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Diarrhea-predominant irritable bowel syndrome （IBS-D） is a chronic functional bowel disorder characterized by abdominal pain and diarrhea， with visceral hypersensitivity and abnormal gastrointestinal dynamics as the pathophysiological basis. The brain-gut interaction plays a role in pain-related functional gastrointestinal disorders， especially IBS-D. 5-Hydroxytryptamine （5-HT）， as an important brain-gut peptide regulating gastrointestinal function， affects brain activity， gastrointestinal motility， pain perception， mucosal inflammation， and immune response through brain-gut interaction and is associated with the occurrence and development of IBS-D. In recent years， traditional Chinese medicine （TCM） has shown great potential to mitigate gastrointestinal symptoms and improve the quality of life with its holistic view and treatment based on syndrome differentiation. Studies have shown that TCM treats IBS-D by regulating the 5-HT signaling pathway. With a focus on syndrome differentiation in TCM， this paper systematically describes the efficacy and mechanism of TCM in treating different TCM syndromes of IBS-D via the 5-HT signaling pathway， aiming to provide a scientific basis for TCM treatment of this disease.
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Multidrug resistance （MDR） has been a main culprit behind the failure of chemotherapy in patients with malignant tumors and a major obstacle to improving the life quality and prolonging the survival of patients. Hepatocellular carcinoma cells， the innate drug-resistant cells， are generally insensitive to radiotherapy and chemotherapy. Moreover， as the early symptoms of hepatocellular carcinoma are atypical， most patients are diagnosed at the advanced stage， with short survival period and high recurrence rate. Thus， the sensitivity to chemotherapy drugs is decreased. This explains how MDR becomes one of the important reasons for the failure of primary hepatocellular carcinoma （PHC） treatment. Therefore， it is an urgent task to search for safe and effective chemosensitizers with little adverse effect in the research on the drug resistance of hepatocellular carcinoma. As Chinese medicine has been widely applied in the treatment of tumors， the mechanisms of compound Chinese medicine prescriptions， Chinese medicine injections， and single Chinese medicinal in reversing chemotherapy resistance in liver cancer have attracted the interest of scholars. According to previous reports， the mechanisms can be summarized as increasing intracellular drug concentration， influencing changes in enzyme activity， inducing apoptosis， reversing abnormalities in cellular signaling pathways， and regulating the tumor microenvironment. Traditional Chinese medicine reduces the chemotherapy resistance of hepatocellular carcinoma cells through multiple targets and multiple pathways， thereby improving the chemotherapy sensitivity of the cancer cells and enhancing the toxicity of chemotherapeutic drugs to hepatocellular carcinoma cells. Therefore， exploring the mechanism of MDR of hepatocellular carcinoma from the perspective of traditional Chinese medicine is important for reversing the MDR and is of great reference value for clinical treatment of hepatocellular carcinoma. However， there are few experimental types and adverse effects available. Thus， the multi-mechanism and multi-target experiments and clinical research should be carried out in the future.
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Objective@#To explore the value of radiomics in stratifying the Gleason score (GS) of prostate cancer based on vast image features from biparametric MRI.@*Methods@#Three hundred and sixteen patients were enrolled in this study from October, 2015 to December, 2018 and their results of surgical pathology were obtained. The lesions were manually depicted by 3D-Slicer. Then, 106-dimensional features extracted by radiomics were used to conduct Spearman non-parametric correlation test with the high and low risk stratification of GS. The constructed Neural Network was trained with the features after dimension reduction by principal component analysis as the input. Then, the testing set was fed in to get the predictive capability of the model. In the end, 10-fold cross-validation and shuffle of 100 times were used to test the accuracy of the prediction and the generalization ability of the model.@*Results@#Seventy seven-dimensional features with significant correlation were found at the level of P valued=0.05 (two-tailed). After dimensional features were reduced, 21 dimensional new feature spaces with 99% original feature information were obtained. The results on the testing data after the 10-fold validation and shuffle were AUC=0.712 with T2WI, AUC=0.689 with DWI (b=1 000 s/mm2), AUC=0.689 with DWI (b=2 000 s/mm2) and AUC=0.691 with DWI (b=3 000 s/mm2).@*Conclusion@#The neural network after extracting features from biparametric MRI images can accurately and automatically distinguish the high risk and low risk groups of Gleason grade of prostatic cancer.
RESUMO
Objective To explore the value of radiomics in stratifying the Gleason score (GS) of prostate cancer based on vast image features from biparametric MRI. Methods Three hundred and sixteen patients were enrolled in this study from October, 2015 to December, 2018 and their results of surgical pathology were obtained. The lesions were manually depicted by 3D?Slicer. Then, 106?dimensional features extracted by radiomics were used to conduct Spearman non?parametric correlation test with the high and low risk stratification of GS. The constructed Neural Network was trained with the features after dimension reduction by principal component analysis as the input. Then, the testing set was fed in to get the predictive capability of the model. In the end, 10?fold cross?validation and shuffle of 100 times were used to test the accuracy of the prediction and the generalization ability of the model. Results Seventy seven?dimensional features with significant correlation were found at the level of P valued=0.05 (two?tailed). After dimensional features were reduced, 21 dimensional new feature spaces with 99% original feature information were obtained. The results on the testing data after the 10?fold validation and shuffle were AUC=0.712 with T2WI, AUC=0.689 with DWI(b=1 000 s/mm2), AUC=0.689 with DWI (b=2 000 s/mm2) and AUC=0.691 with DWI (b=3 000 s/mm2). Conclusion The neural network after extracting features from biparametric MRI images can accurately and automatically distinguish the high risk and low risk groups of Gleason grade of prostatic cancer.