Infertility affects approximately one-sixth of couples globally, with the incidence of male infertility steadily increasing. However, our understanding of the impact of viral infections on fertility remains limited. This review consolidates findings from previous studies, outlining 40 viruses identified in human semen and summarizing their key characteristics, modes of transmission, and their effects on both the reproductive and endocrine systems. Furthermore, it elucidates potential pathogenic mechanisms and treatment prospects of viruses strongly associated with male infertility. This synthesis will enhance our comprehension of how viral infections influence male reproductive health, offering valuable insights for future research as well as the diagnosis and treatment of infectious infertility. Abbreviated summary: Semen viral infections can damage the male reproductive system, further affecting semen quality and causing infertility, as well as sexually transmitted infections to partners and vertical transmission resulting in poor pregnancy outcomes.

This study aims to develop a machine learning (ML)-based predictive model for assessing the risk of multilobar pulmonary consolidation in children with macrolide-resistant Mycoplasma pneumoniae pneumonia (MRMP) caused by the 23S rRNA A2063G mutation, a subgroup underrepresented in prior studies. A total of 404 MRMP cases diagnosed between October 2024 and February 2025 were included in this study. Key clinical characteristics, including laboratory test results, symptoms, and treatment outcomes, were extracted from electronic medical records. Six ML models, including Logistic Regression, Naive Bayes, K-Nearest Neighbors, Multilayer Perceptron, Random Forest, and XG-Boost, were developed to predict multilobar pulmonary consolidation. Least absolute shrinkage and selection operator (LASSO) regression was used to select relevant variables. Model performance was then evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Finally, Sharpley Additive Explanations was used for model interpretability. XG-Boost demonstrated the highest predictive performance with an area under the ROC curve of 0.976 and 0.904 in the training and validation sets, respectively, showing a high sensitivity of 0.97, specificity of 0.81, accuracy of 0.94, and an F1 score of 0.95. Key predictors identified for multilobar pulmonary consolidation included the top 10 variables: C-reactive protein, lactate dehydrogenase, fibrinogen, platelet count, albumin, hemoglobin, creatinine, aspartate aminotransferase, interleukin-6, and oxygen therapy. DCA showed that the model also exhibited strong clinical utility. The XG-Boost predictive model offers a robust tool for identifying high-risk children with MRMP caused by the 23S rRNA A2063G mutation. By integrating clinical features, the model enhances early risk stratification and can support clinical decision-making, improving the accuracy and efficiency of treatment plans. IMPORTANCE: Macrolide-resistant Mycoplasma pneumoniae pneumonia caused by the 23S rRNA A2063G mutation poses a significant threat to pediatric health, often leading to severe multilobar pulmonary consolidation. This study develops a high-performance machine learning model (XG-Boost) that accurately predicts this complication using key clinical indicators such as C-reactive protein, lactate dehydrogenase, and IL-6. With an area under the ROC curve of 0.976, the model enables early risk stratification, guiding clinicians in optimizing treatment for high-risk children. By improving diagnostic precision and intervention timing, this tool can reduce disease severity, minimize hospital stays, and enhance patient outcomes. The interpretability of the model via Sharpley Additive Explanations analysis further ensures its clinical applicability, making it a valuable advancement in managing antibiotic-resistant pediatric pneumonia.

Chikungunya virus (CHIKV) infection during pregnancy presents a major threat to maternal-fetal health, yet a comprehensive global assessment of vertical transmission risks and perinatal outcomes remains scarce. To provide a comprehensive evidence synthesis, we conducted a systematic review and meta-analysis, prospectively registered in PROSPERO (CRD420251164423). We screened studies published up to October 20, 2025, and included 57 observational studies for qualitative synthesis, with 27 studies pooled in meta-analyses. The pooled vertical transmission rate was 18.1%, revealing a pronounced gestational-age gradient: rates were low in the first (3.9%) and second (1.2%) trimesters but surged dramatically during the third trimester (36.5%) and the intrapartum period (49.1%). Adverse pregnancy outcomes occurred in 11.3% of infected women, with abnormal fetal heart rate (44.9%) and stillbirth (22.0%) being the most frequent. Among neonates, 36.5% experienced adverse outcomes, commonly including feeding difficulties (79.4%), fever (68.9%), and thrombocytopenia (57.2%); neonatal mortality reached 6.9%. Crucially, meta-analysis of comparative studies demonstrated that maternal CHIKV infection was associated with a more than two-fold higher risk of adverse perinatal outcomes (OR = 2.28), with particularly robust associations identified for abnormal fetal heart rate (OR = 5.07) and delayed neurodevelopment (OR = 11.98). This study underscores that maternal CHIKV infection, especially during late gestation, substantially elevates the risks of vertical transmission and severe perinatal complications. Consequently, our findings strongly advocate for the implementation of enhanced prenatal surveillance and systematic postnatal assessment protocols in CHIKV-endemic regions to mitigate adverse outcomes and guide public health interventions.