Objective: To evaluate the practical effectiveness of confidential unit exclusion (CUE) in ensuring blood safety in Zhengzhou, analyze its application characteristics and existing problems, and provide a basis for optimizing blood safety management strategies. Methods: A retrospective analysis was conducted on CUE data handled by Henan Red Cross Blood Center from January 2019 to December 2024. Parameters such as the number of cases, demographic characteristics, reasons for exclusion, and time of report were statistically analyzed and compared with those of non-CUE. Results: From 2019 to 2024, the CUE reporting rate in Zhengzhou was 0.002 6% (40/1 547 666). CUE donors were predominantly male (65.00%, 26/40), aged 18-34 years (47.50%, 19/40), had college degree orabove (50.00%, 20/40), and were employees of enterprises or public institutions (32.50%, 13/40). Among the 40 CUE blood units, only one was reactive for anti-TP, while all others were qualified. The main reasons for CUE were recent vaccination (32.50%, 13/40), medical conditions unsuitable for donation (27.50%, 11/40), and high-risk sexual behavior (17.50%, 7/40). A total of 70.00% of reports occurred within 24 hours after donation, during which none of the corresponding blood units had been released; all units reported after more than 7 days had already been issued for clinical use, with no adverse transfusion reactions reported upon follow-up. Conclusion: The confidential unit exclusion program has played an active role in establishing a supplementary information feedback channel for blood donors. The procedure can be optimized by strengthening interactive communication and confirmation before donation, improving the accuracy of donors' self-assessment, and expanding convenient and rapid information-based reporting channels.

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 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.

Objective:To investigate the value of CT quantitative parameters of tumor and spleen in predicting the clinical stage of gastric cancer (Ⅰ/Ⅱ stage and Ⅲ/Ⅳ stage).Methods:This study was a case-control study. The data of 145 patients with gastric cancer confirmed by pathology in the First Affiliated Hospital of Zhengzhou University from February 2019 to June 2021 were retrospectively collected, including 70 cases of Ⅰ/Ⅱ stage and 75 cases of Ⅲ/Ⅳ stage. On the baseline CT images, the tumor related parameters, including tumor thickness, length of tumor, CT attenuation of tumor unenhanced phase, CT attenuation of tumor arterial phase, CT attenuation of tumor venous phase were measured. The spleen related parameters, including splenic thickness, CT attenuation of splenic unenhanced phase, CT attenuation of splenic arterial phase, CT attenuation of splenic venous phase, and standard deviation of CT attenuation (CTsd) in splenic unenhanced phase were also measured. The independent sample t test or Mann-Whitney U test was used to compare the parameters between the Ⅰ/Ⅱ stage and Ⅲ/Ⅳ stage patients. The multi-factor logistic regression analysis was used to find the independent predictors of gastric cancer clinical stage, and establish the combined parameters. The efficiency to the diagnosis of gastric cancer stage of single and combined parameters was evaluated using the operating characteristic curve, and the DeLong test was used to compare the differences of area under the curve (AUC). Results:There were significant differences in tumor thickness, length of tumor, CT attenuation of tumor venous phase, CT attenuation of splenic unenhanced phase, CT attenuation of splenic venous phase, CTsd in splenic unenhanced phase between the Ⅰ/Ⅱ stage and Ⅲ/Ⅳ stage of gastric cancer ( P<0.05). Multivariate analysis showed that tumor thickness ( OR=1.073, 95% CI 1.026-1.123, P=0.002), CT attenuation of splenic venous phase ( OR=1.040, 95% CI 1.011-1.070, P=0.006) and CTsd in splenic unenhanced phase ( OR=1.625, 95% CI 1.330-1.987, P<0.001) were independent risk factors for the clinical stage of gastric cancer and the combined parameters were established. The AUC values of tumor thickness, CT attenuation of splenic venous phase, CTsd in splenic unenhanced phase and combined parameters were 0.655, 0.614, 0.749 and 0.806, respectively. The AUC of combined parameters was higher than those of tumor thickness and CT attenuation of splenic venous phase, and the differences were statistically significant ( Z=3.37, 3.82, both P<0.001). Conclusion:Tumor thickness, CT attenuation of splenic venous phase and CTsd in splenic unenhanced phase are independent risk factors for the clinical stage of gastric cancer, and combined parameters can improve the diagnostic efficiency.

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.