The outbreak of viral pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China poses a major threat to public health. SARS-CoV-2 is highly homologous to severe acute respiratory syndrome-associated coronavirus and Middle East respiratory syndrome-associated coronavirus, all of which may cause severe respiratory symptoms. In addition to respiratory symptoms, a considerable proportion of patients with SARS and SARS-CoV-2 infection have varying degrees of liver injury, but their epidemiological features and pathogenesis remains unclear. This article summarizes the epidemiology of SARS-CoV-2 and elaborates on the current status of the research on SARS-CoV-2, possible mechanism of liver injury caused by SARS-CoV-2, and effective treatment regimens, so as to provide a reference and new research ideas for the prevention and treatment of liver injury in patients with SARS-CoV-2 infection.

OBJECTIVETo evaluate the feasibility of establishing a mouse model of ovarian oxidative stress by intraperitoneal injections of arsenic sodium.

METHODSTwenty adult female Kunming mice were randomized equally into the normal control group and ovarian oxidative stress model group for intraperitoneal injections of 0.5 ml distilled water and 8 mg/kg arsenic sodium solution every other day, respectively. After 8 injections, the mice were sacrificed for histological observation of the ovarian sections and enzyme-linked immunosorbent assay (ELISA) of serum estradiol (E(2)) and pregnenedione (P) levels ande contents of reactive oxygen species (ROS) , malondialdehyde (MDA), superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in the ovary homogenate.

RESULTSNumerous atretic follicles were found in the ovaries of mice in the model group with obviously reduced growing follicles. Compared with those in the normal control group, the contents of ROS and MDA increased and SOD and GSH-Px levels in the ovarian homogenate decreased significantly in the model group (P<0.05).

CONCLUSIONA mouse model of ovarian oxidative stress can be established by intraperitoneal injections of arsenic sodium.

Animals ; Arsenites ; Disease Models, Animal ; Female ; Glutathione Peroxidase ; analysis ; Malondialdehyde ; analysis ; Mice ; Mice, Inbred Strains ; Ovary ; metabolism ; physiopathology ; Oxidative Stress ; Reactive Oxygen Species ; analysis ; Superoxide Dismutase ; analysis

Objective:To translate the English version of Sexual Interest and Desire Inventory-Female (SIDI-F) into Chinese, evaluate its reliability, validity and the proper cut-off point of diagnosis of hypoactive sexual desire disorder (HSDD) in China.Methods:Chinese version of SIDI-F was developed and 96 healthy women from January 1, 2019 to December 31, 2019 in Taihe Hospital, Shiyan City, Hubei Province were selected to fill in the Chinese version of SIDI-F and the Female Sexual Function Index (FSFI). Next, analyzed the reliability, validity and the cut-off point of diagnosis of HSDD of the SIDI-F.Results:The Cronbach coefficient of the Chinese version of SIDI-F was 0.931, split-half reliability was 0.922, the intra-group correlation coefficient was 0.805. Analysis of content validity of the SIDI-F indicated that the average of scale-level content validity index was 1.00, the item-level content validity index was 1.00, and the Pearson correlation coefficient between the score of SIDI-F and the erotica score of the FSFI (FSFI-D) was 0.802. Factor analysis of the Chinese version of SIDI-F showed good construct validity. The area under ROC was 0.835. With the SIDI-F score and the best cut-off point of 26.5, Youden index was the largest, at 0.635. The validity indicators were 76.7% for sensitivity, 86.8% for specificity, 5.95 for positive likelihood ratio.Conclusions:The Chinese version of SIDI-F has high reliability and validity in Chinese population, and these show 26.5 point can be used as the best cut-off value of diagnose HSDD.

OBJECTIVE：To prepar e Celastrol oral u lcer film ，and to evalute its quality primarily. METHODS ：The comprehensive scores of the appearance ，film formation and toughness of the drug film were used as indicators ，and the amount of celastrol was controlled to 0.05％. Orthogonal test was used to optimize the amount of excipients as starch ，sodium carboxymethyl cellulose，glycerol and condensed honey ，so as to optimize the formulation ；the validation test was performed. The adhesion force of the film prepared by the optimal formulation were determined. UV spectrophotometer was used to detect the content of celastrol in the film. RESULTS ：The optimal dosage of each excipient in Celastrol oral ulcer film was starch 1.0 g，sodium carboxymethyl cellulose 0.2 g，glycerin 0.4 g，condensed honey 1.5 g. In 3 times of validation tests ，the appearance of the prepared film was good. The average adhesion of the film prepared by the optimal formulation was 4.2 g，and the average content of celastrol was 0.135 3 mg/cm2（RSD＝1.90％，n＝3）. CONCLUSIONS ：In this study ，the best formulation of Celastrol oral ulcer film was optimized，and the film forming ability of the prepared film is good and the quality is stable and uniform.

Non-alcoholic fatty liver disease (NAFLD) incidence is rapidly increasing and become the most common chronic liver disease globally. NAFLD also possesses a risk of developing cardiovascular, kidney, and other diseases. To date, NAFLD still faces difficulties in early diagnosis and treatment options. Thus, early detection, prevention, optimally individualized treatment selections, and prediction of prognosis all are the keys in clinical NAFLD control. Although there are assessment tools available for NAFLD severity appraisal using different clinical parameters, it becomes a hot topic of research in the field for how to optimize non-invasive assessment methodologies. Artificial intelligence (AI) and machine learning are increasingly being used in healthcare, especially in assessment and analysis of chronic liver disease, including NAFLD. This review summarized and discussed the most recent progress of AI and machine learning in differential diagnosis of NAFLD and evaluation of NAFLD severity, in order to provide treatment selections, i.e., the novel AI diagnosis models based on the electronic health records and laboratory tests, ultrasound and radiographic imaging, and liver histopathology data. The therapeutic models discussed the personalized lifestyle changes and NAFLD drug development. The NAFLD prognosis model reviewed and predicted how NAFLD-changed liver metabolisms affect prognosis of patients. This review also speculated future prospective research hot spots and development in the filed for how to utilize the existing AI models to distinguish NAFLD and non-alcoholic steatohepatitis (NASH) and assess NAFLD fibrosis status.