A newly identified coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the infectious coronavirus disease 2019 (COVID-19), emerged in December 2019 in Wuhan, Hubei Province, China, and now poses a major threat to global public health. Previous studies have observed highly variable alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in patients with COVID-19. However, circulating levels of the cholangiocyte injury biomarker gamma-glutamyltransferase (GGT) have yet to be reported in the existing COVID-19 case studies. Herein, we describe the relationship between GGT levels and clinical and biochemical characteristics of patients with COVID-19. Our study is a retrospective case series of 98 consecutive hospitalized patients with confirmed COVID-19 at Wenzhou Central Hospital in Wenzhou, China, from January 17 to February 5, 2020. Clinical data were collected using a standardized case report form. Diagnosis of COVID-19 was assessed by symptomatology, reverse-transcription polymerase chain reaction (RT-PCR), and computed tomography scan. The medical records of patients were analyzed by the research team. Of the 98 patients evaluated, elevated GGT levels were observed in 32.7%; increased C-reactive protein (CRP) and elevated ALT and AST levels were observed in 22.5%, 13.3%, and 20.4%, respectively; and elevated alkaline phosphatase (ALP) and triglycerides (TGs) were found in 2% and 21.4%, respectively. Initially, in the 82 patients without chronic liver disease and alcohol history, age older than 40 years (P = 0.027); male sex (P = 0.0145); elevated CRP (P = 0.0366), ALT (P < 0.0001), and ALP (P = 0.0003); and increased TGs (P = 0.0002) were found to be associated with elevated GGT levels. Elevated GGT (P = 0.0086) and CRP (P = 0.0162) levels had a longer length of hospital stay. Conclusion: A sizable number of patients with COVID-19 infection have elevated serum GGT levels. This elevation supports involvement of the liver in persons with COVID-19. (Hepatology Communications 2020;0:1-7). C oronavirus disease 2019 (COVID-19), an infectious disease characterized by fever and pneumonia, is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Deep-sequencing analysis from lower respiratory tract samples indicated that this pathogen is a novel coronavirus. (1) COVID-19 may progress rapidly to acute respiratory distress syndrome with considerable

Background A striking characteristic of Coronavirus Disease 2019(COVID-19) is the coexistence of clinically mild and severe cases. A comprehensive analysis of multiple risk factors predicting progression to severity is clinically meaningful. Methods The patients were classified into moderate and severe groups. The univariate regression analysis was used to identify their epidemiological and clinical features related to severity, which were used as possible risk factors and were entered into a forward-stepwise multiple logistic regression analysis to develop a multiple factor prediction model for the severe cases.Results 255 patients (mean age, 49.1±SD 14.6) were included, consisting of 184 (72.2%) moderate cases and 71 (27.8%) severe cases. The common symptoms were dry cough (78.0%), sputum (62.7%), and fever (59.2%). The less common symptoms were fatigue (29.4%), diarrhea (25.9%), and dyspnea (20.8%). The univariate regression analysis determined 23 possible risk factors. The multiple logistic regression identified seven risk factors closely related to the severity of COVID-19, including dyspnea, exposure history in Wuhan, CRP (C-reactive protein), aspartate aminotransferase (AST), calcium, lymphocytes, and age. The probability model for predicting the severe COVID-19 was P=1/1+exp (-1.78+1.02×age+1.62×high-transmission-setting-exposure +1.77×dyspnea+1.54×CRP+1.03×lymphocyte+1.03×AST+1.76×calcium). Dyspnea (OR=5.91) and hypocalcemia (OR=5.79) were the leading risk factors, followed by exposure to a high-transmission setting (OR=5.04), CRP (OR=4.67), AST (OR=2.81), decreased lymphocyte count (OR=2.80), and age (OR=2.78). Conclusions This quantitative prognosis prediction model can provide a theoretical basis for the early formulation of individualized diagnosis and treatment programs and prevention of severe diseases.

Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website.Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre -including this research content -immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

Background: The roles of mitochondria and the endoplasmic reticulum (ER) in the progression of hepatocellular carcinoma (HCC) are well established. However, a special domain that regulates the close contact between the ER and mitochondria, known as the mitochondria-associated endoplasmic reticulum membrane (MAM), has not yet been investigated in detail in HCC. Methods: The TCGA-LIHC dataset was only used as a training set. In addition, the ICGC and several GEO datasets were used for validation. Consensus clustering was applied to test the prognostic value of the MAM-associated genes. Then, the MAM score was constructed using the lasso algorithm. In addition, uncertainty of clustering in single-cell RNA-seq data using a gene co-expression network (AUCell) was used for the detection of the MAM scores in various cell types. Then, CellChat analysis was applied for comparing the interaction strength between the different MAM score groups. Further, the tumor microenvironment score (TME score) was calculated to compare the prognostic values, the correlation with the other HCC subtypes, tumor immune infiltration landscape, genomic mutations, and copy number variations (CNV) of different subgroups. Finally, the response to immune therapy and sensitivity to chemotherapy were also determined. Results: First, it was observed that the MAM-associated genes could differentiate the survival rates of HCC. Then, the MAM score was constructed and validated using the TCGA and ICGC datasets, respectively. The AUCell analysis indicated that the MAM score was higher in the malignant cells. In addition, enrichment analysis demonstrated that malignant cells with a high MAM score were positively correlated with energy metabolism pathways. Furthermore, the CellChat analysis indicated that the interaction strength was reinforced between the high-MAM-score malignant cells and T cells. Finally, the TME score was constructed, which demonstrated that the HCC patients with high MAM scores/low TME scores tend to have a worse prognosis and high frequency of genomic mutations, while those with low MAM scores/high TME scores were more likely to have a better response to immune therapy. Conclusions: MAM score is a promising index for determining the need for chemotherapy, which reflects the energy metabolic pathways. A combination of the MAM score and TME score could be a better indicator to predict prognosis and response to immune therapy.