Objective: To analyze the demographic characteristics of patients with red blood cell transfusion reactions, the usage of red blood cell preparations, and the differences in the composition ratio of adverse reactions based on multi-center data from the Haemovigilance Network, in order to reveal the clinical characteristics of red blood cell transfusion and its underlying issues. Methods: Clinical data of patients who experienced transfusion reactions after red blood cell transfusion in the Haemovigilance Network from 2018 to 2023 were collected. The demographic characteristics of patients who experienced transfusion reactions with different types of red blood cell preparations, the utilization of these preparations, and the differences of the composition ratios of transfusion reactions were analyzed. Count data were expressed as numbers (n) or percentages (%), and comparisons between groups were performed using the Chi-square test. Results: Red blood cell transfusion reactions were more common in females (53.56%), with the majority of patients aged 50-69 years (35.54%). The Han polulation accounted for the vast majority of patients (92.77%), and patients in the hematology and obstetrics/gynecology departments had a relatively high proportion of transfusion reactions (13.26% and 14.26%, respectively). Leukocyte-reduced red blood cells and suspended red blood cells were the most common types of transfusion reactions reported among red blood cell preparations. Allergic reactions and non-hemolytic febrile reactions were the most common transfusion reactions, and there were significant differences in the composition ratios of allergic reactions (χ =869.89, P<0.05) and non-hemolytic febrile reactions (χ =812.75, P<0.05) across various types of red blood cell preparations. Conclusion: There are differences in the demographic characteristics and composition ratio of transfusion reactions among different red blood cell preparations. The management of red blood cell transfusion reactions should be tailored to patient characteristics and conditions, and the selection and use of blood products should be optimized to reduce or avoid the occurrence of transfusion reactions, such as considering the use of washed red blood cells for patients with a history of transfusion allergies or those prone to allergies.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Congenital heart disease (CHD) is one of the leading causes of heart failure (HF) in pediatrics.In 2024, the American Heart Association, based on existing knowledge and research findings, issued its first scientific statement on the assessment and management of HF in children and adolescents with CHD.The statement emphasizes the current lack of understanding of the epidemiology, pathophysiology, staging, and outcomes of chronic HF in pediatrics with CHD, and calls for the development of standardized definitions, monitoring protocols, and treatment strategies to improve the clinical outcomes and quality of life for this population.This article interprets the main content of the statement, aiming to provide reference and guidance for the accurate assessment and comprehensive management of chronic HF in children and adolescents with CHD.

Objective: To investigate the regulatory effect and mechanism of specific antagonist of acid-sensitive ion channel 3 (APETx2) on visceral sensitivity in mice with post-infectious irritable bowel syndrome (PI-IBS) ． Methods: The PI-IBS model was established by National Institutes of Health (NIH) mice infected with Trichinella spiralis．Gastrointestinal transport function was assessed by measuring the time to first black stool and the number of fecal pellets collected for 6 hours ; abdominal wall withdrawal reflex (AWR) was used to assess visceral sensitivity ; the expression of calcitonin gene-related peptide ( CGRP) in the colon tissue was detected by immunohistochemistry ; the expression of brain-derived neurotrophic factor (BDNF) and CGRP mRNA in the colon tissues was detected by quantitative real time polymerase chain reaction ( qRT-PCR) ． The expression levels of acid sensing ion channel 3 (ASIC3) ，CGRP，and transient receptor potential vanilloid 1 (TRPV1) protein in brain tissue were detected by Western blot analysis. Results: Compared with the control group，the PI-IBS group significantly reduced the time of first black stool，the number of fecal particles and AWR score within 6 hours significantly increased，the protein expression of CGRP in colon tissue，BDNF and CGRP mRNA significantly increased，and the protein expression of CGRP，ASIC3 and TRPV1 in brain tissue significantly increased．Compared with the control group，the PI-IBS group significantly reduced the time to first black stool，the number of fecal particles and AWR score within 6 hours significantly increased，the expression of CGRP protein in colon tissue，the expression of BDNF and CGRP mRNA significantly increased，and the protein expression of CGRP，ASIC3 and TRPV1 in brain tissue significantly increased ; compared with the PI-IBS group，the first time of black stool clearance in the APETx2 group was significantly prolonged，the number of fecal particles and AWR score within 6 hours were significantly reduced，the expression of CGRP protein in colon tissue，the expression of BDNF and CGRP mRNA was significantly reduced，the protein expression of CGRP，ASIC3 and TRPV1 in brain tissue was significantly reduced，and the difference was statistically significant (P＜0. 05) ． Conclusion APETx2 can alleviate visceral sensitivity and regulate gastrointestinal motility in PI-IBS mice by downregulating the expression of BDNF，CGRP，ASIC3 and TRPV1．APETx2 may provide a new therapeutic option for the treatment of IBS.

Recent studies revealed the relationship among homologous recombination repair (HRR), androgen receptor (AR), and poly(adenosine diphosphate-ribose) polymerase (PARP); however, the synergy between anti-androgen enzalutamide (ENZ) and PARP inhibitor olaparib (OLA) remains unclear. Here, we showed that the synergistic effect of ENZ and OLA significantly reduced proliferation and induced apoptosis in AR-positive prostate cancer cell lines. Next-generation sequencing followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed the significant effects of ENZ plus OLA on nonhomologous end joining (NHEJ) and apoptosis pathways. ENZ combined with OLA synergistically inhibited the NHEJ pathway by repressing DNA-dependent protein kinase catalytic subunit (DNA-PKcs) and X-ray repair cross complementing 4 (XRCC4). Moreover, our data showed that ENZ could enhance the response of prostate cancer cells to the combination therapy by reversing the anti-apoptotic effect of OLA through the downregulation of anti-apoptotic gene insulin-like growth factor 1 receptor ( IGF1R ) and the upregulation of pro-apoptotic gene death-associated protein kinase 1 ( DAPK1 ). Collectively, our results suggested that ENZ combined with OLA can promote prostate cancer cell apoptosis by multiple pathways other than inducing HRR defects, providing evidence for the combined use of ENZ and OLA in prostate cancer regardless of HRR gene mutation status.
Male ; Humans ; Prostatic Neoplasms, Castration-Resistant/genetics* ; Drug Resistance, Neoplasm/genetics* ; Cell Line, Tumor ; Receptors, Androgen/genetics* ; Nitriles ; Apoptosis

Adult ; Aged ; China/epidemiology* ; Cohort Studies ; Female ; Humans ; Male ; Metabolic Syndrome/etiology* ; Middle Aged ; Parks, Recreational/supply & distribution* ; Prevalence ; Residence Characteristics/statistics & numerical data* ; Rural Population/statistics & numerical data* ; Young Adult