Tag: pubmed

Matern Health Neonatol Perinatol. 2026 Jul 6;12(1):30. doi: 10.1186/s40748-026-00275-6.

ABSTRACT

BACKGROUND: Mistreatment during childbirth remains a barrier to maternal health in low-resource settings.

OBJECTIVE: To assess the prevalence of mistreatment during childbirth and its associated factors among women in public health facilities of Ilubabor Zone, Southwest Ethiopia, 2025.

METHODS: A facility-based cross-sectional study was conducted from August to October 2025 among 470 women using multistage sampling and interviewer-administered questionnaires. Data were analyzed with descriptive statistics and multivariable logistic regression.

RESULTS: Mistreatment during childbirth was reported by 68.1% (95% CI: 63.9-72.3%) of women. Verbal insults, denial of mobility during labor, refusal of pain relief, and lack of consent for procedures were the most common forms. Significant factors included low maternal education (AOR = 7.10, 95% CI: 3.71-13.58), observed provider burnout (AOR = 3.30, 95% CI: 2.14-5.10), and high provider workload (AOR = 2.70, 95% CI: 1.80-4.06).

CONCLUSION: High prevalence of mistreatment was observed, driven by low maternal education and multiple provider- and system-level factors. Enhanced training, staffing, and community education are urgently needed.

PMID:42402616 | DOI:10.1186/s40748-026-00275-6

Trop Med Health. 2026 Jul 5. doi: 10.1186/s41182-026-01016-3. Online ahead of print.

ABSTRACT

BACKGROUND: Diphtheria remains a life-threatening disease in low-resource settings where immunization coverage is poor and access to a laboratory is limited. Somalia is currently experiencing a resurgence of clinically suspected diphtheria among children, yet detailed case-level clinical data remain scarce. The purpose of this study was to illustrate clinical features, vaccination status, complications, and early outcomes of pediatric diphtheria in a resource-limited Somali hospital. Furthermore, it identified the factors associated with severity and mortality for enhanced early recognition and management.

METHODS: A prospective observational study was conducted, including 51 pediatric patients aged < 18 years admitted with clinically diagnosed diphtheria to a tertiary referral hospital in Somalia between January 2025 and December 2025. Diagnosis was based on WHO clinical criteria due to the absence of local laboratory capacity. Demographic, clinical, treatment, and outcome data were collected prospectively. The requirement for pediatric intensive care unit admission defined disease severity. Statistical comparisons between severe and non-severe cases used appropriate parametric or non-parametric tests, and univariable logistic regression explored predictors of severity and mortality. Statistical significance was set at p < 0.05. This study was guided by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines to guarantee transparent, thorough, and consistent demonstration of the observational methods and results.

RESULTS: Fifty-one children met the clinical case definition (median age 6.0 years; 52.9% male). Classical airway manifestations were frequent: pseudomembrane (88.2%), bull neck (88.2%), stridor (58.8%), and airway obstruction (52.9%). Vaccination coverage was extremely low, with only 9.8% having received any diphtheria-containing vaccine. Systemic complications included neuritis in 47.1% and myocarditis in 19.6%. Eleven patients (21.6%) required PICU admission and were classified as severe. Toxin-mediated complications strongly predicted severity: myocarditis (72.7% in severe vs 5.0% non-severe) and neuritis (100% vs 32.5%). Eight children died (overall case-fatality rate: 15.7%), all in the PICU cohort. Mortality was highest among children with myocarditis (50.0%) and neuritis (33.3%), and all deaths occurred in unvaccinated children. Univariable logistic regression revealed that toxin-mediated problems such as myocarditis and neuritis were robust predictors of severe disease and mortality (p < 0.05), whereas the classical airway features and vaccination status were not substantial.

CONCLUSION: Clinically diagnosed pediatric diphtheria in Somalia is characterized by profound under-immunization, high rates of classical airway features, significant toxin-mediated complications, and substantial mortality. Early identification of severe illness is essential, particularly recognition of cardiac and neurological involvement. Strengthening immunization programs, ensuring timely diphtheria antitoxin availability, and expanding critical care capacity are urgent priorities to improve outcomes.

PMID:42402610 | DOI:10.1186/s41182-026-01016-3

Clin Epigenetics. 2026 Jul 5. doi: 10.1186/s13148-026-02197-8. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial metabolism-driven epigenetic modifications have emerged as crucial regulators for acute myeloid leukemia (AML) progression, linking metabolic activity in leukemic stem cells to epigenetically controlled transcriptional programs that drive oncogenic gene expression.

RESULTS: Here, by integrating proteomic and transcriptomic data, we identified six genes whose expression were able to predict outcome in AML. Among these, IDH3B was highly expressed in leukemic stem cells and associated with poor prognosis. Functional studies revealed that IDH3B deletion in KMT2A-rearranged AML increased global protein succinylation, reduced acetylation, and sensitized cells to the menin-KMT2A inhibitor, both in vitro and in vivo. Mechanistically, loss of IDH3B, by increasing histone succinylation and reducing H3K79 methylation at the MYC promoter, amplified Revumenib-induced transcriptional repression of MYC.

CONCLUSIONS: These findings establish IDH3B as a key metabolic-epigenetic regulator in AML and highlight it as a potential synergistic target to enhance menin inhibition therapy.

PMID:42402603 | DOI:10.1186/s13148-026-02197-8

BMC Med Educ. 2026 Jul 6. doi: 10.1186/s12909-026-09841-0. Online ahead of print.

ABSTRACT

This study examines whether undergraduate students in an introductory statistics course report different learning outcomes and experiences based on whether they use code-based or non-code-based statistical software. The sample included 2,241 students enrolled in courses using the Passion-Driven Statistics curriculum across 61 post-secondary institutions. Seventy-two percent of participants learned a code-based platform (R, SAS, Stata, Python), while 27.6% learned a non-code-based platform (SPSS, Excel, JMP, StatCrunch). Mixed-effects cumulative logit and logistic regression models were used to compare outcomes between groups while accounting for clustering of students within courses and adjusting for student demographics and academic background. Students in code-based courses had higher odds of reporting that they worked harder and found the course more challenging than those in non-code-based courses. At the same time, learning a code-based platform was positively associated with perceived gains in analyzing data for patterns, greater excitement about learning new concepts, and increased interest in conducting research. However, students learning code-based software reported feeling less prepared for advanced disciplinary coursework or thesis work. Overall, the results suggest that learning to work with code is associated with greater engagement and interest in data-driven work, even as it introduces greater challenges. These findings highlight the potential value of incorporating code-based statistical tools into undergraduate curricula while also underscoring the importance of supporting students through the initial learning curve.

PMID:42402579 | DOI:10.1186/s12909-026-09841-0

BMC Public Health. 2026 Jul 6. doi: 10.1186/s12889-026-28394-x. Online ahead of print.

ABSTRACT

BACKGROUND: Health perception mismatch is common in older adults, yet its longitudinal patterns and prognostic value remain unclear. This study aimed to identify distinct mismatch trajectories and their associations with depression, cognitive decline, and mortality.

METHODS: Data from three longitudinal ageing cohorts-the China Health and Retirement Longitudinal Study (CHARLS), English Longitudinal Study of Ageing (ELSA), and Health and Retirement Study (HRS)-were used to identify health perception mismatch trajectories and evaluate their associations with depression, cognitive impairment, and mortality. Baseline SRH-ADL/IADL interplay was examined within these longitudinal cohorts, while the National Health and Nutrition Examination Survey (NHANES) was retained as an independent external cross-sectional comparison. Associations were evaluated using multivariable regression, structural equation modeling, inverse probability of treatment weighting, causal forests, random-effects meta-analysis, and counterfactual prediction.

FINDINGS: Baseline analyses in CHARLS, ELSA, and HRS showed heterogeneous and generally modest SRH-ADL/IADL additive interaction estimates, while NHANES provided an independent external cross-sectional comparison. Three stable perception mismatch trajectories, Mildly pessimistic, Mildly optimistic, and Highly optimistic, were consistently identified across CHARLS, ELSA, and HRS. Despite reporting better SRH, individuals in the Highly optimistic group had poorer baseline health and higher subsequent risks of depression, cognitive decline, and mortality. Counterfactual prediction analyses indicated 13-18% differences in mortality and depression risk between hypothetical trajectory contrasts, with the largest differences observed between the mildly pessimistic and highly optimistic classes, suggesting that discordantly favorable SRH may mark elevated risk.

INTERPRETATION: Health perception mismatch trajectories may help identify older adults at elevated health risk. Discordantly optimistic perceptions may mark underlying vulnerability, suggesting that combining subjective and objective assessments could complement existing approaches to risk stratification. However, these observational findings do not establish that modifying health perceptions or trajectory membership would alter subsequent outcomes.

PMID:42402575 | DOI:10.1186/s12889-026-28394-x

Cardiovasc Diabetol. 2026 Jul 5. doi: 10.1186/s12933-026-03279-w. Online ahead of print.

ABSTRACT

BACKGROUND: The triglyceride-glucose (TyG) index and its composite obesity indices have been linked to cardiovascular disease (CVD) risk. However, most prior studies relied on single baseline measurements, and few have employed group-based multi-trajectory modeling to capture concurrent longitudinal changes in metabolic and anthropometric indicators. This study aimed to identify joint longitudinal trajectory groups of TyG combined with body mass index (BMI) and waist-to-height ratio (WHtR) using parallel approaches and evaluate their associations with incident CVD in middle-aged and older adults.

METHODS: This prospective cohort study included 1808 CVD-free participants aged ≥ 50 years from the English Longitudinal Study of Ageing. Group-based multi-trajectory modeling was applied to jointly identify latent trajectory classes using repeated measurements of TyG with BMI and TyG with WHtR across three waves over approximately 8 years. Cox proportional hazards models with four sequential adjustment models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for incident composite CVD, heart disease, and stroke. Subgroup analyses were stratified by age, gender, smoking, diabetes, and hypertension status. Nine sensitivity analyses were conducted to assess robustness.

RESULTS: During follow-up, 263 participants (14.5%) developed incident CVD. Four trajectory groups were identified for each approach. In fully adjusted BMI + TyG models, compared with the normal weight-low TyG reference group, composite CVD risk increased progressively across the overweight-moderate TyG (HR 2.31, 95% CI 1.40-3.83), obese-high TyG (HR 2.72, 95% CI 1.63-4.52), and severely obese-high TyG groups (HR 5.06, 95% CI 3.01-8.51). The WHtR + TyG approach demonstrated a consistent dose-response pattern, with HRs of 2.22, 2.75, and 5.12 for ascending risk groups. For stroke, only the highest-risk groups reached statistical significance (HR 6.74 and 5.00, respectively). Formal discriminative comparison showed no significant difference between the two approaches (C-statistic difference 0.003, P = 0.723). All nine sensitivity analyses consistently corroborated the primary findings.

CONCLUSIONS: Both approaches yield robust and comparable dose-response gradients, supporting further validation of serial TyG-related composite index monitoring for cardiovascular risk stratification in aging populations.

PMID:42402573 | DOI:10.1186/s12933-026-03279-w

BMC Public Health. 2026 Jul 6. doi: 10.1186/s12889-026-28343-8. Online ahead of print.

ABSTRACT

BACKGROUND: With the increasing frequency and intensity of extreme weather events due to climate change, heat waves have emerged as a significant public health threat. To date, the potential effect of extreme heat wave events, particularly when combined with air pollution, remains poorly understood for pregnancy outcomes among women undergoing assisted reproductive technology (ART).

METHODS: A retrospective study included 15,198 women receiving ART and 7519 fresh embryo transfer cycles between 2020 to 2022 at the Reproductive Center of West China Second University Hospital in Chengdu, China. Heat wave, a climate change indicator for extreme temperature events, was calculated based on daily temperature during the period of 85 days prior to oocyte retrieval. All environmental exposure variables, including weather and pollution, were matched geospatially to day 0 to day 85 before oocyte retrieval. Generalized linear model (GLMM) were used to assess the association between environmental exposures and ART outcomes, with secondary analysis using interaction terms between heat waves and individual pollutants.

RESULTS: Exposure to one heat wave event was positively correlated with the likelihood of becoming pregnant (+ 34.9% in univariate model and + 34.5% in multivariate model for heat wave events + 1 time) and this association was more pronounced in women under 35 years of age (+ 53.7% heat wave events + 1 time), while no statistical correlation was observed between exposure to two heat wave events and ART outcomes. Additionally, CO exhibited a significant negative association with biochemical pregnancy for women under 35 years old (-66.2% for CO + 1 mg/m³), and SO₂ exhibited a significant negative association on biochemical pregnancy rate for women older than 35 years old (-6.5% for SO₂ + 1 μg/m³). Results from the interaction model indicated that concurrent exposure to O₃ and two heat wave events was statistical associated with clinical pregnancy (OR = 3.77).

CONCLUSIONS: Findings from this study suggest that heat waves could be an important climatic indicator that reflects the impact of extreme weather on pregnancy outcomes among women receiving ART treatment. The synergy between exposure to extreme temperatures and air pollution could be further analyzed to provide deeper insight into the environmental impact on reproductive health.

PMID:42402568 | DOI:10.1186/s12889-026-28343-8

Epilepsy Behav. 2026 Jul 5;183:111180. doi: 10.1016/j.yebeh.2026.111180. Online ahead of print.

ABSTRACT

BACKGROUND: Functional dissociative seizures (FDS) are paroxysmal seizure-like events with no electrographic abnormalities. Structured psychoeducation is considered a standard component of FDS management. Yoga Nidra (YN) is a guided meditative relaxation technique that has shown benefit in other neuropsychiatric conditions, but its adjunctive role in FDS has not been studied.

OBJECTIVE: To evaluate whether adjunctive YN provides additional benefit over structured brief psychoeducation alone in reducing monthly FDS frequency.

METHODS: This open-label, randomised controlled pilot trial enrolled 50 FDS patients aged ≥13 years, randomised 1:1 to receive brief psychoeducation plus YN (n = 25) or brief psychoeducation plus sham YN (n = 25). All participants received structured psychoeducation; the intervention group additionally practised standardised audio-guided YN. The primary outcome was the change in monthly FDS frequency. Secondary outcomes included 6-month FDS episode count, seizure freedom duration, HAM-A, HAM-D, WSAS, and QOLIE-31 scores. Outcomes were assessed at 6 months by a blinded evaluator.

RESULTS: Between January 2021 and February 2022, 72 patients were screened, and 22 were excluded. Fifty patients were randomly assigned to receive brief psychoeducation plus YN (n = 25) or brief psychoeducation plus sham YN (n = 25), of whom primary outcome data were available for 48 patients at 6 months (23 patients in the brief psychoeducation plus YN group and 25 in the brief psychoeducation plus sham YN group). Median monthly FDS frequency reduced from 6 (IQR 2.5-45) to 0.5 (0-1) in the brief psychoeducation plus YN group and from 7 (3.5-45) to 0.5 (01.7) in the sham group; between-group difference was not statistically significant (p = 0.88).

CONCLUSION: Both groups demonstrated substantial clinical improvement during follow-up in seizure frequency, mood symptoms, functioning and quality of life; however, adjunctive YN did not provide measurable additional benefit over the control condition. Larger studies are needed to clarify the role of adjunctive YN in the management of functional dissociative seizures.

PMID:42402245 | DOI:10.1016/j.yebeh.2026.111180

Comput Biol Med. 2026 Jul 5;213:111839. doi: 10.1016/j.compbiomed.2026.111839. Online ahead of print.

ABSTRACT

Cardiovascular diseases have been the primary contributor to deaths worldwide, and hence, the need to detect arrhythmia from Electrocardiogram signals in a precise and efficient manner is a critical problem in the medical community. This work presents a lightweight and computationally efficient framework that integrates Discrete Wavelet Transform (DWT)-based statistical features, ECG morphological descriptors, and Artificial Bee Colony (ABC)-optimized eXtreme Gradient Boosting Machine (XGBM) classification for ECG beat analysis. This work has been implemented using the popular MIT-BIH Arrhythmia Database, which has 100,674 instances of ECG beats, divided into five AAMI classes, with a severe level of class imbalance, where 89.4% instances belong to the Normal class. ECG signals have been pre-processed using a seven-stage algorithm, including Butterworth high-pass filtering, notch filtering, Pan-Tompkins R-peak detection, beat segmentation, and normalisation. Then, a three-level Haar transform is implemented, and 32 statistical features have been extracted from the DWT decomposition, along with 32 morphological features, forming a 64-dimensional vector. The proposed ABC algorithm with 8 bees and 8 iterations optimizes the six XGBM model hyperparameters using a balanced fitness function of accuracy and macro F1-score and converges at the optimal fitness value of 0.8211. The proposed ABC-XGBM model has a classification accuracy of 95.14%, a macro F1-score of 0.948, a macro AUC of 0.983, Matthews Correlation Coefficient of 0.925, and G-Mean of 0.932 with class-wise AUC values > 0.94. An ablation study has shown that the proposed DWT adds +3.7% and the proposed ABC optimization adds +1.14% in accuracy improvement. Five-fold cross-validation has shown a stable performance with a mean accuracy of 0.952 ± 0.001 at a time complexity of 1.0 ms per sample without the dependency of the GPU. The proposed framework is better than the other deep learning models such as CardioAttentionNet with a classification accuracy of 91.20% and the proposed transformer-based classifier with a classification accuracy of 90.50%.

PMID:42402238 | DOI:10.1016/j.compbiomed.2026.111839

J Environ Manage. 2026 Jul 5;413:130402. doi: 10.1016/j.jenvman.2026.130402. Online ahead of print.

ABSTRACT

The cement industry contributes approximately 7-8% of global anthropogenic CO₂ emissions, yet accurate cradle-to-gate embodied carbon estimation requires plant-level inventory data that are largely unavailable across developing and emerging economies. This data scarcity constrains global benchmarking and the implementation of emerging embodied carbon regulations. This study proposes a STIRPAT-grounded hybrid machine learning-life cycle assessment (ML-LCA) framework for estimating national-scale cement embodied carbon using exclusively publicly available macroeconomic data. GDP per capita, population, and temporal indicators are used to predict the clinker-to-cement ratio (CCR), which is subsequently propagated through a technology-stratified, process-based LCA model enforcing stoichiometric and thermodynamic constraints across A1-A3 stages. Among seven candidate algorithms, Gradient Boosting was selected for its smooth non-linear approximation and LCA integration suitability. SHAP analysis confirms GDP per capita as the dominant CCR driver, with contributions directionally consistent with established technology diffusion theory, ensuring model transparency. Validation across 18 economies through statistical metrics, residual diagnostics, country-level diagnostic benchmarking, Leave-One-Country-Out (LOCO) cross-validation, and three independent literature-benchmarking countries (Pakistan, Mexico, Spain) confirms physically plausible and externally consistent outputs ranging from 0.53 to 0.97 kg CO₂/kg cement. A central methodological contribution is the ability to estimate the clinker-substitution decoupling effect at the country scale using only macroeconomic inputs, in contexts where plant-level LCA inventory data are unavailable. Conventional LCA already separates process, energy, and material composition contributions when inventory data are present; the present framework extends this separation to data-scarce national contexts. At the system level, an Environmental Kuznets Curve-type pattern is qualitatively reproduced when model outputs are aggregated across countries, providing a coherence check on the framework as a whole. Out-of-country generalisation is assessed using Leave-One-Country-Out (LOCO) cross-validation as the primary protocol (mean fold RMSE 0.077; 12 of 18 folds below RMSE 0.10), with a forward-chaining temporal split as a complementary diagnostic. The framework is operationalised through an interactive decision-support interface, offering a scalable, transparent baseline for embodied carbon benchmarking, policy screening, and net-zero pathway evaluation in the global cement sector. The framework is positioned as a screening-level reference for data-scarce contexts, complementary to plant-level LCA and Environmental Product Declarations where these are available.

PMID:42402234 | DOI:10.1016/j.jenvman.2026.130402