BACKGROUNDUnhygienic blood collection in the early 1990s led to blood-borne infections in Central China. This study aimed to estimate human immunodeficiency virus (HIV) co-infection with hepatitis C and B viruses (HCV and HBV) and their risk factors in a rural area of Shanxi Province with a history of commercial blood donation.

METHODSA cross-sectional study was conducted in 2004. All adult residents in the target area were invited to participate in the study. Face-to-face interviews were completed and blood specimens were tested for HIV, HCV, and HBV surface antigen (HBsAg).

RESULTSPrevalence rates of HIV, HCV, and HBsAg were 1.3% (40/3 062), 12.7% (389/3 062), and 3.5% (103/2982), respectively. Of the 40 HIV-positive specimens, 85% were HCV positive and 2.5% were HBsAg positive. The history of commercial blood donation was positively associated with HIV, HCV, and HIV/HCV co-infections, but was negatively associated with HBsAg seropositivity. Migration for employment in the last 5 years was positively related to HIV, HBsAg, and HIV/HCV co-infections. Univariate logistic analysis showed that illegal drug use, number of sex partners, extramarital sex behavior, commercial sex behavior, and condom use rate were not related to anti-HIV, anti-HCV, HBsAg seropositivity or their co-infections.

CONCLUSIONThe history of commercial blood donation was the main risk factor for HIV, HCV, and HIV/HCV co-infections in this former commercial blood donation area. HIV and HCV prevention and treatment interventions are important in this area.

Adolescent ; Adult ; Blood Donors ; China ; epidemiology ; Cross-Sectional Studies ; Female ; HIV Infections ; epidemiology ; etiology ; Hepatitis B ; epidemiology ; etiology ; Hepatitis C ; epidemiology ; etiology ; Humans ; Male ; Middle Aged ; Transfusion Reaction ; Young Adult

OBJECTIVE To evaluate the efficacy and safety of antibody-drug conjugates （ADC） in the treatment of breast cancer， so as to provide an evidence-based reference for clinical medication. METHODS Retrieved from CNKI， Wanfang database， VIP， PubMed， the Cochrane Library， Embase， and Web of Science， randomized controlled trials （RCTs） about trastuzumab emtansine， trastuzumab deruxtecan and sacituzumab govitecan （trial group） versus chemotherapy or other anti-tumor drugs （control group）， were collected during the inception to April 2023. After screening the literature， extracting data， and evaluating the quality of the literature， a meta-analysis was conducted by using RevMan 5.4.1 software. RESULTS A total of 8 RCTs were included， with a total of 5 577 patients. The results of the meta-analysis showed that the progression-free survival （PFS） [HR＝0.76， 95%CI （0.69， 0.83）， P＜0.000 01]， overall survival （OS） [HR＝0.87， 95%CI （0.81， 0.93）， P＜0.000 1]， and clinical benefit rate （CBR） [OR＝2.70， 95%CI （1.15， 6.33）， P＝0.02] of the trial group were significantly higher than control group. There was no statistically significant difference in objective response rate （ORR） between the two groups [OR＝2.34， 95%CI （0.59， 9.33）， P＝0.23]. The results of subgroup analysis showed that the PFS of HER2-positive patients and HER2-negative patients， and the OS of HER2-positive patients in the trial group were significantly higher than control group （P＜0.05）. The incidence of anemia and increase of aspartic acid transaminase （AST） in the trial group was significantly higher than control group （P＜0.05）. The results of sensitivity analysis showed that the results obtained with PFS， OS， and ORR as indicators were relatively robust， while the results obtained with CBR as indicators lacked robustness. CONCLUSIONS ADC drugs have significant effects on breast cancer， but will increase the risk of anemia and elevated AST.

Objective To evaluate the efficiency of intra-tumor and peri-tumor ultrasound radiomics models based on machine learning algorithms for predicting benign and malignant Breast Imaging Reporting and Data System (BI-RADS) 4 breast lesions, and provide insights into early diagnosis of breast cancer. Methods A retrospective analysis was conducted based on the medical records of 450 female patients who underwent breast ultrasound examination in the Affiliated Cancer Hospital of Xinjiang Medical University from June 2020 to April 2022. The patients were divided into the benign (n = 199) and malignant (n = 195) groups according to pathological examination, and randomized into the training (n = 275) and validation (n = 119) sets at a ratio of 7∶3. Radiomics features were extracted and screened. Intra-tumor, peri-tumor, and intra-tumor + peri-tumor ultrasound radiomics models were constructed based on three machine learning algorithms, including logistic regression (LR), support vector machine (SVM), and multi-layer perceptron (MLP). Receiver operating characteristics (ROC) curves, calibration curves, and decision curves were plotted to evaluate the efficacy of the radiomics models for prediction of benign and malignant breast lesions. Results A total of 17 intra-tumor, 16 peri-tumor, and 17 intra-tumor + peri-tumor radiomics features were selected for model construction. Based on LR, MLP, and SVM algorithms, the intra-tumor + peri-tumor radiomics models showed higher predictive efficacy than intra-tumor and peri-tumor radiomics models. The predictive efficacy of intra-tumor, peri-tumor, and intra-tumor + peri-tumor radiomics models were higher based on the SVM algorithm than based on LR and MLP algorithms. For the intra-tumor radiomics model based on the SVM algorithm, the area under the ROC curve (AUC), accuracy, sensitivity, and a specificity were 0.909, 0.851, 0.860, and 0.842, respectively, in the training set and 0.866, 0.832, 0.847, and 0.817, respectively, in the validation set. For the peri-tumor radiomics model based on the SVM algorithm, these values were 0.899, 0.855, 0.882, and 0.827, respectively, in the training set and 0.844, 0.815, 0.847, and 0.783, respectively, in the validation set. For the intra-tumor + peri-tumor radiomics model based on the SVM algorithm, these values were 0.943, 0.876, 0.860, and 0.892, respectively, in the training set and 0.881, 0.849, 0.915, and 0.783, respectively, in the validation set. Conclusion The intra-tumor and peri-tumor ultrasound radiomics models based on machine learning algorithms are highly valuable for prediction of benign and malignant BI-RADS 4 breast lesions. The intra-tumor + peri-tumor ultrasound radiomics model based on the SVM algorithm has the optimal efficacy for prediction of benign and malignant BI-RADS 4 breast lesions.