The preclinical safety assessment of hepatotoxicity drugs has a low sensitivity and low specificity. Related tests often generate false negative results and unexpected toxicity,which is one of the major reasons for the cessation of development and withdrawal from the market. Proteomics enjoys advantages of rapidness,high sensitivity and high throughout,and therefore can be used in the search for new biomarkers of hepatotoxicity in preclinical studies,leading to the development of safer drugs and a more efficient drug discovery process. In this review,the current preclinical biomark?ers of liver toxicity and development of proteomic technologies in the discovery and validation of bio?markers of drug-induced liver injury are described,in general the application of proteomics to Chinese medicine-induced liver toxicity in particular. Compared with traditional methods,proteomic technologies show promising results for the discovery of novel hepatotoxic markers. Proteomics,in conjugation with other omics techniques,will play a major role in the early stage of hepatotoxicity screening and will prove to be a good bridge in clinics in the future.

Objective To improve the skills and level of TURP and peri-operative period managements to the high risky senile patients with large volume benign prostatic hyperplasia.Methods The clinical data of 318 high risky and senile patients whose ages above 80ys,ASA score ＞ 2 and prostate volume ＞ 60g were analyzed retrospectively.They underwent the treatment of TURP.ResultsTotal 318 patients underwent TURP were safe.The operating time ranged from 40 to 85 minutes,averaged 58.2minutes;the volume of blood transfusion ranged from 200ml to 600ml;No serious complications happened during and after the operation.With follow up of 1 ～ 12 months,the International Prostate Symptom Scores(I-PSS) decreased 14.7 averagely,Quality of Life(QOL) decreased 3.3 averagely,Maximal flow rate( Qmax ) increased 6.4ml/s averagely,and Post-voided Residual(PVR) decreased 85.3ml averagely.Conclusion The actions including sufficient preparations and evaluations pre-operatively,the maintenance to the stability of circulatory system during the operation,guaranteeing the demands of blood exchange in the vital organs such as heart,lungs and brain,are the key points to the success.

ObjectiveTo improve the treatment of urethral stricture and evaluate the applicative effects of wire-guided supporting catheterization in clinical urology. MethodsDuring January 2004 and December 2005, the patients with urethral stricture were dwelled with the catheters using the traditional guideless catheterization (group A). If the dwelling failed,it will be replaced by the wire-guided supporting catheterization using the improved catheters. During January 2006 and December 2009, the patients with urethral stricture were dwelled with the improved catheters using wire-guided supporting catheterization ( group B) straightly. Compared the first-time dwelling success incidence,the incidence of catheter associated urinary tract infection and side-effect events. Then after 1 year, compared the urethral stricture recrudescence and the course of treatment. ResultsThe success incidence of first-time catheterization in group A was 48.98％ (24/49) ,and in group B was 97.94％ (95/97) ,there was significant difference between 2 groups;The incidence of catheter associated urinary tract infection in group A was 12.24％, and 8. 25％ in group B, there was significant difference between 2 groups; As for the course of treatment, group A was 46. 2w,group B was 32.7w;The urethral stricture recrudescence in group A and B were 16.33％ and 9.28％ respectively,and there was significant difference between 2 groups. ConclusionThe wire-guided supporting catheterization, which minimize the injury and simplify the operation of internal urethrotomy,could makes the improved catheter easy to induct and replace, improve the success rate of first time-catheterization and prevent the false tunnel damage and new scar expansion. It could make benefit to reduce of incidence of catheter associated urinary tract infection and urethral stricture recrudescence,but also could shorten the course of treatment significantly.

Objective:To evaluate the effect of deep learning based on DWI and fluid attenuated inversion recovery (FLAIR) to construct a prediction model of the onset time in acute stroke.Methods:A total of 324 cases of acute stroke with clear onset time, from January 2017 to May 2020 in Nanjing First Hospital, were retrospectively enrolled and analyzed. The patients were divided into a training set of 226 patients and a test set of 98 patients according to the complete randomization method using a 7∶3 ratio, and the patients were divided into ≤ 4.5 h and >4.5 h according to symptom onset time in each group. The acute infarction areas on DWI and the corresponding high signal area on FLAIR were manually outlined by physician. Using the InceptionV3 model as the basic model for image features extraction, the deep learning prediction model based on single sequence (DWI, FLAIR) and multi sequences (DWI+FLAIR) were established and verified. Then the area under curve (AUC), accuracy of human readings, single sequence model and multi sequence model in predicting the acute stroke onset time from imaging were compared.Results:DWI-FLAIR mismatch was found in 94 cases (94/207) of patients with symptom onset time from imaging ≤ 4.5 h, while in 28 cases (28/117) of patients with symptom onset time from imaging >4.5 h. ROC analysis showed that the AUC of DWI-FLAIR mismatch in predicting acute stroke onset time from imaging was 0.607, and the accuracy was 60.2%. The prediction model of deep learning based on single sequence showed that the AUC of FLAIR was 0.761 and the accuracy was 71.4%; the AUC of DWI was 0.836 and the accuracy was 81.6%. The AUC of predicting stroke onset time based on the multi-sequence (DWI+FLAIR) deep learning model was 0.852, which was significantly better than that of manual identification ( Z = 0.617, P = 0.002), FLAIR sequence deep learning model ( Z = 2.133, P = 0.006) and DWI sequence deep learning model ( Z = 1.846, P = 0.012). Conclusion:The deep learning model based on DWI and FLAIR is superior to human readings in predicting acute stroke onset time from imaging, which could provide guidance for intravenous thrombolytic therapy for acute stroke patients with unknown onset time.

Patients with end-stage liver disease often have malnutrition caused by reduced nutrient intake, increased energy consumption, impaired fasting adaptability, reduced liver glycogen reserve, and increased protein consumption. L3 skeletal muscle index (L3-SMI) (skeletal muscle cross-sectional area at the level of L3/square of height) is an important indicator for evaluating malnutrition in end-stage liver disease, with the advantages of strong objectivity, little influence by water-sodium retention, and good repeatability. This article reviews the application of L3-SMI in the nutritional diagnosis of liver cirrhosis, liver failure, liver cancer, and liver transplantation. The analysis shows that L3-SMI can effectively evaluate nutritional status and the effect of nutritional intervention in patients with end-stage liver disease, and therefore, it is expected to become an important method for nutritional diagnosis in end-stage liver disease.