijing Ditan Hospital from 29 February to 20 March 2020 who were screened by both chest computed tomography (CT) and reverse-transcriptase-polymerase chain reaction (RT-PCR) at the initial presentation. Demographic, clinical and laboratory data, in addition to chest CT imaging, were collected and analysed. RESULTS In total, 2545 cases were included, among which 71 (2.8%) were finally diagnosed with laboratory-confirmed COVID-19. The majority 63 (88.7%) were from Europe. The most common initial symptoms were cough and fever, which accounted for 49.3% and 42.3%, respectively. Only four cases (5.6%) had lymphocytopenia, and thirteen cases (18.3%) demonstrated elevated levels of C-reactive protein (CRP). All cases had normal serum levels of procalcitonin (PCT). At initial presentation, among the 71 confirmed cases, 59 (83.1%) had a positive RT-PCR assay, and 35 (49.3%) had a positive chest CT. Twelve (16.9%) had a negative RT-PCR assay but a positive chest CT. CONCLUSIONS A combination of RT-PCR and chest CT is an effective strategy for the screening of imported COVID-19 cases. Our findings provide important information and clinical evidence about the infection control of imported COVID-19 cases.
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Affiliation(s)
- Li Li
- Clinical and Research Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China
- Clinical Center for HIV/AIDS, Capital Medical University, Beijing, 100015, China
| | - Cheng-Jie Ma
- Clinical and Research Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China
- Clinical Center for HIV/AIDS, Capital Medical University, Beijing, 100015, China
| | - Yu-Fei Chang
- Clinical and Research Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China
| | - Si-Yuan Yang
- Clinical and Research Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China
- Clinical Center for HIV/AIDS, Capital Medical University, Beijing, 100015, China
| | - Yun-Xia Tang
- Clinical and Research Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China
- Clinical Center for HIV/AIDS, Capital Medical University, Beijing, 100015, China
| | - Ling-Hang Wang
- Clinical and Research Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
- Clinical Center for HIV/AIDS, Capital Medical University, Beijing, 100015, China.
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O’Donnell KL, Clancy CS, Griffin AJ, Shifflett K, Gourdine T, Thomas T, Long CM, Furuyama W, Marzi A. Optimization of Single-Dose VSV-Based COVID-19 Vaccination in Hamsters. Front Immunol 2022; 12:788235. [PMID: 35069564 PMCID: PMC8770858 DOI: 10.3389/fimmu.2021.788235] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/14/2021] [Indexed: 01/08/2023] Open
Abstract
The ongoing COVID-19 pandemic has resulted in global effects on human health, economic stability, and social norms. The emergence of viral variants raises concerns about the efficacy of existing vaccines and highlights the continued need for the development of efficient, fast-acting, and cost-effective vaccines. Here, we demonstrate the immunogenicity and protective efficacy of two vesicular stomatitis virus (VSV)-based vaccines encoding the SARS-CoV-2 spike protein either alone (VSV-SARS2) or in combination with the Ebola virus glycoprotein (VSV-SARS2-EBOV). Intranasally vaccinated hamsters showed an early CD8+ T cell response in the lungs and a greater antigen-specific IgG response, while intramuscularly vaccinated hamsters had an early CD4+ T cell and NK cell response. Intranasal vaccination resulted in protection within 10 days with hamsters not showing clinical signs of pneumonia when challenged with three different SARS-CoV-2 variants. This data demonstrates that VSV-based vaccines are viable single-dose, fast-acting vaccine candidates that are protective from COVID-19.
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Affiliation(s)
- Kyle L. O’Donnell
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
| | - Chad S. Clancy
- Rocky Mountain Veterinary Branch, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
| | - Amanda J. Griffin
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
| | - Kyle Shifflett
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
| | - Tylisha Gourdine
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
| | - Tina Thomas
- Rocky Mountain Veterinary Branch, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
| | - Carrie M. Long
- Laboratory of Bacteriology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
| | - Wakako Furuyama
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
| | - Andrea Marzi
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
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235
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Diaz-Miron J, Reppucci ML, Weinman J, Kaizer A, Annam A, Orsborn J, Steward L, Wilson J, Bensard D. The use of ultrasound in establishing COVID-19 infection as part of a trauma evaluation. Emerg Radiol 2022; 29:227-234. [PMID: 34988751 PMCID: PMC8731214 DOI: 10.1007/s10140-021-02005-1] [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: 10/14/2021] [Accepted: 11/29/2021] [Indexed: 11/25/2022]
Abstract
Purpose The use of lung ultrasound for diagnosis of COVID-19 has emerged during the pandemic as a beneficial diagnostic modality due to its rapid availability, bedside use, and lack of radiation. This study aimed to determine if routine ultrasound (US) imaging of the lungs of trauma patients with COVID-19 infections who undergo extended focused assessment with sonography for trauma (EFAST) correlates with computed tomography (CT) imaging and X-ray findings, as previously reported in other populations. Methods This was a prospective, observational feasibility study performed at two level 1 trauma centers. US, CT, and X-ray imaging were retrospectively reviewed by a surgical trainee and a board-certified radiologist to determine any correlation of imaging findings in patients with active COVID-19 infection. Results There were 53 patients with lung US images from EFAST available for evaluation and COVID-19 testing. The overall COVID-19 positivity rate was 7.5%. COVID-19 infection was accurately identified by one patient on US by the trainee, but there was a 15.1% false-positive rate for infection based on the radiologist examination. Conclusions Evaluation of the lung during EFAST cannot be used in the trauma setting to identify patients with active COVID-19 infection or to stratify patients as high or low risk of infection. This is likely due to differences in lung imaging technique and the presence of concomitant thoracic injury.
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Affiliation(s)
- Jose Diaz-Miron
- Division of Pediatric Surgery, Department of Surgery, Children's Hospital Colorado, University of Colorado School of Medicine, 13123 E 16th Ave, B-323, Aurora, CO, 80045, USA.
| | - Marina L Reppucci
- Division of Pediatric Surgery, Department of Surgery, Children's Hospital Colorado, University of Colorado School of Medicine, 13123 E 16th Ave, B-323, Aurora, CO, 80045, USA
| | - Jason Weinman
- Division of Pediatric Radiology, Department of Radiology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Alexander Kaizer
- Center for Innovative Design and Analysis, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Aparna Annam
- Division of Pediatric Radiology, Department of Radiology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Jonathan Orsborn
- Division of Pediatric Emergency Medicine, Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Lauren Steward
- Division of Gastrointestinal, Trauma, and Endocrine Surgery, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Juliana Wilson
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Denis Bensard
- Division of Pediatric Surgery, Department of Surgery, Children's Hospital Colorado, University of Colorado School of Medicine, 13123 E 16th Ave, B-323, Aurora, CO, 80045, USA
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Kirkpatrick AW, McKee JL, Ball CG, Ma IWY, Melniker LA. Empowering the willing: the feasibility of tele-mentored self-performed pleural ultrasound assessment for the surveillance of lung health. Ultrasound J 2022; 14:2. [PMID: 34978611 PMCID: PMC9417136 DOI: 10.1186/s13089-021-00250-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2021] [Indexed: 11/23/2022] Open
Abstract
Background SARS-CoV-2 infection, manifesting as COVID-19 pneumonia, constitutes
a global pandemic that is disrupting health-care systems. Most patients who are
infected are asymptomatic/pauci-symptomatic can safely self-isolate at home.
However, even previously healthy individuals can deteriorate rapidly with
life-threatening respiratory failure characterized by disproportionate hypoxemic
failure compared to symptoms. Ultrasound findings have been proposed as an early
indicator of progression to severe disease. Furthermore, ultrasound is a safe
imaging modality that can be performed by novice users remotely guided by experts.
We thus examined the feasibility of utilizing common household
informatic-technologies to facilitate self-performed lung ultrasound. Methods A lung ultrasound expert remotely mentored and guided participants
to image their own chests with a hand-held ultrasound transducer. The results were
evaluated in real time by the mentor, and independently scored by three
independent experts [planned a priori]. The primary outcomes were feasibility in
obtaining good-quality interpretable images from each anatomic location
recommended for COVID-19 diagnosis. Results Twenty-seven adults volunteered. All could be guided to obtain
images of the pleura of the 8 anterior and lateral lung zones (216/216 attempts).
These images were rated as interpretable by the 3 experts in 99.8% (647/648) of
reviews. Fully imaging one’s posterior region was harder; only 108/162 (66%) of
image acquisitions was possible. Of these, 99.3% of images were interpretable in
blinded evaluations. However, 52/54 (96%) of participants could image their lower
posterior lung bases, where COVID-19 is most common, with 99.3% rated as
interpretable. Conclusions Ultrasound-novice adults at risk for COVID-19 deterioration can be
successfully mentored using freely available software and low-cost ultrasound
devices to provide meaningful lung ultrasound surveillance of themselves that
could potentially stratify asymptomatic/paucisymptomatic patients with early risk
factors for serious disease. Further studies examining practical logistics should
be conducted. Trial Registration: ID
ISRCTN/77929274 on 07/03/2015. Supplementary Information The online version contains supplementary material available at 10.1186/s13089-021-00250-6.
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Affiliation(s)
- Andrew W Kirkpatrick
- TeleMentored Ultrasound Supported Medical Interventions (TMUSMI) Research Group, University of Calgary, Calgary, AB, Canada. .,Departments of Surgery, University of Calgary, Calgary, AB, Canada. .,Departments of Critical Care Medicine, University of Calgary, Calgary, AB, Canada. .,Regional Trauma Services, EG 23, Foothills Medical Centre, 1403 29 St NW, Calgary, AB, T2N 2T9, Canada. .,Canadian Forces Medical Services, University of Calgary, Calgary, AB, Canada.
| | - Jessica L McKee
- TeleMentored Ultrasound Supported Medical Interventions (TMUSMI) Research Group, University of Calgary, Calgary, AB, Canada
| | - Chad G Ball
- Departments of Surgery, University of Calgary, Calgary, AB, Canada.,Regional Trauma Services, EG 23, Foothills Medical Centre, 1403 29 St NW, Calgary, AB, T2N 2T9, Canada
| | - Irene W Y Ma
- W21C, University of Calgary, Calgary, AB, Canada.,John A. Buchanan Chair, Division of General Internal Medicine, University of Calgary, Calgary, AB, Canada
| | - Lawrence A Melniker
- Department of Emergency Medicine, New York-Presbyterian Brooklyn Methodist Hospital, New York, NY, USA
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237
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Affiliation(s)
- Yufan Zhang
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, Key Laboratory of Bioactive Materials, Ministry of Education, and College of Life Sciences Nankai University Tianjin China
| | - Dan Ding
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, Key Laboratory of Bioactive Materials, Ministry of Education, and College of Life Sciences Nankai University Tianjin China
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Patil S, Toshniwal S, Acharya A, Narwade G. Role of “Ferritin” in COVID-19 pneumonia: Sensitive marker of inflammation, predictor of mechanical ventilation, and early marker of post-COVID-lung fibrosis – A prospective, observational, and interventional study in a tertiary care setting in India. MULLER JOURNAL OF MEDICAL SCIENCES AND RESEARCH 2022. [DOI: 10.4103/mjmsr.mjmsr_19_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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239
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Huq AKO, Bazlur Rahim ANM, Moktadir SMG, Uddin I, Manir MZ, Siddique MAB, Islam K, Islam MS. Integrated Nutritional Supports for Diabetic Patients During COVID-19 Infection: A Comprehensive Review. Curr Diabetes Rev 2022; 18:e022821191889. [PMID: 33645486 DOI: 10.2174/1573399817666210301103233] [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: 06/18/2020] [Revised: 11/26/2020] [Accepted: 12/21/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND Diabetes mellitus is an endocrine metabolic disorder, which affects the major organs in human and comorbid with others. Besides, diabetic patients are more prone to various infectious diseases as well as COVID-19 sporadic infection which is a high risk for patients with diabetes mellitus. To combat these infections and comorbid situations, an integrated balanced nutritional supportive could help in maintaining sound health and increase immunity for prevention and management of such type of viral infections. OBJECTIVES While information regarding nutritional supports in COVID-19 pandemic in diabetic patients is not available, this review aimed to accumulate the evidence from previous publications where studied about nutrition-based supports or interventions for viral diseases with special emphasis on respiratory infections. METHODS For reviewing, searches are done for getting journal articles into Google Scholar, Pub Med/Medline, Database of Open Access Journal and Science Direct for relevant data and information. RESULTS Integrated nutritional supports of both macronutrients and micronutrients guidelines, including home-based physical exercise schedule, is summarized in this comprehensive review for possible prevention and management of diabetic patients in COVID-19 infections. The immuneboosting benefits of some vitamins, trace elements, nutraceuticals and probiotics in viral infections of diabetic patients are also included. CONCLUSION There is an urgent need for a healthy diet and integrated nutritional supports with home-based physical activities for diabetic patients during the self-isolation period of COVID-19 Infection.
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Affiliation(s)
- A K Obidul Huq
- Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh
| | - Abu Naim Mohammad Bazlur Rahim
- Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh
| | - S M Golam Moktadir
- Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh
| | - Ielias Uddin
- Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh
| | - Mohammad Zahidul Manir
- Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh
| | - Muhammad Abu Bakr Siddique
- Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh
| | - Khaleda Islam
- Institute of Nutrition and Food Science, University of Dhaka, Dhaka-1000, Bangladesh
| | - Md Sirajul Islam
- Department of Environmental Science and Resource Management, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh
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240
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Yuan Y, Sun C, Tang X, Cheng C, Mombaerts L, Wang M, Hu T, Sun C, Guo Y, Li X, Xu H, Ren T, Xiao Y, Xiao Y, Zhu H, Wu H, Li K, Chen C, Liu Y, Liang Z, Cao Z, Zhang HT, Paschaldis IC, Liu Q, Goncalves J, Zhong Q, Yan L. Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China. ENGINEERING (BEIJING, CHINA) 2022; 8:116-121. [PMID: 33282444 PMCID: PMC7695569 DOI: 10.1016/j.eng.2020.10.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/04/2020] [Accepted: 10/11/2020] [Indexed: 05/14/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.
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Affiliation(s)
- Ye Yuan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Chuan Sun
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiuchuan Tang
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Cheng Cheng
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Laurent Mombaerts
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval L-4367, Luxembourg
| | - Maolin Wang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Tao Hu
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chenyu Sun
- AMITA Health Saint Joseph Hospital Chicago, Chicago, IL 60657, USA
| | - Yuqi Guo
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiuting Li
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Xu
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Tongxin Ren
- Huazhong University of Science and Technology-Wuxi Research Institute, Wuxi 214174, China
| | - Yang Xiao
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yaru Xiao
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Hongling Zhu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Honghan Wu
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Kezhi Li
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Chuming Chen
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yingxia Liu
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Zhichao Liang
- Department of Infectious Diseases, Shenzhen Key Laboratory of Pathogenic Microbiology and Immunology, National Clinical Research Center for Infectious Disease, The Third People's Hospital of Shenzhen (Second Hospital Affiliated with the Southern University of Science and Technology), Shenzhen 518055, China
| | - Zhiguo Cao
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hai-Tao Zhang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ioannis Ch Paschaldis
- Department of Electrical and Computer Engineering & Division of Systems Engineering & Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Quanying Liu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jorge Goncalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval L-4367, Luxembourg
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 1TN, UK
| | - Qiang Zhong
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Li Yan
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Xiong F, Cheng L, Min Y, Tu C, Mao D, Yang Y, Song Y, Wan S, Ding Y. An analysis on the clinical features of maintenance hemodialysis patients with coronavirus disease 2019: A single center study. INTEGRATIVE MEDICINE IN NEPHROLOGY AND ANDROLOGY 2022. [PMCID: PMC9549769 DOI: 10.4103/imna.imna_6_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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242
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Malyutin DS, Koneva ES, Achkasov EE, Kostenko AB, Tsvetkova AV, Elfimov MA, Eremenko AA, Bazarov DV, Shestakov AV, Korchazhkina NB. [Influence of therapeutic exercises and hardware massage in electrostatic field on lung damage in patients with novel coronavirus pneumonia]. VOPROSY KURORTOLOGII, FIZIOTERAPII, I LECHEBNOI FIZICHESKOI KULTURY 2022; 99:43-50. [PMID: 36083817 DOI: 10.17116/kurort20229904243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To analyze the efficacy and safety of therapeutic exercises and chest hardware massage in electrostatic field in patients with COVID-associated viral pneumonia. MATERIAL AND METHODS We retrospectively analyzed 1551 patients admitted to the Clinical Hospital No. 1 (MEDSI Group JSC) with COVID-associated pneumonia between April 01, 2020 and June 15, 2021 (ICD-10 U07.1 and U07.2). Considering inclusion and exclusion criteria, we enrolled 153 patients. All patients were divided into comparable groups and subgroups depending on the methods of rehabilitation treatment and CT stage of viral pneumonia. Lung damage was assessed semi-automatically using Philips Portal v11 COPD software. Rehabilitation measures included therapeutic exercises and chest hardware massage in electrostatic field. therapeutic exercises. RESULTS Therapeutic exercises significantly reduced severity of lung damage in patients with viral pneumonia CT-2 and no oxygen support (from 28.05% [28; 29.5] at admission to 15.3% [14.2; 19.3] at discharge). It was not observed in patients without rehabilitation treatment and in patients undergoing therapeutic exercises and massage in electrostatic field. CONCLUSION Therapeutic exercises in patients with COVID-19 and baseline lung damage > 25% and < 50% (CT-2 stage) significantly reduce severity of lung damage at discharge compared to the control group.
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Affiliation(s)
- D S Malyutin
- Group of companies MEDSI, Otradnoe, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - E S Koneva
- Group of companies MEDSI, Otradnoe, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - E E Achkasov
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - A B Kostenko
- Group of companies MEDSI, Otradnoe, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - A V Tsvetkova
- Group of companies MEDSI, Otradnoe, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - M A Elfimov
- Petrovsky National Research Center of Surgery, Moscow, Russia
| | - A A Eremenko
- Petrovsky National Research Center of Surgery, Moscow, Russia
| | - D V Bazarov
- Petrovsky National Research Center of Surgery, Moscow, Russia
| | - A V Shestakov
- Petrovsky National Research Center of Surgery, Moscow, Russia
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243
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Xu GX, Liu C, Liu J, Ding Z, Shi F, Guo M, Zhao W, Li X, Wei Y, Gao Y, Ren CX, Shen D. Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:88-102. [PMID: 34383647 PMCID: PMC8905616 DOI: 10.1109/tmi.2021.3104474] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/26/2021] [Accepted: 08/08/2021] [Indexed: 06/13/2023]
Abstract
Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a novel domain adaptation (DA) method with two components to address these problems. The first component is a stochastic class-balanced boosting sampling strategy that overcomes the imbalanced learning problem and improves the classification performance on poorly-predicted classes. The second component is a representation learning that guarantees three properties: 1) domain-transferability by prototype triplet loss, 2) discriminant by conditional maximum mean discrepancy loss, and 3) completeness by multi-view reconstruction loss. Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i.e., class centers) in a hyper-sphere manifold. Experiments on cross-site severity assessment of COVID-19 from CT images show that the proposed method can effectively tackle the imbalanced learning problem and outperform recent DA approaches.
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Affiliation(s)
- Geng-Xin Xu
- School of MathematicsSun Yat-sen UniversityGuangzhou510275China
| | - Chen Liu
- Department of RadiologySouthwest HospitalThird Military Medical University (Army Medical University)Chongqing400038China
| | - Jun Liu
- Department of RadiologyThe Second Xiangya HospitalCentral South UniversityChangsha410011China
- Department of Radiology Quality Control CenterChangshaHunan410011China
| | - Zhongxiang Ding
- Department of RadiologyHangzhou First People’s HospitalZhejiang University School of MedicineHangzhou310027China
| | - Feng Shi
- Department of Research and DevelopmentShanghai United Imaging Intelligence Company Ltd.Shanghai200232China
| | - Man Guo
- Department of RadiologySouthwest HospitalThird Military Medical University (Army Medical University)Chongqing400038China
| | - Wei Zhao
- Department of RadiologyThe Second Xiangya HospitalCentral South UniversityChangsha410011China
| | - Xiaoming Li
- Department of RadiologySouthwest HospitalThird Military Medical University (Army Medical University)Chongqing400038China
| | - Ying Wei
- Department of Research and DevelopmentShanghai United Imaging Intelligence Company Ltd.Shanghai200232China
| | - Yaozong Gao
- Department of Research and DevelopmentShanghai United Imaging Intelligence Company Ltd.Shanghai200232China
| | - Chuan-Xian Ren
- School of MathematicsSun Yat-sen UniversityGuangzhou510275China
- Pazhou LabGuangzhou510330China
- Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University) Ministry of EducationGuangzhou510275China
| | - Dinggang Shen
- Department of Research and DevelopmentShanghai United Imaging Intelligence Company Ltd.Shanghai200232China
- School of Biomedical EngineeringShanghaiTech UniversityShanghai201210China
- Department of Artificial IntelligenceKorea UniversitySeoul02841Republic of Korea
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244
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Patil S, Gondhali G, Acharya A. "Serial ferritin titer" monitoring in COVID-19 pneumonia: valuable inflammatory marker in assessment of severity and predicting early lung fibrosis - prospective, multicentric, observational, and interventional study in tertiary care setting in India. THE EGYPTIAN JOURNAL OF INTERNAL MEDICINE 2022; 34:75. [PMID: 36254195 PMCID: PMC9556145 DOI: 10.1186/s43162-022-00163-3] [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: 03/04/2022] [Accepted: 08/10/2022] [Indexed: 11/07/2022] Open
Abstract
Introduction The COVID-19 pneumonia is a heterogeneous disease with variable effect on lung parenchyma, airways, and vasculature leading to long-term effects on lung functions. Materials and methods Multicentric, prospective, observational, and interventional study conducted during July 2020 to May 2021, in the MIMSR Medical College and Venkatesh Hospital Latur India, included 1000 COVID-19 cases confirmed with RT-PCR. All cases were assessed with lung involvement documented and categorized on HRCT thorax, oxygen saturation, inflammatory marker, ferritin at entry point, and follow-up during hospitalization. Age, gender, comorbidity, and use of BIPAP/NIV and outcome as with or without lung fibrosis as per CT severity were key observations. CT severity scoring is done as per universally accepted standard scoring tool as score < 7 as mild, 7–14 as moderate, and score > 15 as severe affection of the lung. Statistical analysis is done by using chi-square test. Observations and analysis In study of 1000 COVID-19 pneumonia cases, age (< 50 and > 50 years) and gender (male versus female) have significant association with ferritin in predicting severity of COVID-19 pneumonia (p < 0.00001) and (p < 0.010), respectively. CT severity score at entry point with ferritin level has significant correlation in severity scores < 8, 8–15, and > 15 documented in normal and abnormal ferritin level as in 190/110, 90/210, and 40/360, respectively (p < 0.00001). Ferritin level has significant association with duration of illness, i.e., DOI < 7 days, 8–15 days, and > 15 days of onset of symptoms documented normal and abnormal ferritin levels in 30/310, 160/300, and 130/70 cases, respectively (p < 0.00001). Comorbidity as diabetes mellitus, hypertension, COPD, IHD, and obesity has significant association in COVID-19 cases with normal and abnormal ferritin level respectively (p < 0.00001). Ferritin level has significant association with oxygen saturation in COVID-19 pneumonia cases; cases with oxygen saturation > 90%, 75–90%, and < 75% are observed as normal and abnormal ferritin level in 110/100, 150/340, and 60/240 cases, respectively (p < 0.00001). BIPAP/NIV requirement during the course of COVID-19 pneumonia in critical care setting has significant association with ferritin level; cases received BIPAP/NIV during hospitalization were documented normal and abnormal ferritin level in 155/445 and 165/235 cases, respectively (p < 0.00001). Timing of BIPAP/NIV requirement during course of COVID-19 pneumonia in critical care setting has significant association with ferritin level; cases received BIPAP/NIV at entry point < 1 day, 3–7 days, and after 7 days of hospitalization were documented significance in fourfold raised ferritin level in 110/70, 150/160, and 30/80 cases, respectively (p < 0.00001). Follow-up of ferritin titer during hospitalization as compared to entry point abnormal ferritin has significant association in post-COVID lung fibrosis (p < 0.00001). Follow-up of ferritin titer during hospitalization as compared to entry point normal ferritin has significant association in post-COVID lung fibrosis (p < 0.00001). Conclusion Ferritin is easily available, sensitive and reliable, cost-effective, and universally acceptable inflammatory marker in COVID-19 pandemic. Ferritin has very crucial role in COVID-19 pneumonia in predicting severity of illness and assessing response to treatment during hospitalization. Follow-up of ferritin titer during hospitalization and at discharge can be used as early predictor of post-COVID lung fibrosis.
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Affiliation(s)
- Shital Patil
- grid.415674.50000 0004 1766 7426Pulmonary Medicine, MIMSR Medical College, Latur, India
| | - Gajanan Gondhali
- grid.415674.50000 0004 1766 7426Internal Medicine, MIMSR Medical College, Latur, India
| | - Abhijit Acharya
- grid.415674.50000 0004 1766 7426Department of Pathology, MIMSR Medical College, Latur, India
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Haghanifar A, Majdabadi MM, Choi Y, Deivalakshmi S, Ko S. COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:30615-30645. [PMID: 35431611 PMCID: PMC8989406 DOI: 10.1007/s11042-022-12156-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 10/16/2021] [Accepted: 01/03/2022] [Indexed: 05/02/2023]
Abstract
One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.
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Affiliation(s)
- Arman Haghanifar
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK Canada
| | | | - Younhee Choi
- Department of Electrical & Computer EngineeringUniversity of Saskatchewan, Saskatoon, SK Canada
| | | | - Seokbum Ko
- Department of Electrical & Computer EngineeringUniversity of Saskatchewan, Saskatoon, SK Canada
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246
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Pustake M, Tambolkar I, Giri P, Gandhi C. SARS, MERS and CoVID-19: An overview and comparison of clinical, laboratory and radiological features. J Family Med Prim Care 2022; 11:10-17. [PMID: 35309670 PMCID: PMC8930171 DOI: 10.4103/jfmpc.jfmpc_839_21] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/30/2021] [Accepted: 07/04/2021] [Indexed: 11/04/2022] Open
Abstract
In the 21st century, we have seen a total of three outbreaks by members of the coronavirus family. Although the first two outbreaks did not result in a pandemic, the third and the latest outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) culminated in a pandemic. This pandemic has been extremely significant on a social and international level. As these viruses belong to the same family, they are closely related. Despite their numerous similarities, they have slight distinctions that render them distinct from one another. The Severe Acute Respiratory Distress Syndrome and Middle East Respiratory Syndrome (MERS) cases were reported to have a very high case fatality rate of 9.5 and 34.4% respectively. In contrast, the CoVID-19 has a case fatality rate of 2.13%. Also, there are no clear medical countermeasures for these coronaviruses yet. We can cross information gaps, including cultural weapons for fighting and controlling the spread of MERS-CoV and SARS-CoV-2, and plan efficient and comprehensive defensive lines against coronaviruses that might arise or reemerge in the future by gaining a deeper understanding of these coronaviruses and the illnesses caused by them. The review thoroughly summarises the state-of-the-art information and compares the biochemical properties of these deadly coronaviruses with the clinical characteristics, laboratory features and radiological manifestations of illnesses induced by them, with an emphasis on comparing and contrasting their similarities and differences.
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Affiliation(s)
- Manas Pustake
- Department of Internal Medicine, Grant Government Medical College and Sir JJ Group of Hospitals, Mumbai, India
| | - Isha Tambolkar
- Department of Internal Medicine, BJ Government Medical College and Sassoon Hospital, Pune, India
| | - Purushottam Giri
- Department of Community Medicine, IIMSR Medical College, Badnapur, District. Jalna, Maharashtra, India
| | - Charmi Gandhi
- Department of Internal Medicine, Grant Government Medical College and Sir JJ Group of Hospitals, Mumbai, India
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247
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Wu K, Jelfs B, Ma X, Ke R, Tan X, Fang Q. Weakly-supervised lesion analysis with a CNN-based framework for COVID-19. Phys Med Biol 2021; 66:245027. [PMID: 34905733 DOI: 10.1088/1361-6560/ac4316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/14/2021] [Indexed: 02/05/2023]
Abstract
Objective.Lesions of COVID-19 can be clearly visualized using chest CT images, and hence provide valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions.Approach.A deep learning-based diagnosis branch is employed for classification of the CT image and then a lesion identification branch is leveraged to capture multiple types of lesions.Main Results.Our framework is verified on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation.Significance.The proposed approach integrates COVID-19 positive diagnosis and lesion analysis into a unified framework without extra pixel-wise supervision. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.
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Affiliation(s)
- Kaichao Wu
- Department of Biomedical Engineering, Shantou University, Shantou, People's Republic of China
- School of Engineering, RMIT University, Melbourne, Australia
| | - Beth Jelfs
- School of Engineering, RMIT University, Melbourne, Australia
| | - Xiangyuan Ma
- Department of Biomedical Engineering, Shantou University, Shantou, People's Republic of China
| | - Ruitian Ke
- The First Affiliated Hospital of Shantou University Medical College, Shantou, People's Republic of China
| | - Xuerui Tan
- The First Affiliated Hospital of Shantou University Medical College, Shantou, People's Republic of China
| | - Qiang Fang
- Department of Biomedical Engineering, Shantou University, Shantou, People's Republic of China
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Stański M, Gąsiorowski Ł, Wykrętowicz M, Majewska NK, Katulska K. COVID-19 pandemic in flu season. Chest computed tomography - what we know so far. Pol J Radiol 2021; 86:e692-e699. [PMID: 35059062 PMCID: PMC8757012 DOI: 10.5114/pjr.2021.112377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 03/22/2021] [Indexed: 11/17/2022] Open
Abstract
Chest computed tomography (CT) is proven to have high sensitivity in COVID-19 diagnosis. It is available in most emergency wards, and in contrast to polymerase chain reaction (PCR) it can be obtained in several minutes. However, its imaging features change during the course of the disease and overlap with other viral pneumonias, including influenza pneumonia. In this brief analysis we review the recent literature about chest CT features, useful radiological scales, and COVID-19 differentiation with other viral infections.
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Affiliation(s)
- Marcin Stański
- Correspondence address: Marcin Stański, Department of General Radiology and Neuroradiology, Poznan University of Medical Sciences, Poznan, Poland, e-mail:
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249
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Wu J, Tang J, Zhang T, Chen Y, Du C. Follow‐up CT of “reversed halo sign” in SARS‐CoV‐2 delta VOC pneumonia: A report of two cases. J Med Virol 2021; 94:1289-1291. [PMID: 34931334 DOI: 10.1002/jmv.27533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 11/26/2021] [Accepted: 12/18/2021] [Indexed: 11/08/2022]
Affiliation(s)
- Jing Wu
- Department of Radiology Nanjing First Hospital, Nanjing Medical University Nanjing Jiangsu China
| | - Jie Tang
- Department of Radiology The Second Hospital of Nanjing, Nanjing University of Chinese Medicine Nanjing Jiangsu China
| | - Tao Zhang
- Department of Radiology Nanjing First Hospital, Nanjing Medical University Nanjing Jiangsu China
| | - Yu‐Chen Chen
- Department of Radiology Nanjing First Hospital, Nanjing Medical University Nanjing Jiangsu China
| | - Chao Du
- Department of Radiology The Second Hospital of Nanjing, Nanjing University of Chinese Medicine Nanjing Jiangsu China
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Zhao W, He L, Xie XZ, Liao X, Tong DJ, Wu SJ, Liu J. Clustering cases of Chlamydia psittaci pneumonia mimicking COVID-19 pneumonia. World J Clin Cases 2021; 9:11237-11247. [PMID: 35071554 PMCID: PMC8717496 DOI: 10.12998/wjcc.v9.i36.11237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 10/11/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The onset symptoms of people infected by Chlamydia psittaci can mimic the coronavirus disease 2019 (COVID-19). However, the differences in laboratory tests and imaging features between psittacosis and COVID-19 remain unknown.
AIM To better understand the two diseases and then make an early diagnosis and treatment.
METHODS Six patients from two institutions confirmed as psittacosis by high-throughput genetic testing and 31 patients confirmed as COVID-19 were retrospectively included. The epidemiology, clinical characteristics, laboratory tests and computed tomography (CT) imaging features were collected and compared between the two groups. The follow-up CT imaging findings of patients with psittacosis were also investigated.
RESULTS The white blood cell count (WBC), neutrophil count and calcium were more likely to be decreased in patients with COVID-19 but were increased in patients with psittacosis (all P = 0.000). Lymphocyte count and platelet count were higher in patients with psittacosis than in those with COVID-19 (P = 0.044, P = 0.035, respectively). Lesions in patients with psittacosis were more likely to be unilateral (P = 0.001), involve fewer lung lobes (P = 0.006) and have pleural effusions (P = 0.002). Vascular enlargement was more common in patients with COVID-19 (P = 0.003). Consolidation in lung CT images was absorbed in all 6 patients.
CONCLUSION Psittacosis has the potential for human-to-human transmission. Patients with psittacosis present increased WBC count and neutrophil count and have specific CT imaging findings, including unilateral distribution, less involvement of lung lobes and pleural effusions, which might help us to differentiate it from COVID-19.
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Affiliation(s)
- Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan Province, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha 410011, Hunan Province, China
| | - Lei He
- Department of Radiology, The First People’s Hospital of Yueyang, Yueyang 410005, Hunan Province, China
| | - Xing-Zhi Xie
- Department of Radiology, Hunan Chest Hospital, Changsha 410013, Hunan Province, China
| | - Xuan Liao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan Province, China
| | - De-Jun Tong
- Hospital Infection Control Center, The Second Xiangya Hospital, Changsha 410011, Hunan Province, China
| | - Shang-Jie Wu
- Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Changsha 410011, Hunan Province, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan Province, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha 410011, Hunan Province, China
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