Objective To explore the value of biparametric magnetic resonance imaging(bp-MRI)radiomics models in noninvasive prediction of high-risk prostate cancer.Methods A total of 320 patients with pathologically confirmed prostate cancer were retro-spectively selected,and all patients underwent bp-MRI before pathology,including T2WI and diffusion weighted imaging(DWI).Appar-ent diffusion coefficient(ADC)maps were extracted from DWI.All patients were divided into high-risk(Gleason score≥8)and medium-low risk(Gleason score ≤7)groups based on the Gleason score.Using 3D Slicer software,the entire prostate gland was outlined.Python software was used to calculate parameters,and the minimum redundancy maximum correlation and sequence back-ward elimination algorithms were used to extract and select radiomics features and to build a model.Three radiomics(T2 WI,DWI,ADC)models were constructed and verified by logistic regression(LR).The performance of the model was evaluated by area under the curve(AUC)of receiver operating characteristic(ROC)curve,specificity(SP),sensitivity(SE),and accuracy(ACC).An indi-vidual prediction model was established via the clinical data of 224 patients and bp-MRI features,and validated via the data of 96 patients.Results A total of 1 165 radiomics features were extracted.After feature screening,2,4 and 6 radiomics features were screened out to construct T2WI model,DWI model and ADC model for predicting high-risk prostate cancer.All radiomics models had significant predictive performance in identifying medium-low risk and high-risk groups(P＜0.05).The DWI model had the highest predictive value,and the AUC,ACC,SE,and SP in the training group were 0.814,0.756,0.838,and 0.744,respectively.The AUC,ACC,SE,and SP in the verification group were 0.840,0.756,0.848,and 0.784,respectively.Conclusion Radiomics based on bp-MRI can better identify medium-low risk and high-risk prostate cancer before surgery.

OBJECTIVE: To explore the pharmacologically active ingredients in granules (TJQW) for treatment of coronavirus disease 2019 (COVID-19) in light of systemic pharmacology. METHODS: We performed database search, literature mining and drug-like index screening to identify the bioactive components in TJQW, the positive drugs for disease treatment and their therapeutic targets. The core disease target was investigated based on the cross-linking interaction of the bioactive components, positive drug and potential disease target, and the target proteins at the key nodes were analyzed by GO and KEGG analyses. Based on the therapeutic targets for COVID-19, virtual screening was conducted to screen the compounds in TJQW and construct the network cross-linking the key bioactive molecules in TJQW, key node targets of the disease, and the related biological pathways. RESULTS: We identified 159 compounds in TJQW and obtained 18 core proteins based on the cross-linking of the bioactive components, positive drugs and disease targets. The key node targets consisted of 22 targets including the latest 4 COVID-19 proteins. Virtual screening results showed that at least 14 compounds could bind with the core disease target proteins. The material basis of TJQW for COVID-19 treatment was explained in multi-pathway, multi-component and multi-target perspectives. In terms of the structural characteristics of the compounds, we screened the top 30 molecules with strong binding with the target proteins, among which flavonoids were the predominant components. CONCLUSIONS This investigation reveals the therapeutic mechanism of TJQW for COVID-19 involving multiple components, targets and pathways from the perspective of key bioactive molecules, disease key node targets and related biological pathways. We screened 30 active precursors from TJQW, which provides reference for the clinical application and further development of TJQW.
Betacoronavirus ; Coronavirus Infections ; drug therapy ; Drugs, Chinese Herbal ; Humans ; Medicine, Chinese Traditional ; Pandemics ; Pneumonia, Viral ; drug therapy

Objective To explore the effective constituents from Sonchus arvensis L. and the potential mechanism in treating sepsis by network pharmacology. Methods The chemical ingredients reported in the literature were taken as research objects and Swiss Target Prediction database was used to collect the identify the potential targets of those ingredients. The GeneCards, OMIM and TTD databases were applied to screen the sepsis related molecular targets. The cross targets were obtained and used to construct the active ingredient-disease target network. In addition, the targets were also imported into STRING database to construct a PPI network. Finally, GO and KEGG enrichment analysis were performed on the target genes to predict the mechanism via DAVID database. Results 71 components from S. arvensis L. were obtained, which corresponded to 579 potential drug targets. There were 3437 related targets of sepsis. S. arvensis L. and sepsis shared 272 common targets. The results showed that 1366 terms were found by GO function enrichment, including 245 molecular functions (MF), 1002 biological processes (BP), and 119 cell composition (CC), The KEGG enrichment analysis suggested that 166 signaling pathways were involved. Conclusion The study revealed that TNF, AKT1, IL-6, IL-1β, TP53 and some other targets might be affected by potentially active ingredients of S arvensis L. such as linoleic acid, linolenic acid and oleic acid to regulate the expression of steroids, sphingolipids hormones as well as epidermal factors and chemokines in treating sepsis.