Objective:To investigate the expression level of C-type lectin domain family 4 member G ( CLEC4 G) in liver disease tissues and its correlation with the clinicopathological characteristics of hepatocellular carcinoma (HCC) patients. Methods:The cancer tissue and the corresponding adjacent tissues (at least 2 cm from the edge of the cancer tissue), cut in surgeries from January to December in 2019, of 40 HCC patients in Zhuzhou Central Hospital, as well as 10 normal liver tissue samples (seen as far away as possible from the edge of the cancer tissue with naked eyes) and 10 liver cirrhosis samples were analyzed retrospectively. The tumor genome atlas (TCGA) database was used to screen the HCC transcriptome data sets, and bioinformatics methods were used to make expression heat maps and box maps which can help analyze the difference of CLEC4 G in cancer and adjacent tissues. The mRNA expression level of CLEC4 G was detected by conducting real-time fluorescence quantitative PCR (qRT-PCR), and the protein expression level of CLEC4G was detected by immunohistochemistry (IHC). The measurement data were expressed as mean±standard deviation ( Mean± SD). Group t test was used for inter-group comparison. The counting information was expressed as a percentage (%). The χ2 test was adopted to analyze the correlation between CLEC4 G expression level and the clinicopathological features of patients. Results:The expression level of CLEC4 G in cancer tissues was significantly decreased in heat map compared with that in adjacent tissues. In the box figure, the relative expression of CLEC4 G mRNA in the cancer tissues was (82.5±18.9) and (3 354.4±296.2) in paracancer tissues, with statistically significant difference ( P<0.001). Respectively, qRT-PCR and IHC showed that mRNA of CLEC4 G were abundant in normal liver tissues (3 301.3±286.4), while they were very little in liver cancer tissues (63.6±32.9), significantly decreasing in liver cirrhosis (1 742.6±208.7) and paracancer tissues (1 553.2±249.9), with statistically significant difference ( P<0.001). Moreover, low CLEC4 G expression level was associated with tumor vascular metastasis in HCC patients. Conclusions:CLEC4 G is highly expressed in normal liver tissue, but with the progression of malignant liver disease, it is significantly decreased with little expression in HCC tissue. It can be expected to be a good marker for the pathological diagnosis of HCC.

Objective:To establish a method for determintation of chlorogenic acid and linarin in Yejuhua granules by HPLC.Methods:We applied HPLC methods. The Kromasil 100-5 C18 column (250 mm×4.6 mm,5 μm) was used, the mobile phase was acetonitrile-0.4%H 3PO 4 solution (gradient elution), the flow rate was 1.0 ml/min, the dection wavelenghth was 334 nm and the column temperture was 32 ℃. Results:Chlorogenic acid and buddleoside had good linearity in the ranges of 0.30-1.50 μg ( r2=0.999 1) and 0.12-0.62 μg ( r2=0.999 8), respectively. The average recoveries were 99.70% and 96.67%, with RSD<2%, respectively. Conclusion:The method is simple, rapid, reliable, efficient, and can be used for determination of chlorogenic acid and buddleoside in Yejuhua Granules.

Objective:To assess the influence of an artificial intelligence (AI) -assisted diagnosis system on the performance of endoscopists in diagnosing gastric cancer by magnifying narrow banding imaging (M-NBI).Methods:M-NBI images of early gastric cancer (EGC) and non-gastric cancer from Renmin Hospital of Wuhan University from March 2017 to January 2020 and public datasets were collected, among which 4 667 images (1 950 images of EGC and 2 717 of non-gastric cancer)were included in the training set and 1 539 images (483 images of EGC and 1 056 of non-gastric cancer) composed a test set. The model was trained using deep learning technique. One hundred M-NBI videos from Beijing Cancer Hospital and Renmin Hospital of Wuhan University between 9 June 2020 and 17 November 2020 were prospectively collected as a video test set, 38 of gastric cancer and 62 of non-gastric cancer. Four endoscopists from four other hospitals participated in the study, diagnosing the video test twice, with and without AI. The influence of the system on endoscopists′ performance was assessed.Results:Without AI assistance, accuracy, sensitivity, and specificity of endoscopists′ diagnosis of gastric cancer were 81.00%±4.30%, 71.05%±9.67%, and 87.10%±10.88%, respectively. With AI assistance, accuracy, sensitivity and specificity of diagnosis were 86.50%±2.06%, 84.87%±11.07%, and 87.50%±4.47%, respectively. Diagnostic accuracy ( P=0.302) and sensitivity ( P=0.180) of endoscopists with AI assistance were improved compared with those without. Accuracy, sensitivity and specificity of AI in identifying gastric cancer in the video test set were 88.00% (88/100), 97.37% (37/38), and 82.26% (51/62), respectively. Sensitivity of AI was higher than that of the average of endoscopists ( P=0.002). Conclusion:AI-assisted diagnosis system is an effective tool to assist diagnosis of gastric cancer in M-NBI, which can improve the diagnostic ability of endoscopists. It can also remind endoscopists of high-risk areas in real time to reduce the probability of missed diagnosis.