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Objective:To explore the mechanism of Xijiao Dihuang Ddecoction (XJDHT) against sepsis-induced liver injury based on transcriptomics.Methods:Sixty C57BL/6 mice were randomly (random number) divided into the sepsis group, sepsis treatment with XJDHT and control group, with 20 mice in each group. The sepsis mouse model was established by intraperitoneal (i.p.) injection of lipopolysaccharide (LPS). The control group was intraperitoneally injected with the same amount of normal saline. The sepsis treatment with XJDHT group was injected with XJDHT (crude drug 187.5 mg) twice a day 2 days before modeling. After modeling, gastric feeding was continued twice a day, while the control group and sepsis group were gavaged with the same amount of normal saline. At 72 h after LPS intervention, 9 mice in each group were randomly selected. After anesthesia, part of the liver were taken for small RNA and RNA sequencing and analysis, and part of the liver were taken for pathological examination.Results:XJDHT could improve the histopathological changes of liver in septic mice, and alleviate some abnormally expressed microRNAs (mmu-mir-292a-5p, mmu-mir-871-3p, mmu-mir-653-5p, mmu-mir-293-5p, mmu-mir-155-3p, mmu-mir-346-5p, mmu-mir-187-5p, mmu-mir-3090-3p) and their target genes.Conclusions:XJDHT can reduce the liver histopathological changes in septic mice, and its mechanism may be related to XJDHT regulating the expression of important key genes of liver of sepsis like mmu-mir-187-5p and its target genes such as ADAM8, irak3 and PFKFB3
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BackgroundComputed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. Our research aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT, relieve working pressure of radiologists and contribute to the control of the epidemic. MethodsFor model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University (Wuhan, Hubei province, China) were retrospectively collected and processed. Twenty-seven consecutive patients undergoing CT scans in Feb, 5, 2020 in Renmin Hospital of Wuhan University were prospectively collected to evaluate and compare the efficiency of radiologists against 2019-CoV pneumonia with that of the model. FindingsThe model achieved a per-patient sensitivity of 100%, specificity of 93.55%, accuracy of 95.24%, PPV of 84.62%, and NPV of 100%; a per-image sensitivity of 94.34%, specificity of 99.16%, accuracy of 98.85%, PPV of 88.37%, and NPV of 99.61% in retrospective dataset. For 27 prospective patients, the model achieved a comparable performance to that of expert radiologist. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. ConclusionThe deep learning model showed a comparable performance with expert radiologist, and greatly improve the efficiency of radiologists in clinical practice. It holds great potential to relieve the pressure of frontline radiologists, improve early diagnosis, isolation and treatment, and thus contribute to the control of the epidemic.