Nevin Manimala

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

J Environ Manage. 2026 Jul 5;413:130432. doi: 10.1016/j.jenvman.2026.130432. Online ahead of print.

ABSTRACT

Urban eutrophic lakes in subtropical regions represent a persistent management challenge due to their strong internal feedbacks that resist rehabilitation. We report a four-year whole-lake intervention evaluating the effectiveness of artificial aeration in a 10 m-deep, eutrophic urban lake in Uruguay, using a Before-After Control-Impact (BACI) design with a comparable reference lake. Despite continuous operation, aeration did not alter the seasonal warm-monomictic regime nor eliminate summer deep-layer anoxia. However, it partially weakened thermal stratification and drove a sustained, statistically significant decline in surface total phosphorus (∼18 μg P L^-1 yr^-1), shifting the TP-based trophic state index from hypereutrophic to eutrophic. In contrast, chlorophyll-a remained hypereutrophic throughout, revealing a marked decoupling between nutrient availability and phytoplankton productivity. Persistent cyanobacterial dominance, rising phosphorus-use efficiency, and cycles of intense bloom growth, collapse, and rapid recovery indicate physiological adjustment to declining phosphorus rather than a proportional loss of biomass. The reference lake showed no equivalent directional changes, supporting the attribution of these trends to aeration. The intervention altered specific processes-thermal stability and internal phosphorus release-without shifting the dominant feedbacks sustaining eutrophy, consistent with “pathological resilience”, wherein internal mechanisms perpetuate a degraded state despite external perturbation. Aeration alone proved insufficient to trigger structural reorganization within four years, underscoring the need for integrated, adaptive rehabilitation strategies in urban lakes with strong self-sustaining eutrophic feedbacks.

PMID:42402233 | DOI:10.1016/j.jenvman.2026.130432

Int J Pediatr Otorhinolaryngol. 2026 Jul 2;207:112922. doi: 10.1016/j.ijporl.2026.112922. Online ahead of print.

ABSTRACT

OBJECTIVE: To describe the incidence, clinical presentation, and management of postoperative complications following injection laryngoplasty for minor laryngeal clefts and to assess their impact on postoperative dysphagia evolution.

METHODS: A unicentric retrospective case-control study of pediatric patients who underwent injection laryngoplasty for minor laryngeal clefts (hyaluronic acid-based injectable or sodium carboxymethylcellulose) between January 2018 and October 2025 in a tertiary pediatric center was conducted. Demographics, comorbidities, intraoperative variables, postoperative dysphagia outcomes, and Penetration-Aspiration Scale scores were analyzed. Ordinal logistic regression was used to evaluate the overall dysphagia trajectory.

RESULTS: Among 48 patients, 10 (20.8%) developed postoperative complications. Case and control groups were comparable in age, sex, weight percentiles, gestational age and most comorbidities. Operative duration, injected volume, and intraoperative antibiotic use were similar (all p > 0.2). Swallowing outcomes (resolution, improvement, persistence, or worsening) did not differ between groups. Ordinal logistic regression showed no association between complications and the dysphagia trajectory (Odds ratio, OR 5.1, p = 0.2). PAS severity categories were also similar (p = 0.8). Patients with hyaluronic acid appeared to have more complications (26.5%) compared to methylcellulose (7.1%), despite not reaching statistical significance.

CONCLUSION: Despite a higher-than-expected complication rate, postoperative inflammatory or infectious complications were not linked to worse swallowing function or higher PAS scores. These findings inform risk-benefit discussions and support informed shared decision-making with families regarding minor cleft treatment options.

PMID:42402231 | DOI:10.1016/j.ijporl.2026.112922

Int J Med Educ. 2026 Jul 2;17:90-96. doi: 10.5116/ijme.6a31.6bd0.

ABSTRACT

OBJECTIVES: This study examines the prevalence and impact of bullying and sexual harassment among students in nursing and medical education programmes in Sweden.

METHODS: This cross-sectional survey targeted students from 38 universities. A total of 18,582 individuals responded to the questionnaire, yielding a 25% response rate. The sample included students enrolled in nursing (N = 1,083) and medical (N = 431) programmes. Data were analysed using descriptive statistics and two-sample t-tests.

RESULTS: Among female students who experienced bullying, higher levels of stress ( t ₍₁₁₈₈₎ = 4.91, p < .001), burnout (t₍₁₁₈₈₎ = 5.83, p < .001), and intention to quit studies ( t ₍₁₁₈₆₎ = 4.30, p < .001) were reported. Bullied male students showed elevated stress ( t ₍₃₁₇₎ = 3.15, p = .002), burnout ( t ₍₃₁₇₎ = 3.49, p < .001), and intention to quit ( t ₍₃₁₆₎ = 3.67, p < .001). Female students who experienced sexual harassment reported increased stress ( t ₍₁₁₈₅₎ = 4.02, p < .001), burnout ( t ₍₁₁₈₅₎ = 4.10, p < .001), and intention to quit ( t ₍₁₁₈₄₎ = 2.73, p = .006). In contrast, sexually harassed male students reported higher stress ( t ₍₃₁₄₎ = 2.04, p = .042), but no significant differences in burnout ( t ₍₃₁₄₎ = 0.80, p = .425) or intention to quit ( t ₍₃₁₄₎ = 1.86, p = .064). Students from the nursing- and medical programme reported a higher prevalence of bullying and sexual harassment than other students.

CONCLUSIONS: Given the high prevalence and detrimental effects of bullying and sexual harassment in nursing and medical education, targeted interventions are needed to prevent and address these behaviours.

PMID:42402223 | DOI:10.5116/ijme.6a31.6bd0

Echocardiography. 2026 Jul;43(7):e70546. doi: 10.1111/echo.70546.

ABSTRACT

BACKGROUND: The prevalence of valvular disease in the US is approximately 2.5%. Given that over 5 million Americans are affected, it is crucial to find those at higher risk of progression. The goal of this paper is to uncover which patients are more likely to have valvular disease progression using demographic factors, EKG and echocardiogram (ECHO) findings.

METHODS: This paper utilized the EchoNext Database, which pairs 100,000 electrocardiograms and echocardiograms based on specific structural heart disease labels. Univariate and multivariate logistic regression were used to find risk factors for progression for aortic stenosis (AS), aortic regurgitation (AR), mitral regurgitation (MR), tricuspid regurgitation (TR) and pulmonary insufficiency (PI). Progression was defined as worsened valvular disease on follow up ECHOs.

RESULTS: There were 100,000 encounters of EKGs/ECHOs included in the study, and 36,286 individual patients. Independent risk factors of AS progression were age and QRS duration. Age, QRS duration, AS, AR, MR and pericardial effusion were independent risk factors for AR progression. MR progression was independently associated with PR, QRS and QTc durations, MR and left ventricular ejection fraction. QRS duration, MR, TR and right ventricular function were independent factors for TR progression. Lastly, for PI progression, male sex and AS were found to be independent risk factors.

CONCLUSIONS: The combination of demographic, electrocardiographic and echocardiographic findings can help better assess the progression of valvular disease.

PMID:42402188 | DOI:10.1111/echo.70546