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1.
Nat Metab ; 4(3): 310-319, 2022 03.
Article in English | MEDLINE | ID: covidwho-1764213

ABSTRACT

Extrapulmonary manifestations of COVID-19 have gained attention due to their links to clinical outcomes and their potential long-term sequelae1. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) displays tropism towards several organs, including the heart and kidney. Whether it also directly affects the liver has been debated2,3. Here we provide clinical, histopathological, molecular and bioinformatic evidence for the hepatic tropism of SARS-CoV-2. We find that liver injury, indicated by a high frequency of abnormal liver function tests, is a common clinical feature of COVID-19 in two independent cohorts of patients with COVID-19 requiring hospitalization. Using autopsy samples obtained from a third patient cohort, we provide multiple levels of evidence for SARS-CoV-2 liver tropism, including viral RNA detection in 69% of autopsy liver specimens, and successful isolation of infectious SARS-CoV-2 from liver tissue postmortem. Furthermore, we identify transcription-, proteomic- and transcription factor-based activity profiles in hepatic autopsy samples, revealing similarities to the signatures associated with multiple other viral infections of the human liver. Together, we provide a comprehensive multimodal analysis of SARS-CoV-2 liver tropism, which increases our understanding of the molecular consequences of severe COVID-19 and could be useful for the identification of organ-specific pharmacological targets.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Liver , Proteomics , Tropism
3.
J Formos Med Assoc ; 121(2): 454-466, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1330958

ABSTRACT

This review evaluates the ability of the fibrosis index based on four factors (FIB-4) identifying fibrosis stages, long-time prognosis in chronic liver disease, and short-time outcomes in acute liver injury. FIB-4 was accurate in predicting the absence or presence of advanced fibrosis with cut-offs of 1.0 and 2.65 for viral hepatitis B, 1.45 and 3.25 for viral hepatitis C, 1.30 (<65 years), 2.0 (≥65 years), and 2.67 for non-alcoholic fatty liver disease (NAFLD), respectively, but had a low-to-moderate accuracy in alcoholic liver disease (ALD) and autoimmune hepatitis. It performed better in excluding fibrosis, so we built an algorithm for identifying advanced fibrosis by combined methods and giving work-up and follow-up suggestions. High FIB-4 in viral hepatitis, NAFLD, and ALD was associated with significantly high hepatocellular carcinoma incidence and mortality. Additionally, FIB-4 showed the ability to predict high-risk varices with cut-offs of 2.87 and 3.91 in cirrhosis patients and predict long-term survival in hepatocellular carcinoma patients after hepatectomy. In acute liver injury caused by COVID-19, FIB-4 had a predictive value for mechanical ventilation and 30-day mortality. Finally, FIB-4 may act as a screening tool in the secondary prevention of NAFLD in the high-risk population.


Subject(s)
COVID-19 , Liver Neoplasms , Non-alcoholic Fatty Liver Disease , Fibrosis , Humans , Liver/pathology , Liver Cirrhosis/pathology , Liver Neoplasms/pathology , Non-alcoholic Fatty Liver Disease/complications , Non-alcoholic Fatty Liver Disease/diagnosis , Non-alcoholic Fatty Liver Disease/pathology , SARS-CoV-2 , Severity of Illness Index
4.
J Raman Spectrosc ; 52(5): 949-958, 2021 May.
Article in English | MEDLINE | ID: covidwho-1095641

ABSTRACT

The outbreak of COVID-19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID-19 detection is the real-time polymerase chain reaction (RT-PCR)-based technique; however, it also has certain limitations, such as sample-dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID-19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID-19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support-vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID-19 patients and 5 symptomatic COVID-19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups-confirmed COVID-19, suspected, and healthy individuals-the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID-19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85-0.88), and the accuracy between the COVID-19 and the healthy controls is 0.90 (95% CI: 0.89-0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67-0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum-level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID-19 screening.

5.
Journal of Thoracic Oncology ; 2020.
Article | WHO COVID | ID: covidwho-276786

ABSTRACT

The global COVID-19 pandemic continues to escalate at a rapid pace inundating medical facilities and creating significant challenges globally. The risk of SARS-CoV-2 infection in cancer patients appears to be higher especially as they are more likely to present with an immunocompromised condition, either from the cancer itself or from the treatments they receive. A major consideration in the delivery of cancer care during the pandemic is to balance the risk of patient exposure and infection with the need to provide effective cancer treatment. Many aspects of the SARS-CoV-2 infection remain poorly characterized currently and even less is known about the course of infection in the context of a patient with cancer. As SARS-CoV-2 is highly contagious, the risk of infection directly affects the cancer patient being treated, other cancer patients in close proximity, and health care providers. Infection at any level for patients or providers can cause significant disruption to even the most effective treatment plans. Lung cancer patients, especially those with reduced lung function and cardiopulmonary co-morbidities are more likely to have increased risk and mortality from COVID-19 as one of its common manifestation is as an acute respiratory illness. The purpose of this manuscript is to present a practical multidisciplinary and international overview to assist in treatment for lung cancer patients during this pandemic, with the caveat that evidence is lacking in many areas. It is expected that firmer recommendations can be developed as more evidence becomes available.

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