As an increasing number of emerging anti-tumor drugs are approved and marketed,the imperative for clinical safety monitoring and risk information management has grown significantly.Drug-induced neuropathy associated with these drugs exhibit characteristics such as insidious onset,rapid progression,and challenging treatment,ultimately leading to treatment failures.Therefore,a comprehensive understanding of the risk of neuropathy induced by emerging anti-tumor drugs,coupled with risk surveillance and early warning,as well as management and reporting,can significantly reduce the incidence and severity of drug-related diseases.This paper provides a review of the neuropathy caused by emerging anti-tumor drugs,introduces the pharmacovigilance system and risk information management measures in clinical usage,aiming to provide a reference for guiding the rational clinical use and minimizing the incidence of drug-induced diseases.

Objective:To evaluate the value of preoperative prediction of vessel invasion (VI) of locally advanced gastric cancer by machine learning model based on the venous phase enhanced CT radiomics features.Methods:A retrospective analysis of 296 patients with locally advanced gastric cancer confirmed by pathology in the First Affiliated Hospital of Zhengzhou University from July 2011 to December 2020 was performed. The patients were divided into VI positive group ( n=213) and VI negative group ( n=83) based on pathological results. The data were divided into training set ( n=207) and test set ( n=89) according to the ratio of 7∶3 with stratification sampling. The clinical characteristics of patients were recorded, and the independent risk factors of gastric cancer VI were screened by multivariate logistic regression. Pyradiomics software was used to extract radiomic features from the venous phase enhanced CT images, and the minimum absolute shrinkage and selection algorithm (LASSO) was used to screen the features, obtain the optimal feature subset, and establish the radiomics signature. Four machine learning algorithms, including extreme gradient boosting (XGBoost), logistic, naive Bayes (GNB), and support vector machine (SVM) models, were used to build prediction models for the radiomics signature and the screened clinical independent risk factors. The efficacy of the model in predicting gastric cancer VI was evaluated by the receiver operating characteristic curve. Results:The degree of differentiation (OR=13.651, 95%CI 7.265-25.650, P=0.003), Lauren′s classification (OR=1.349, 95%CI 1.011-1.799, P=0.042) and CA199 (OR=1.796, 95%CI 1.406-2.186, P=0.044) were independent risk factors for predicting the VI of locally advanced gastric cancer. Based on the venous phase enhanced CT images, 864 quantitative features were extracted, and 18 best constructed radiomics signature were selected by LASSO. In the training set, the area under the curve (AUC) of XGBoost, logistic, GNB and SVM models for predicting gastric cancer VI were 0.914 (95%CI 0.875-0.953), 0.897 (95%CI 0.853-0.940), 0.880 (95%CI 0.832-0.928) and 0.814 (95%CI 0.755-0.873), respectively, and in the test set were 0.870 (95%CI 0.769-0.971), 0.877 (95%CI 0.788-0.964), 0.859 (95%CI 0.755-0.961) and 0.773 (95%CI 0.647-0.898). The logistic model had the largest AUC in the test set. Conclusions:The machine learning model based on the venous phase enhanced CT radiomics features has high efficacy in predicting the VI of locally advanced gastric cancer before the operation, and the logistic model demonstrates the best diagnostic efficacy.

Objective:To analyze the changes of diagnosis and treatment before and after renal biopsy in adult patients with acute kidney disease (AKD), and to explore the value of renal biopsy in the diagnosis and treatment of AKD.Methods:It was a single-center retrospective observational study. The adult patients with AKD who underwent renal biopsy in the Department of Nephrology of the First Affiliated Hospital of Nanjing Medical University from January 1, 2017 to December 31, 2021 were enrolled. Demographic data, general clinical data, laboratory tests, and diagnosis and treatment data before and after renal biopsy were collected to analyze the concordance rate between clinical and pathological diagnoses, changes in treatment after renal biopsy, and bleeding complication.Results:A total of 575 patients diagnosed with AKD by renal biopsy were included in this study, with age of 51 (36, 63) years old and 359 males (62.4%). Among them, there were 293 patients (51.0%) of acute kidney injury, 348 patients (60.5%) of hypertension and 124 patients (21.6%) of diabetes. The peak serum creatinine was 272 (190, 477) μmol/L. The hemoglobin was 106 (86, 126) g/L. The 24-hour urine protein was 2.15 (0.79, 4.82) g. There were 347 patients (60.3%) of acute glomerular diseases, 136 patients (23.7%) of acute interstitial nephritis, 47 patients (8.2%) of thrombotic microangiopathy, and 45 patients (7.8%) of acute tubular necrosis. The most common types of acute glomerular diseases were IgA nephropathy and anti-neutrophil cytoplasmic antibody-associated glomerulonephritis, accounting for 22.3% (128/575) and 12.2% (70/575), respectively. The clinical diagnoses before renal biopsy were consistent with the renal histopathological diagnoses in 454 patients, with an accuracy rate of 79.0%. Following the renal biopsy, the treatment plan involving glucocorticoids or immunosuppressants was adjusted in 394 patients (68.5%). Significant post-biopsy bleeding occurred in 15 patients (2.6%), with 12 patients requiring blood transfusion and 1 patient requiring surgical intervention.Conclusions:Twenty-one clinical diagnoses do not match the pathological diagnoses in adult AKD patients, 68.5% of patients have changes in their treatment plans, and 2.6% of patients have significant hemorrhagic complications after renal biopsy. Clinicians need to carefully consider the benefits and risks and make individualized decisions about renal biopsy.