Intimate partner violence during pregnancy: To screen or not to screen?

Intimate partner violence (IPV) during pregnancy emerges as a compelling and urgent concern within the domain of public health, casting a long shadow over a substantial cohort of women. Its pernicious consequences extend beyond the individual, enveloping the well-being of both the mother and the fetus, giving rise to an elevated risk of preterm birth, low birth weight, fetal harm, and maternal psychological distress, including depression, anxiety, post-traumatic stress disorder, and, tragically, maternal mortality. Despite the prevalence of IPV being comparable to other conditions like gestational diabetes and preeclampsia, a universal screening protocol for IPV remains absent globally. We reviewed the clinical guidelines and practices concerning IPV screening, painstakingly scrutinizing their contextual nuances across diverse nations. Our study unveils multifaceted challenges of implementing universal screening. These hurdles encompass impediments to victim awareness and disclosure, limitations in healthcare providers' knowledge and training, and the formidable structural barriers entrenched within healthcare systems. Concurrently, we delve into the potential biomarkers intricately entwined with IPV. These promising markers encompass inflammatory indicators, epigenetic and genetic influences, and a diverse array of chemical compounds and proteins. Lastly, we discussed various criteria for universal screening including (1) valid and reliable screening tool; (2) target population as pregnant women; (3) scientific evidence of screening programme; and (4) integration of education, testing, clinical services, and programme management to minimise the challenges, which are paramount. With the advancement of digital technology and various biomarkers identification, screening and detecting IPV in clinical settings can be conducted systemically. A systems-level interventions with academia-community-indutrial partnerships can help connect pregnant women to desire support services to avoid adverse maternal and child health outcomes.

Casual effects of telomere length on sarcoidosis: a bidirectional Mendelian randomization analysis.

Background: Telomere length, crucial for genomic stability, have been implicated in various inflamm-aging diseases, but their role in sarcoidosis remains unexplored. Objective: This study aims to explore the casual effects between TL and sarcoidosis via a bidirectional Mendelian Randomization (MR) study. Methods: We examined single nucleotide polymorphisms (SNPs) associated with TL and sarcoidosis, utilizing available open-access genome-wide association study (GWAS) databases from the UK Biobank and FinnGen. We employed five MR techniques, including Inverse Variance Weighted (IVW), MR Egger, weighted median (WM), Robust adjusted profile score (RAPS), and Maximum likelihood, to assess causal relationships and explore pleiotropy. Results: Summary data extracted from GWAS datasets of TL (n = 472,174) and (n = 217,758) of European ancestry. Employing 130 SNPs with genome-wide significance as instrumental factors for TL, we detect a significant negative correlation between TL and sarcoidosis (OR: 0.682, 95% confidence interval: 0.524-0.888, p : 0.0045). Similarly, utilizing 6 SNPs with genome-wide significance as instrumental factors for sarcoidosis, we fail to identify a noteworthy association between sarcoidosis and TL (OR: 0.992, 95% confidence interval: 0.979-1.005, p : 0.2424). Conclusion: Our results suggest that longer telomeres may reduce the risk of sarcoidosis, highlighting TL as a potential biomarker for diagnosis and long-term monitoring. Understanding the critical role of telomere shortening enables more effective focus on diagnosing, treating, and curing sarcoidosis linked to telomeres. Clinical investigations into treatments that enhance TL are warranted.

Data-driven rapid detection of Helicobacter pylori infection through machine learning with limited laboratory parameters in Chinese primary clinics.

Background: Helicobacter pylori (H. pylori) is a significant global health concern, posing a high risk for gastric cancer. Conventional diagnostic and screening approaches are inaccessible, invasive, inaccurate, time-consuming, and expensive in primary clinics. Objective: This study aims to apply machine learning (ML) models to detect H. pylori infection using limited laboratory parameters from routine blood tests and to investigate the association of these biomarkers with clinical outcomes in primary clinics. Methods: A retrospective analysis with three ML and five ensemble models was conducted on 1409 adults from Hubei Provincial Hospital of Traditional Chinese Medicine. evaluating twenty-three blood test parameters and using the C 14 urea breath test as the gold standard for diagnosing H. pylori infection. Results: In our comparative study employing three different feature selection strategies, Random Forest (RF) model exhibited superior performance over other ML and ensemble models. Multiple evaluation metrics underscored the optimal performance of the RF model (ROC = 0.951, sensitivity = 0.882, specificity = 0.906, F1 = 0.906, accuracy = 0.894, PPV = 0.908, NPV = 0.880) without feature selection. Key biomarkers identified through importance ranking and shapley additive Explanations (SHAP) analysis using the RF model without feature selection include White Blood Cell Count (WBC), Mean Platelet Volume (MPV), Hemoglobin (Hb), Red Blood Cell Count (RBC), Platelet Crit (PCT), and Platelet Count (PLC). These biomarkers were found to be significantly associated with the presence of H. pylori infection, reflecting the immune response and inflammation levels. Conclusion: Abnormalities in key biomarkers could prompt clinical workers to consider H. pylori infection. The RF model effectively identifies H. pylori infection using routine blood tests, offering potential for clinical application in primary clinics. This ML approach can enhance diagnosis and screening, reducing medical burdens and reliance on invasive diagnostics.

Evolution of Chatbots in Nursing Education: Narrative Review.

Background: The integration of chatbots in nursing education is a rapidly evolving area with potential transformative impacts. This narrative review aims to synthesize and analyze the existing literature on chatbots in nursing education. Objective: This study aims to comprehensively examine the temporal trends, international distribution, study designs, and implications of chatbots in nursing education. Methods: A comprehensive search was conducted across 3 databases (PubMed, Web of Science, and Embase) following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. Results: A total of 40 articles met the eligibility criteria, with a notable increase of publications in 2023 (n=28, 70%). Temporal analysis revealed a notable surge in publications from 2021 to 2023, emphasizing the growing scholarly interest. Geographically, Taiwan province made substantial contributions (n=8, 20%), followed by the United States (n=6, 15%) and South Korea (n=4, 10%). Study designs varied, with reviews (n=8, 20%) and editorials (n=7, 18%) being predominant, showcasing the richness of research in this domain. Conclusions: Integrating chatbots into nursing education presents a promising yet relatively unexplored avenue. This review highlights the urgent need for original research, emphasizing the importance of ethical considerations.

Horizontal gene transfer is predicted to overcome the diversity limit of competing microbial species.

Natural microbial ecosystems harbor substantial diversity of competing species. Explaining such diversity is challenging, because in classic theories it is extremely infeasible for a large community of competing species to stably coexist in homogeneous environments. One important aspect mostly overlooked in these theories, however, is that microbes commonly share genetic materials with their neighbors through horizontal gene transfer (HGT), which enables the dynamic change of species growth rates due to the fitness effects of the mobile genetic elements (MGEs). Here, we establish a framework of species competition by accounting for the dynamic gene flow among competing microbes. Combining theoretical derivation and numerical simulations, we show that in many conditions HGT can surprisingly overcome the biodiversity limit predicted by the classic model and allow the coexistence of many competitors, by enabling dynamic neutrality of competing species. In contrast with the static neutrality proposed by previous theories, the diversity maintained by HGT is highly stable against random perturbations of microbial fitness. Our work highlights the importance of considering gene flow when addressing fundamental ecological questions in the world of microbes and has broad implications for the design and engineering of complex microbial consortia.

Application of machine learning for risky sexual behavior interventions among factory workers in China.

Introduction: Assessing the likelihood of engaging in high-risk sexual behavior can assist in delivering tailored educational interventions. The objective of this study was to identify the most effective algorithm and assess high-risk sexual behaviors within the last six months through the utilization of machine-learning models. Methods: The survey conducted in the Longhua District CDC, Shenzhen, involved 2023 participants who were employees of 16 different factories. The data was collected through questionnaires administered between October 2019 and November 2019. We evaluated the model's overall predictive classification performance using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. All analyses were performed using the open-source Python version 3.9.12. Results: About a quarter of the factory workers had engaged in risky sexual behavior in the past 6 months. Most of them were Han Chinese (84.53%), hukou in foreign provinces (85.12%), or rural areas (83.19%), with junior high school education (55.37%), personal monthly income between RMB3,000 (US$417.54) and RMB4,999 (US$695.76; 64.71%), and were workers (80.67%). The random forest model (RF) outperformed all other models in assessing risky sexual behavior in the past 6 months and provided acceptable performance (accuracy 78%; sensitivity 11%; specificity 98%; PPV 63%; ROC 84%). Discussion: Machine learning has aided in evaluating risky sexual behavior within the last six months. Our assessment models can be integrated into government or public health departments to guide sexual health promotion and follow-up services.

Multifaceted Determinants of Sexual Intercourse with Non-Regular Female Sex Partners and Female Sex Workers among Male Factory Workers in China-A Cross-Sectional Survey.

With a stratified multi-stage sampling approach, 1361 male factory workers in the Longhua district of the Shenzhen Municipality of China were selected to investigate the multifaceted determinants of sexual intercourse with non-regular female sex partners (NRP) and female sex workers (FSW) among them. The results showed that 24.5% and 21.2% of participants had sexual intercourse with NRP and FSW in the past 6 months, respectively. More specifically, at the individual level, perceived higher job stress and maladaptive coping styles were linked with a higher likelihood of having sexual intercourse with NRP and FSW (adjusted odds ratios [AOR] ranged from 1.06 to 1.17). At the interpersonal level, those who had higher exposure to information related to sexual intercourse with NRP or FSW were more likely to have sex with these female sex partners (AOR: 1.08 & 1.11). At the social structural level, perceived social norms supporting multiple sex partnerships were linked with a higher likelihood of having sexual intercourse with NRP and FSW (AOR: 1.10 & 1.11). No interaction effects were found between the variables at different levels. Providing pre-employment training to clarify roles and job duties, introducing adaptive coping strategies, and addressing misconceptions of social norms are useful strategies to reduce sexual intercourse with NRP or FSW.