The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for preci...The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.展开更多
Background: Diabetes is a known risk factor for susceptibility and severity of COVID-19 infection. It may also be a complication of COVID-19. Many hypotheses have been proposed to explain this condition. It may be due...Background: Diabetes is a known risk factor for susceptibility and severity of COVID-19 infection. It may also be a complication of COVID-19. Many hypotheses have been proposed to explain this condition. It may be due to the effect of SARS-CoV-2 on β cells or drug-related side effects. In children, there is a paucity of data on the burden of this complication. Objective: We aimed to report a case of secondary diabetes during COVID-19 in a pediatric unit. Case Presentation: A 16-year-old girl presented with severe respiratory distress. She was treated for COVID-19 infection with antibiotics and corticosteroids. On day 6 of treatment, she developed polyuria and polydipsia. A random blood glucose test showed hyperglycaemia. The diagnosis of secondary diabetes was maintained. Conclusion: Covid-19 infection can be complicated by diabetes in children. It is essential to monitor blood glucose levels regularly.展开更多
目的:分析新型冠状病毒感染(COVID-19)相关心律失常的文献,探索该领域的研究现状、热点并预测未来的趋势,为后来的研究者提供借鉴。方法:选择Web of Science的核心合集数据库,每项研究都进行了文献计量和视觉分析,使用CiteSpace和VOSvie...目的:分析新型冠状病毒感染(COVID-19)相关心律失常的文献,探索该领域的研究现状、热点并预测未来的趋势,为后来的研究者提供借鉴。方法:选择Web of Science的核心合集数据库,每项研究都进行了文献计量和视觉分析,使用CiteSpace和VOSviewer软件生成知识图谱。结果:共鉴定出768篇文章,发文涉及美国、意大利和中国为首的319个国家/地区和4 366个机构,领先的研究机构是梅奥诊所和哈佛医学院。New England Journal of Medicine是该领域最常被引用的期刊。在6 687位作者中,Arbelo Elena撰写的研究最多,Guo T被共同引用的次数最多,心房纤颤是最常见的关键词。结论:随着COVID-19的暴发,对COVID-19所致新发/进行性心律失常事件的研究蓬勃发展,未来的研究者可能会对COVID-19感染后新发或遗留的快速性心律失常/缓慢性心律失常的发生机制进行进一步的探索。展开更多
Background:Solid organ transplant(SOT)activities,such as liver transplant,have been greatly influenced by the pandemic of coronavirus disease 2019(COVID-19),a disease caused by severe acute respiratory syndrome corona...Background:Solid organ transplant(SOT)activities,such as liver transplant,have been greatly influenced by the pandemic of coronavirus disease 2019(COVID-19),a disease caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Immunosuppressed individuals of liver transplant recipients(LTRs)tend to have a high risk of COVID-19 infection and related complications.Therefore,COVID-19 vaccination has been recommended to be administered as early as possible in LTRs.Data sources:The keywords“liver transplant”,“SARS-CoV-2”,and“vaccine”were used to retrieve articles published in PubMed.Results:The antibody response following the 1st and 2nd doses of vaccination was disappointingly low,and the immune responses among LTRs remarkably improved after the 3rd or 4th dose of vaccination.Although the 3rd or 4th dose of COVID-19 vaccine increased the antibody titer,a proportion of patients remained unresponsive.Furthermore,recent studies showed that SARS-CoV-2 vaccine could trigger adverse events in LTRs,including allograft rejection and liver injury.Conclusions:This review provides the recently reported data on the antibody response of LTRs following various doses of vaccine,risk factors for poor serological response and adverse events after vaccination.展开更多
基金funded by King Saud University,Riyadh,Saudi Arabia.Researchers Supporting Project Number(RSP2024R167),King Saud University,Riyadh,Saudi Arabia.
文摘The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.
文摘Background: Diabetes is a known risk factor for susceptibility and severity of COVID-19 infection. It may also be a complication of COVID-19. Many hypotheses have been proposed to explain this condition. It may be due to the effect of SARS-CoV-2 on β cells or drug-related side effects. In children, there is a paucity of data on the burden of this complication. Objective: We aimed to report a case of secondary diabetes during COVID-19 in a pediatric unit. Case Presentation: A 16-year-old girl presented with severe respiratory distress. She was treated for COVID-19 infection with antibiotics and corticosteroids. On day 6 of treatment, she developed polyuria and polydipsia. A random blood glucose test showed hyperglycaemia. The diagnosis of secondary diabetes was maintained. Conclusion: Covid-19 infection can be complicated by diabetes in children. It is essential to monitor blood glucose levels regularly.
文摘目的:分析新型冠状病毒感染(COVID-19)相关心律失常的文献,探索该领域的研究现状、热点并预测未来的趋势,为后来的研究者提供借鉴。方法:选择Web of Science的核心合集数据库,每项研究都进行了文献计量和视觉分析,使用CiteSpace和VOSviewer软件生成知识图谱。结果:共鉴定出768篇文章,发文涉及美国、意大利和中国为首的319个国家/地区和4 366个机构,领先的研究机构是梅奥诊所和哈佛医学院。New England Journal of Medicine是该领域最常被引用的期刊。在6 687位作者中,Arbelo Elena撰写的研究最多,Guo T被共同引用的次数最多,心房纤颤是最常见的关键词。结论:随着COVID-19的暴发,对COVID-19所致新发/进行性心律失常事件的研究蓬勃发展,未来的研究者可能会对COVID-19感染后新发或遗留的快速性心律失常/缓慢性心律失常的发生机制进行进一步的探索。
基金the National Natural Science Foundation of China(82103662).
文摘Background:Solid organ transplant(SOT)activities,such as liver transplant,have been greatly influenced by the pandemic of coronavirus disease 2019(COVID-19),a disease caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Immunosuppressed individuals of liver transplant recipients(LTRs)tend to have a high risk of COVID-19 infection and related complications.Therefore,COVID-19 vaccination has been recommended to be administered as early as possible in LTRs.Data sources:The keywords“liver transplant”,“SARS-CoV-2”,and“vaccine”were used to retrieve articles published in PubMed.Results:The antibody response following the 1st and 2nd doses of vaccination was disappointingly low,and the immune responses among LTRs remarkably improved after the 3rd or 4th dose of vaccination.Although the 3rd or 4th dose of COVID-19 vaccine increased the antibody titer,a proportion of patients remained unresponsive.Furthermore,recent studies showed that SARS-CoV-2 vaccine could trigger adverse events in LTRs,including allograft rejection and liver injury.Conclusions:This review provides the recently reported data on the antibody response of LTRs following various doses of vaccine,risk factors for poor serological response and adverse events after vaccination.