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Learning about break-induced replication from bacteriophages

Author Correction: Molecular machineries and pathways of mitochondrial protein transport

Degrons: defining the rules of protein degradation

In vivo analysis of Drosophila chondroitin sulfate biosynthetic genes

Molecular basis of IFN-γ–induced STAT3 phosphorylation stimulated by Sendai virus C protein

The ubiquitin ligase Nedd4-2 promotes localization of DNMBP/Tuba to P-bodies under hyperosmotic stress

Molecular machineries and pathways of mitochondrial protein transport

Leptin and G-protein coupled receptor (GPCR) signaling: Therapeutic potential in obesity

Prevalence and determinants of medicinal plants utilization during labour among women of reproductive age in Butiama, Tanzania: A community-based cross-sectional study

Background The use of certain medicinal plants during childbirth has been linked to negative outcomes such as uterine rupture and foetal distress, both globally and in Sub-Saharan Africa including Tanzania. Despite this, little is known about the factors influencing women’s use of medicinal plants during labour or delivery in Tanzania. This study sought to assess the prevalence and determinants of medicinal plants use during labour and/or delivery in the Butiama district. Methodology This community-based quantitative study used a cross-sectional design with 398 participants, selected through multistage sampling. Data were collected via a structured questionnaire in Swahili language using the Kobo Toolbox from June to July 2024. Analysis was performed with SPSS version 27.0, including checks for completeness before data entry. Descriptive statistics were computed for univariate analysis, while bivariate analysis, conducted through cross-tabulation, determined relationships between variables. Multivariate logistic regression identified significant predictors at p < 0.05. Results In a study of 398 participants, 233(58.5%) reported using medicinal plants during labour and/or delivery. Peasants and homemakers had 2.6 times higher odds of using medicinal plants than those in formal employment (AOR = 2.584, 95% CI: 1.249–5.349, p = 0.011). Women with one child were 1.8 times more likely to use medicinal plants than those with two or more children (AOR = 1.823, 95% CI: 1.136–2.926, p = 0.013). Women within five kilometres of a health facility had 47.7% lower odds of using medicinal plants compared to their counterparts (AOR = 0.523, 95% CI: 0.334–0.819, p = 0.005). Married and cohabiting women were 42.1% less likely to use medicinal plants than divorced/separated/widowed women (AOR = 0.579, 95% CI: 0.338–0.990, p = 0.046). Women with fewer than four antenatal visits were 55.6% more likely to use medicinal plants compared to those with four or more visits. (AOR = 0.556, 95% CI: 0.365–0.848, p = 0.006). Conclusion Over fifty percent of participants reported using medicinal plants during childbirth, with socio-economic status and healthcare access factors suggesting that targeted education and interventions around medicinal plant use would be beneficial.

Please stay out of the abandoned buildings

Failure characteristics of unsaturated intact loess under different hydraulic pathways

This study systematically investigates the effects of two hydraulic pathways—wetting followed by loading (W-L) and loading followed by wetting (L-W)—on the water retention and strength characteristics of intact loess from a Xi’an metro line. Using an improved unsaturated triaxial testing system, experiments were conducted under controlled suction, net confining pressure, and shear stress levels. The Van Genuchten model accurately describes the water retention behavior, with the saturation-suction ratio (s/ S c ) exhibiting a linear relationship. The Critical State Line (CSL) for the L-W pathway exhibits a lower slope than that for the W-L pathway, indicating a reduction in shear strength and that hydraulic pathways strongly influence the suction contribution to loess strength. A threshold line in the q-p ’ plane is identified, suggesting that hydraulic effects must be considered when the pre-wetting stress state exceeds this threshold. Scanning electron microscopy (SEM) analysis combined with quantitative pore analysis reveals that W-L induces pore expansion and cement dissolution, while L-W promotes particle compaction, partial cement fragmentation, and a measurable refinement of the pore network.

Prevalence and predictors of transfusion-transmitted infections among blood donor types at a teaching hospital in Ghana: Implications for haemovigilance

Introduction Transfusion-transmitted infections (TTIs), notably hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), and syphilis, remain a major threat to blood safety in resource-limited settings. Ghana mainly uses rapid diagnostic tests (RDTs) for initial screening, which may affect the accuracy of detecting TTIs. Objective This study estimated the prevalence, identified sociodemographic and donor-type predictors of TTIs and compared the diagnostic yield of RDTs versus ELISA in a teaching hospital in the Central Region of Ghana. Methods This retrospective study analysed 10,152 available blood donors’ records screened from January 2022 to March 2024. Fixed-site donors were initially screened using RDTs (SD Biosensor Standard Q® HBsAg/HCV Ab, First Response® HIV 1-2.0, Advanced Quality™ Syphilis) followed by confirmatory ELISA testing for RDT-non-reactive samples. Mobile donors underwent ELISA (ChemWell® FUSION analyser) testing only. Multivariable logistic regression was used to identify independent TTI predictors. Results The overall prevalence of TTIs (infection with at least one tested pathogen) was 16.5% (95% CI: 15.80-17.20; N = 1,675), with syphilis 8.4% (95% CI: 7.83-8.91; N = 850) being the most common. Voluntary donors had a lower TTI prevalence than replacement donors (10.6% vs 19.9%, p < 0.001). Repeat donors exhibited reduced risk of HBV (aOR: 0.254, 95% CI: 0.206-0.313, p < 0.001), HCV (aOR: 0.734, 95% CI: 0.568-0.949, p = 0.018), and syphilis (aOR: 0.486, 95% CI: 0.417-0.567, p < 0.001). However, donor type itself was not a significant predictor of TTIs after adjusting for sociodemographic variables. ELISA testing identified an additional 7.3% (95% CI: 6.67-8.01; N = 422/5,754) TTI cases among RDT non-reactive fixed-site donors (missed cases). Conclusion The high prevalence of TTIs highlights persistent blood safety challenges. Repeat donation was independently protective, reducing risks of HBV, HCV, and syphilis. To improve blood safety, it will be essential to encourage regular voluntary donations. It will also require supplementing RDTs with ELISA where feasible, and strengthening haemovigilance systems, while accounting for the cost and logistical constraints. Although NAT is the gold standard for TTI detection, nationwide implementation in Ghana is currently not feasible.

Causal predictive modeling of survival of lung and bronchus cancer patients diagnosed during 2010–2011 in Texas

Background Lung and Bronchus cancer is the most fatal type of cancer in the United States. According to the American Cancer Society, there were more than 127,000 deaths from lung cancer in 2023. Lung cancer care cost 23.8 billion dollars in 2020. In Texas, only 22.8% of lung cancer patients survived 5 years or more past diagnosis based on 2012–2018 data. Aim This study evaluates the survival length of lung and bronchus cancer patients in Texas using advanced statistical and machine learning methods applied to an 11-year cohort study from Surveillance, Epidemiology, and End Results Program. It also quantifies the causal effect of early (localized) versus late (distant) stage at diagnosis on survival time of those patients. Additionally, it explores the influence of demographic and available clinical factors to assess disparities in survival across different groups. Methodology We performed classical survival analyses, followed by causal survival analysis to study the average years lost among different patient groups. Additionally, we performed survival random forest and survival neural network modeling. Finally, we conducted causal inference and causal survival random forest to estimate and predict the average treatment effect of early-stage diagnosis on lung cancer patient survival. Results Stage and age are the two most important factors in predicting the survival of patients with lung and bronchus cancer. Lung cancer patients diagnosed with the regional stage have about twice the risk of dying as those in the localized stage at any time, and this risk increases as the stage advances. We also find that the average extended lifetime of the localized stage group was about 4 years compared to survivors diagnosed with the distant stage. It can also extend the probability of survival by up to 50%. Conclusion Our study underscores the need for early screening, diagnosis and improving equity in lung cancer patients care, which could lead to improved outcomes and reduced mortality in this high-risk population. Impact Understanding lung and bronchus cancer survival using advanced causal inference and predictive modeling techniques, highlights the critical importance of early-stage diagnosis, showing that patients diagnosed at localized stages have a substantially higher survival probability. This research underscores the necessity of promoting early screening and equitable cancer care to improve survival rates and healthcare outcomes for lung and bronchus cancer patients.

Suicide risk characteristics of vocational college students: A latent profile analysis

Background Guided by the stress-diathesis model, this study employed latent profile analysis to investigate heterogeneity in suicide risk profiles and inform targeted intervention strategies among college vocational students. Methods Data were collected from 1,620 vocational college students identified as high-risk for suicide. Validated instruments—including the Adolescent Life Events Scale (ASLEC), Symptom Checklist-90 (SCL-90), and Social Support Rating Scale (SSRS)—were used to assess stress factors (negative life events), symptom factors (depression, anxiety, psychosomatic symptoms), diathesis traits (neuroticism, adverse childhood experiences), and protective factors (social support). Latent profile analysis (LPA) was applied to identify distinct risk subgroups. Results LPA revealed three distinct risk subgroups: a High-risk group (17.4%), characterized by severe psychological symptoms, elevated suicide preparation, and impaired social functioning; a Moderate-risk group (46.5%), defined by neuroticism, persistent despair, and intermediate symptom severity; and a Low-risk group (36.1%), distinguished by robust social support and minimal psychopathological manifestations. Regression analyses indicated that negative life events, depressive symptoms, and neuroticism significantly predicted suicide risk, while social support served as a protective factor. Conclusions These findings validate the stress-diathesis framework and advance suicide prevention research by operationalizing heterogeneous risk profiles through LPA. The tripartite classification system offers actionable insights for tiered campus mental health interventions, suggesting crisis management for high-risk individuals, resilience-building for moderate-risk groups, and preventive support for low-risk populations.

Machine learning algorithms for predicting and identifying the influencing predictors of antenatal care visits among women in Bangladesh: Evidence from BDHS 2022 data

Background and Objective Bangladesh, a South Asian country, continues to face significant challenges in maternal health, as reflected by its high maternal mortality ratio (MMR). According to the 2022 Bangladesh Demographic and Health Survey (BDHS), the MMR is 156 deaths per 100,000 births. This figure highlights ongoing challenges in maternal healthcare, despite improvements in recent years. Utilizing antenatal care (ANC) is a crucial intervention for reducing maternal mortality, as it enables early detection and treatment of complications, promotes health-seeking behavior, and prepares women for a safe childbirth. Thus, this study aimed to apply machine learning algorithms to predict the status of ANC visits and identify influential predictors among women in Bangladesh. Materials and Methods The study used BDHS 2022 data of 5,128 women aged 15–49 years. The outcome variable was ANC, defined as having at least four visits during pregnancy. We employed Boruta and Stepwise regression to identify the important predictors associated with ANC. Subsequently, ten different machine learning algorithms— decision tree, random forest, artificial neural network, logistic regression, adaptive boosting, extreme gradient boosting, gradient boosting, k-nearest neighbors, ranger (RG), and support vector machine—were trained on the training set to predict ANC visits. The predictive performance of the models was evaluated using accuracy, precision, recall, F1-score, and AUC on the test set, Results, The RG model performed best in predicting ANC visit status, with an accuracy of 69.46%, a precision of 68.51%, a Recall of 80.80%, an F1-score of 77.72%, and an AUC of 0.734, compared to the other models. The RG model identified age, wealth index, region, husband’s education, respondent education, and place of residence as the influential predictors of ANC utilization among women in Bangladesh, Conclusion The RG model and the identified influential predictors offer valuable insights for designing targeted public health strategies to enhance ANC utilization among women in Bangladesh.

An interpretable and balanced machine learning framework for Parkinson’s disease prediction using feature engineering and explainable AI

Parkinson’s disease (PD) is a progressive neurological disorder that affects millions globally, posing significant challenges in early and accurate diagnosis. Recent advancements in machine learning (ML) offer promising approaches for addressing these challenges by enabling more precise and efficient PD predictions. This paper proposes an enhanced ML framework for PD prediction, integrating data balancing, feature selection, and explainable AI techniques. We evaluate nine different ML algorithms using a dataset of clinical and voice features. To address the class imbalance, we employ the Synthetic Minority Oversampling Technique (SMOTE) and NearMiss, comparing results to an imbalanced baseline. Feature engineering approaches, including Featurewiz, Tree based Feature Importance and the chi-square test, are utilized to identify key predictive features such as Pitch Period Entropy (PPE), Noise-to-Harmonic Ratio (NHR), and other voice biomarkers. Explainable AI (XAI) techniques (SHAP and LIME) interpret model decision-making and highlight influential features. The best-performing model, KNN with SMOTE, achieved 92% accuracy, F1-score 0.94, and a G-Mean of 0.95—demonstrating balanced, reliable PD detection. While some models achieved higher accuracy on imbalanced data (up to 97%), their performance lacked sensitivity and balance. Our findings suggest that combining SMOTE with feature engineering and XAI substantially enhances model fairness, performance, and interpretability. This research advances PD prediction by providing an accurate and interpretable ML-based diagnostic tool to support early diagnosis and better patient management.

Prevalence of violence in a clinical sample of adolescent patients visiting a child and adolescent psychiatric outpatient clinic in Nepal

Background Child violence is a global concern affecting the well-being and development of children and adolescents worldwide. Despite the obvious need, few studies on child violence have been conducted in clinical samples, especially in low- and middle-income countries. Objective The aim of this study was to examine the prevalence of different types of violence in past-year among adolescent psychiatric patients in Nepal. Participants and setting The participants were 810 adolescents aged 11–15, 392 boys and 418 girls, who visited a child- and adolescent psychiatric outpatient unit in Kathmandu during a 12-month period. Methods We used a descriptive, quantitative, cross-sectional design. Data was collected with screening instruments completed by the adolescents themselves. Prevalence rates and range of occurrence of various forms of child violence were computed for both genders. Gender comparisons were conducted using Pearson chi-square tests. Adolescents rated the occurrence in the “rarely”, “sometimes”, “often” or “frequently” categories. Associations between the different forms were examined by Spearman’s correlation test. Results In this study 88% of adolescents had experienced some forms of violence, girls reporting higher prevalence than boys last year. Emotional abuse was the most common. Neglect was reported by 25% of the adolescents, and domestic violence by 40%. Sixty percent of the adolescents had experienced peer aggression. Nearly 75% of the adolescents had experienced polyvictimization and it was higher in girls than boys. Significant correlations were found between several forms of violence, indicating compounded risks. Conclusions The study demonstrates high prevalence of multiple forms of violence among adolescent psychiatric patients, calling for increased awareness of child violence in young patients admitted to mental health institutions in Nepal.

Design and optimization of soft finger actuators for rehabilitation applications: A combined finite element and neural network approach

This study presents a comprehensive analysis of soft finger actuators using finite element modeling to assess their performance in various structural configurations. By conducting detailed numerical simulations, we explore how variations in structural parameters influence the bending angle, thereby guiding iterative design improvements. Specifically, the research examines the impact of critical design factors, such as the number of bellows, actuator height, surrounding thickness, and foot thickness, on the bending behavior of soft actuators. The objective is to optimize these actuators for use in rehabilitation training gloves, where precise motion control is para-mount. Our findings reveal that increasing both the height and the number of bellows significantly enhances the achievable bending angle, facilitating more effective rehabilitation exercises. In contrast, greater foot and surrounding thicknesses exhibit a restrictive effect on bending, underscoring the need to carefully consider these parameters in design processes. These insights are instrumental in formulating design guidelines that aim to optimize actuator performance in therapeutic applications. Crucially, the manuscript presents a rigorous comparison between the experimental results and simulation results, demonstrating a high degree of concordance that validates the FEM approach and the predictions of the neural networks. This close match between the observed and predicted data not only confirms the reliability of the simulations, but also enhances the credibility of the design recommendations for rehabilitation applications. Furthermore, the study uses artificial neural networks to predict bending angles with high precision. With a residual variance of just 0. 74% and an explained variance of 99. 26%, the neural network model demonstrates exceptional predictive capacity, highlighting its potential as a tool for further refinement of the design and optimization of the performance of soft actuators. This research not only advances our understanding of soft actuator mechanics, but also contributes to the development of more effective rehabilitation technologies.

Pediatric cochlear implantation: The impact of frequency-to-place mismatch after a three-year follow-up

Introduction Frequency-to-place mismatch in cochlear implants (CIs) may influence auditory and speech outcomes, yet its impact on pediatric patients remains underexplored. This study aims to assess the impact of frequency-to-place mismatch on hearing and speech outcomes. Materials and methods In this retrospective study, the angular insertion depth and center frequency of each electrode contact were derived. The difference between the tonotopic and default frequency was used to estimate the mismatch. The impact of this mismatch on auditory and speech outcomes was assessed in pediatric CI users. Results This study included 89 implanted ears of young children. The analysis revealed a significant difference between default and postoperative electrode frequencies, with greater mismatches observed in shorter electrode arrays. A weak but significant correlation was observed between mismatch and sound field-aided thresholds (SF-AT), while no clear trends were found in other outcome measures. Patients with lower mismatches tended to perform better, while those with mismatches exceeding 7 semitones showed slightly lower—though non-significant—speech performance. However, a significant difference was observed in SIR, favoring the lower mismatch group. Conclusion Children with <7 semitones of fequency-to-place mismatch showed better, though non-significant, outcomes across multiple measures—including SDS, CAP, and SF-AT—and a significant difference in SIR. Despite no significant linear correlations overall, these findings suggest that specific mismatch levels may still influence outcomes after three years of CI use. Future studies should investigate whether tonotopic-based mapping improves speech perception and overall auditory performance in young CI users.

From bonding to action: The influence of generalized and interpersonal trust on voluntary membership among European adults

This study examines the moderating role of social trust (generalized and particularized/interpersonal) at the national level on the relationship between age (middle-aged 45–59 years; older adults 60–74 years; and long-lived 75 years and older) and membership in voluntary organizations in general and of various types. We hypothesize that in all three age groups, people in countries with higher levels of general and specific trust are more likely to engage in volunteer activities. At the same time, participation in volunteer activities is expected to decline gradually with age. The sample comprises individuals over 45 years of age ( N  = 28,198) in 36 countries in Europe. The empirical estimation uses data from the 2017/22 European Values Survey. Multilevel analysis is used to allow hierarchical aggregation of variables from different levels: individual, national and welfare system. The study reveals that generalized trust is positively associated with volunteering membership among people aged 45 and older. However, it is interpersonal trust that is positively related to voluntary membership among people aged 75 years and older. Furthermore, the influence of the two types of trust varies according to the type of membership. The research highlights that although public and social policies in recent years have promoted the voluntary activity of older adults, not all ages are the same; each age group has a series of characteristics that must be taken into consideration for such an increase in volunteering to take place at all ages.