Objective To investigate the expression level and potential clinical value of serum HBV RNA in HBeAg-positive chronic hepatitis B (CHB) patients at different periods. Methods A total of 61 CHB patients who attended the outpatient and inpatient services of Department of Hepatology, Hangzhou Xixi Hospital, from August 2019 to December 2020 were enrolled, and according to the antiviral therapy for HBeAg-positive CHB patients, they can be divided into group A with untreated HBeAg-positive CHB (HBeAg+ and HBV DNA+) patients, group B with treatment-experienced patients before HBeAg seroconversion (HBeAg+ and HBV DNA-), and group C with treatment-experienced patients after HBeAg seroconversion (HBeAg- and HBV DNA-). Peripheral blood HBV RNA load was measured at different periods, and its correlation with HBsAg and HBV DNA was analyzed. The t -test was used for comparison of normally distributed continuous data between groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups; the chi-square test was used for comparison of categorical data between groups; a Pearson or Spearman correlation analysis was used to describe the correlation between two variables. Results The positive rates of HBV RNA in these three groups were 100% (22/22), 88.2% (15/17), and 22.7% (6/22), respectively. In group A, HBV RNA was positively correlated with HBsAg and HBV DNA ( r =0.612 and 0.922, both P < 0.01), while in groups B and C, there was no correlation between HBV RNA and HBsAg. Group B had significantly higher levels of HBV RNA and HBsAg than group C ( Z =-4.44 and -2.41, both P < 0.05). The HBV DNA-positive group had a significantly higher level of HBV RNA than the HBV DNA-negative group ( Z =-6.16, P < 0.01). Conclusion After HBV DNA clearance achieved by antiviral therapy with nucleos(t)ide analogues in CHB patients, serum HBV RNA can still be detected in some of these patients. Since HBV RNA only comes from cccDNA in the liver, it can better reflect viral replication activity in the liver than HBV DNA and thus has a certain clinical value in the management of CHB patients.

Objective To characterize the metabolic "fingerprint" of fecal extracts for diagnosis of early-stage colorectal cancer (CRC) using proton nuclear magnetic resonance spectroscopy (1H-NMR)-based metabolomics coupled with pattern recognition.Methods From January 2014 to December 2014,we collected fecal samples at the Second Affiliated Hospital of Shantou University Medical College,from 25 patients with colorectal adenomas (CR-Ad),20 with stage Ⅰ /Ⅱ CRC,and 32 healthy controls (HCs).The patients were diagnosed by histopathology.No subjects had any complicating diseases.HCs showed no abnormalities from blood tests,endoscopic examination,diagnostic imaging,and/or medical interviews.We excluded participants who used antibiotics,NSAIDS,statins,or probiotics within two months of study participation,and any patients who underwent chemotherapy or radiation treatments prior to surgery.We used orthogonal partial least-squares-discriminant analysis (OPLS-DA) for pattern recognition (dimension reduction) on 1H-NMR processed data (1H frequency of 400.13 MHz),to find metabolic differences among CR-Ad,carcinoma and HC fecal samples;and receiver operating characteristic (ROC) analysis to determine the diagnostic value of the fecal metabolic biomarkers.Results Fecal samples were collected from 20 patients with Stage Ⅰ/Ⅱ CRC (11 M,9 F,median age (52±13) years),25 with CR-Ad (14 M,11 F,median age (53±11) years) and 32 HCs (15 M,17 F,median age (53± 14) years).OPLS-DA clearly distinguished CR-Ad and stage Ⅰ/Ⅱ CRC from HC samples,based on their metabolomic profiles.Relative signal intensities in HCs were significantly lower than in the cancer patients for butyrate (HC:23.0±6.0;CR-Ad:18.0±5.0;CRC:14.0±6.0;Z=-2.07,P=0.008),acetate (HC:45.0± 11.0;CR-Ad:31.0±11.0;CRC:24.0±8.0;Z=-2.32,P=0.011),propionate (HC:26.0 ± 7.0;CR-Ad:22.0 ± 6.0;CRC:19.0 ± 5.0;Z=-2.43,P=0.032),glucose (HC:37.0±7.0;CR-Ad:31.0±7.0;CRC:26.0±8.0;Z=-2.07,P=0.044) and glutamine (HC:4.5±2.0;CR-Ad:4.9 ± 1.0;CRC:5.4 ± 1.0;Z=2.21,P=0.044).However,relative signal intensities in HCs were significantly higher than in patients for lactate (HC:4.8± 1.0;CR-Ad:6.9±2.0;CRC:4.8± 1.0;Z=2.02,P=0.038),glutamate (HC:3.2±2.0;CR-Ad:4.9 ± 1.0;CRC:3.2± 2.0;Z=2.21,P=0.044) and succinate (HC:12.0±2.0;CR-Ad:15.0±3.0;CRC:12.0± 2.0;Z=2.25,P=0.011).Among the potential biomarkers,acetate at 1.92 ppm,and succinate at 2.41 ppm displayed relatively high area under ROC,with sensitivity and specificity both ＞90％,to distinguish early-stage CRC patients from HCs.Conclusion Fecal metabolic profiles distinguish of HCs from patients with CRC patients,even in the early stages (stage Ⅰ/Ⅱ),highlighting the potential of NMR-based fecal metabolomic fingerprinting as tools for early CRC diagnosis.

Objective To characterize the metabolic "fingerprint" of fecal extracts for diagnosis of early-stage colorectal cancer (CRC) using proton nuclear magnetic resonance spectroscopy (1H-NMR)-based metabolomics coupled with pattern recognition.Methods From January 2014 to December 2014,we collected fecal samples at the Second Affiliated Hospital of Shantou University Medical College,from 25 patients with colorectal adenomas (CR-Ad),20 with stage Ⅰ /Ⅱ CRC,and 32 healthy controls (HCs).The patients were diagnosed by histopathology.No subjects had any complicating diseases.HCs showed no abnormalities from blood tests,endoscopic examination,diagnostic imaging,and/or medical interviews.We excluded participants who used antibiotics,NSAIDS,statins,or probiotics within two months of study participation,and any patients who underwent chemotherapy or radiation treatments prior to surgery.We used orthogonal partial least-squares-discriminant analysis (OPLS-DA) for pattern recognition (dimension reduction) on 1H-NMR processed data (1H frequency of 400.13 MHz),to find metabolic differences among CR-Ad,carcinoma and HC fecal samples;and receiver operating characteristic (ROC) analysis to determine the diagnostic value of the fecal metabolic biomarkers.Results Fecal samples were collected from 20 patients with Stage Ⅰ/Ⅱ CRC (11 M,9 F,median age (52±13) years),25 with CR-Ad (14 M,11 F,median age (53±11) years) and 32 HCs (15 M,17 F,median age (53± 14) years).OPLS-DA clearly distinguished CR-Ad and stage Ⅰ/Ⅱ CRC from HC samples,based on their metabolomic profiles.Relative signal intensities in HCs were significantly lower than in the cancer patients for butyrate (HC:23.0±6.0;CR-Ad:18.0±5.0;CRC:14.0±6.0;Z=-2.07,P=0.008),acetate (HC:45.0± 11.0;CR-Ad:31.0±11.0;CRC:24.0±8.0;Z=-2.32,P=0.011),propionate (HC:26.0 ± 7.0;CR-Ad:22.0 ± 6.0;CRC:19.0 ± 5.0;Z=-2.43,P=0.032),glucose (HC:37.0±7.0;CR-Ad:31.0±7.0;CRC:26.0±8.0;Z=-2.07,P=0.044) and glutamine (HC:4.5±2.0;CR-Ad:4.9 ± 1.0;CRC:5.4 ± 1.0;Z=2.21,P=0.044).However,relative signal intensities in HCs were significantly higher than in patients for lactate (HC:4.8± 1.0;CR-Ad:6.9±2.0;CRC:4.8± 1.0;Z=2.02,P=0.038),glutamate (HC:3.2±2.0;CR-Ad:4.9 ± 1.0;CRC:3.2± 2.0;Z=2.21,P=0.044) and succinate (HC:12.0±2.0;CR-Ad:15.0±3.0;CRC:12.0± 2.0;Z=2.25,P=0.011).Among the potential biomarkers,acetate at 1.92 ppm,and succinate at 2.41 ppm displayed relatively high area under ROC,with sensitivity and specificity both ＞90％,to distinguish early-stage CRC patients from HCs.Conclusion Fecal metabolic profiles distinguish of HCs from patients with CRC patients,even in the early stages (stage Ⅰ/Ⅱ),highlighting the potential of NMR-based fecal metabolomic fingerprinting as tools for early CRC diagnosis.

Objective To establish a differential model of phlegm and blood stasis pattern and qi deficiency and blood stasis pattern in stable angina pectoris using radiomics.Methods A total of 91 patients with stable angina pectoris who underwent coronary artery CT angiography in Affiliated Hospital of Liaoning University of Traditional Chinese Medicine from January 2021 to January 2022 were collected,including 47 cases of phlegm and blood stasis pattern and 44 cases of qi deficiency and blood stasis pattern.The patients were divided into train set(64 cases)and test set(27 cases)according to the ratio of 7∶3 by stratified random sampling method.3D-slicer software was used to extract the radiomics features of pericoronary adipose tissue(PCAT)images.Principal component analysis was used to visualize the distribution of radiomics features of pattern of phlegm and blood stasis and pattern of qi deficiency and blood stasis.The least absolute shrinkage and selection operator regression analysis and support vector machine decreasing feature elimination were used for feature selection.The multinomial logistics regression was used for model construction.The receiver operating characteristic(ROC)curve was used to verify the model in the train set and the test set to evaluate the effectiveness of the radiomics features in differentiating phlegm and blood stasis pattern and qi deficiency and blood stasis pattern.Finally,Spearman coefficient was used to analyze the correlation between the differential features and clinical physicochemical data.Results A total of 837 radiomics features were extracted from PCAT images by 3D-slicer software.In the principal component analysis,PC1 and PC2 explained 77.9%and 8.1%of the total variance,respectively,and there was a relatively obvious separation trend between the two pattern groups.After feature screening,7 radiomics features were used to construct the differential model of phlegm and blood stasis pattern and qi deficiency and blood stasis pattern.The area under the ROC curve(AUC)of the differential model was 0.844 in the train set and 0.834 in the test set.Spearman correlation analysis showed that the differential features were significantly correlated with cTnI,neutrophil,triglyceride,total cholesterol,and leukocyte.Conclusion The CT radiomics model based on PCAT has a high discrimination efficiency for stable angina pectoris with phlegm and blood stasis pattern and qi deficiency and blood stasis pattern.