Category: Nevin Manimala

Eur Stroke J. 2026 May 6;11(5):aakag039. doi: 10.1093/esj/aakag039.

ABSTRACT

INTRODUCTION: Sex-related disparities in acute ischaemic stroke (AIS) care have been widely reported. However, evidence from Italy remains limited. We aimed to evaluate sex differences in access to revascularisation treatments (RTs) and key time performance indicators in a large Italian cohort.

PATIENTS AND METHODS: We conducted a single-centre, retrospective, observational study including all adults admitted to the emergency department of a comprehensive stroke centre in Rome between January 2015 and December 2022 for suspected stroke. Clinical and demographic characteristics, comorbidities, presenting symptoms, RTs and stroke care time metrics were collected. Multivariable logistic and linear regression models, as well as restricted cubic spline analyses, were used to assess sex-related differences in RTs and time indicators, adjusting for relevant confounders.

RESULTS: Within the 9167 patients, 44.4% had AIS, and 48.2% were women. Among patients with AIS, women were older (P < .001), had higher NIHSS scores at onset (P < .001), a greater comorbidity burden (P < .001) and higher in-hospital mortality (P = .010). No significant sex differences were observed in intravenous thrombolysis and endovascular treatment administration rates. However, median door-to-needle time was 4 min longer in women than in men (P = .030). After adjustment, female sex was not significantly associated with RT administration or in-hospital mortality, but remained an independent predictor of longer DNT [adjusted β -8.242; 95% CI (-15.453 to -1.031); P = .025].

DISCUSSION AND CONCLUSIONS: Although access to RTs was comparable between sexes, women experienced longer DNT, indicating persistent in-hospital barriers to timely stroke care. These findings highlight the need for targeted interventions to achieve full equity in AIS management.

PMID:42114131 | DOI:10.1093/esj/aakag039

J Anim Sci. 2026 May 11:skag133. doi: 10.1093/jas/skag133. Online ahead of print.

ABSTRACT

Estimating individual dry matter intake (DMI) on pasture remains costly, labor-intensive, and episodic. We developed a deployable sequence model to predict daily, per-animal DMI using walk-on scale weights, metered water intake, animal metadata, and publicly available weather. A 3-layer long short-term memory (LSTM) network (hidden size = 256, dropout = 0.20) ingested 7-day windows of engineered covariates, including rolling statistics, first- and second-order differences, short lags, thermal ranges, and Temperature-Humidity Index (THI), scaled with a RobustScaler; categorical variables were encoded with learned embeddings. The prediction head was a compact multilayer perceptron, and outputs were inverse Box-Cox transformed to kilograms per day. Training used animal-identity-grouped splits, Huber loss, Adam optimization with gradient clipping, learning-rate scheduling, early stopping, and Optuna hyperparameter tuning. A single feature-scaler bundle was evaluated against National Academies of Sciences, Engineering, and Medicine (NASEM) equations and three strictly held-out external validations: regional drylot (Morgantown, WV), non-regional drylot (Hyplains, KS), and grazing (2023 Wardensville, WV). Using identical evaluation masks, the LSTM improved accuracy relative to NASEM (pooled RMSE 1.329 vs. 1.858 kg/d; R2 0.655 vs. 0.326), with the largest gains in grazing (LSTM RMSE 1.180, R2 0.507; NASEM RMSE 3.883, R2 -4.337). Iterative fine-tuning with a frozen encoder showed stable behavior under bias-only head updates across production systems. These results demonstrate that accurate, generalizable DMI prediction can be achieved from pragmatic inputs, enabling scalable evaluation of intake phenotypes and feed-efficiency traits directly in grazing systems.

PMID:42114127 | DOI:10.1093/jas/skag133

Genetics. 2026 May 11:iyag119. doi: 10.1093/genetics/iyag119. Online ahead of print.

ABSTRACT

In genetic association studies, permutation tests serve as a cornerstone to estimate p-values. This is because researchers may design new test statistics without a known closed-form distribution, or the assumption of a well-established test may not hold. However, permutation tests require a vast number of permutations which is proportional to the magnitude of the actual p-values. When it comes to genome-wide association studies where multiple-test corrections are routinely conducted, the actual p-values are extremely small, requiring a daunting number of permutations that may be beyond the available computational resources. Existing models that reduce the required number of permutations all assume a specific format of the test statistic to exploit its specific statistical properties. We propose Kernel-smoothed permutation which is a model-free method universally applicable to any statistic. Our tool forms the null distribution of test statistics using a kurtosis-driven transformation, followed by a kernel-based density estimation (KDE). We compared our Kernel-smoothed permutation to Naïve permutation using statistics from known closed-form null distributions. Based on three frequently used test statistics in association studies, i.e., t-test, sequence kernel association test (SKAT), and chi-squared test, we demonstrated that our model reduced the required number of permutations by a magnitude with similar or higher accuracy. Based on a real-world genome-wide association study (GWAS) analysis, we used Crohn’s disease cohort to further confirm that our model substantially outperforms the Naïve permutation.

PMID:42114112 | DOI:10.1093/genetics/iyag119

J Bras Nefrol. 2026 Jul-Sep;48(3):e20250199. doi: 10.1590/2175-8239-JBN-2025-0199en.

ABSTRACT

BACKGROUND: Chronic kidney disease (CKD) in dialysis patients compromises musculoskeletal health and reduces physical activity levels. The Dialysis Patient-Perceived Exercise Benefits and Barriers Scale (DPEBBS) was specifically developed to assess dialysis patients’ perceptions of exercise. This study aimed to translate, cross-culturally adapt, and evaluate the psychometric properties of the Brazilian version of the DPEBBS (EPAD).

METHODS: A cross-sectional study was conducted following the COSMIN guidelines. Psychometric properties assessed included reliability, internal consistency, and construct validity. Participants were recruited from the hemodialysis department of Unifesp. A total of 112 adults on dialysis completed the DPEBBS, the Short Form Health Survey-36 (SF-36), and underwent anthropometric evaluation. The interval between test-retest was one week. Descriptive and inferential analyses were performed to test validity and reliability.

RESULTS: The scale demonstrated high test-retest stability, with consistent mean scores across assessments. Internal consistency was strong, and reliability was supported by a low minimal detectable change and a high intraclass correlation coefficient. Convergent validity with the SF-36 Physical Functioning domain was weak but statistically significant (r = -0.326; p = 0.001), and the correlation with the General Health domain was weak and not statistically significant (r = -0.185; p = 0.052). Consistency analysis showed α = 0.885, ICC = 0.794, SEM = 4.96%, and demonstrated the absence of floor and ceiling effects.

CONCLUSION: The EPAD showed robust validity and reliability for dialysis patients. Despite adequate reliability and validity, this study has limitations, including a single-center sample. The EPAD may support individualized exercise counseling and rehabilitation planning in hemodialysis units.

PMID:42114104 | DOI:10.1590/2175-8239-JBN-2025-0199en

Ann Intern Med. 2026 May 12. doi: 10.7326/ANNALS-25-04761. Online ahead of print.

ABSTRACT

Human health and natural systems are intrinsically linked-stable natural systems enable healthy human life. Health systems aim to promote, restore, and maintain health. Health systems may promote human health while having detrimental effects on natural systems, contributing to the transgression of planetary boundaries, such as biosphere integrity, climate change, and the introduction of new entities like microplastics. To date, the health guideline field lacks methods to assess the impacts of health interventions on planetary boundaries. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group established the Planetary Health Project Group in 2023 to develop formal GRADE guidance for integrating planetary health into guideline recommendations to address this gap.

Guided by the concepts of planetary health and planetary boundaries and following established methods for GRADE guidance development, the project group conducted iterative case study analyses, expert workshops, and a 2-round global Delphi consensus process. Four case studies were selected for application of this guidance before recommendations were finalized. The GRADE Working Group approved the official guidance.

The Planetary Health Project Group presents 7 domains of guidance for incorporating planetary health aspects into the guideline development process, including highly desirable items and optional items. Highly desirable items include formally addressing planetary health in public health and health system guidelines and explicitly justifying its exclusion where it is not addressed. Judgments within the evidence-to-decision (EtD) framework should systematically integrate included evidence across the prioritized planetary boundaries and equity. This guidance aims to support guideline developers and policymakers in making evidence-based, trustworthy recommendations to protect individual and planetary health, while maintaining thoroughness and feasibility for guideline developers within the GRADE approach.

PMID:42114098 | DOI:10.7326/ANNALS-25-04761

Bioinformatics. 2026 May 11:btag289. doi: 10.1093/bioinformatics/btag289. Online ahead of print.

ABSTRACT

MOTIVATION: Phylogenetic networks represent complex biological scenarios that are overlooked in trees, such as hybridization and horizontal gene transfer. Although numerous methods have been developed for phylogenetic network inference, their scalability is severely limited by the computational demands of likelihood optimization and the vastness of network space. Composite (or pseudo-) likelihood approaches like SNaQ have improved computational tractability for network inference, but they remain inadequate for datasets of sizes routinely handled by tree inference methods.

RESULTS: Here, we introduce SNaQ.jl, a new standalone Julia package with the composite likelihood inference originally implemented within PhyloNetworks.jl as well as new scalability features that enhance computational efficiency through (1) parallelization of quartet likelihood calculations during composite likelihood computation, (2) weighted random selection of quartets, and (3) probabilistic decision-making during network search. Through a simulation study and empirical data analysis, we show that this new version of SNaQ.jl (version 1.1) improves average runtimes by up to 499% on average with no change in function parameters or method accuracy.

AVAILABILITY AND IMPLEMENTATION: SNaQ.jl is a new open source Julia package available at https://github.com/JuliaPhylo/SNaQ.jl.

PMID:42114082 | DOI:10.1093/bioinformatics/btag289

Neurol Clin Pract. 2026 Jun;16(3):e200612. doi: 10.1212/CPJ.0000000000200612. Epub 2026 Apr 6.

ABSTRACT

OBJECTIVES: The aim of this study was to evaluate whether the treatment effects of tolebrutinib on confirmed disability worsening (CDW) diverge from its effects on relapse prevention compared with other disease-modifying therapies (DMTs) for relapsing multiple sclerosis (MS).

METHODS: We extracted published effect estimates for annualized relapse rate (ARR) and CDW confirmed at 24 weeks from all phase 3 trials with teriflunomide as the active comparator: ASCLEPIOS (ofatumumab), OPTIMUM (ponesimod), ULTIMATE (ublituximab), EVOLUTION (evobrutinib), and GEMINI (tolebrutinib). When duplicate trials were available, pooled estimates were derived. Log-transformed estimates were used in a weighted linear regression of CDW vs ARR, with bubble size reflecting statistical precision. Tolebrutinib was excluded from the regression fit but displayed for comparison.

RESULTS: Across 4 DMTs other than tolebrutinib, a strong linear association was observed between treatment effects on ARR and CDW (R² = 0.997), indicating that disability benefit was generally proportional to relapse reduction. By contrast, tolebrutinib deviated from this relationship, with a hazard ratio for CDW of 0.71 (95% CI 0.53-0.95) despite a relapse rate ratio of 1.03 (95% CI 0.85-1.25).

DISCUSSION: Tolebrutinib was the only therapy to show a benefit on CDW without a measurable effect on relapses, highlighting a dissociation between disability worsening and relapse suppression not observed with other DMTs.

PMID:42114075 | DOI:10.1212/CPJ.0000000000200612

Neurol Clin Pract. 2026 Jun;16(3):e200618. doi: 10.1212/CPJ.0000000000200618. Epub 2026 May 7.

ABSTRACT

BACKGROUND AND OBJECTIVES: Military sexual trauma (MST) is increasingly recognized in US veterans. MST is associated with psychiatric disease, substance abuse, and pain conditions, including headache. Little is known about the relationship between MST and specific headache disorders.

METHODS: This retrospective cross-sectional study analyzed administrative data from the Women Veterans Cohort Study, a sample of post-9/11 US veterans enrolled for Veterans Health Administration care. A positive MST screen in the electronic medical record defined exposure. We extracted demographic and clinical data from administrative coding for migraine and relevant confounders, comparing between subgroups with χ² tests. Health care utilization variables included designated sites of care and prescribed acute and preventive treatments and were evaluated with multivariable logit, negative binomial (nb), and zero-inflated nb models.

RESULTS: Of 846,435 veterans screened for MST, 4.4% of veterans had a positive screen, whereas 9.5% had migraines. Veterans with migraine and a positive MST screen (21.7%) were more often non-White (45.3% vs 38.6%, p < 0.001), and 33 or less years old (55% vs 53%, p < 0.001) than veterans with migraine and a negative MST screen (9.0%). Adjusting for sex, the odds of migraine were greater for veterans with a positive MST screen (OR 1.62, 95% CI 1.57-1.67). Veterans with migraine and a positive MST screen were no more likely to receive triptan medications than veterans with migraine (46.2% vs 45.7%, p = 0.47) although were more likely to be prescribed opioids (36.1% vs 33.4%, p ≤ 0.001), compared with those with migraine and a negative MST screen. After controlling for sex, comorbidities (including chronic pain conditions), treatments, and other health care use, health care utilization was increased among migraine veterans with a positive MST screen, compared with migraine veterans without a positive MST screen for primary care (IRR 1.06, 95% CI 1.04-1.08, p < 0.001) and emergency department care (IRR 1.14, (95% CI 1.07-1.22), whereas neurology visits were not increased (IRR 0.97, 95% CI 0.92-1.02).

DISCUSSION: Veterans with a positive MST screen constitute a vulnerable population more likely to have migraine, take opioid medications, and use emergency departments for migraine care.

PMID:42114073 | DOI:10.1212/CPJ.0000000000200618

Neurol Clin Pract. 2026 Jun;16(3):e200615. doi: 10.1212/CPJ.0000000000200615. Epub 2026 Apr 28.

ABSTRACT

BACKGROUND AND OBJECTIVES: Refractory migraine (RM) is associated with substantial disability, yet its clinical and health care utilization patterns remain poorly characterized in large, real-world populations. Understanding how preventive treatment progression relates to health care use and patient characteristics may inform earlier identification and care strategies. We sought to evaluate demographic, clinical, and health care utilization patterns in chronic migraine according to the number of preventive medication trials using a large cross-institutional electronic health record (EHR) database.

METHODS: We conducted a retrospective observational study using the Epic Cosmos Cross-institutional EHR Database from January 1, 2016, to December 31, 2024. Adults with chronic migraine (International Classification of Diseases, Tenth Edition code G43.7) were included. Preventive medication trials were categorized into 5 classes: antihypertensives, antidepressants, antiseizure agents, calcitonin gene-related peptide-targeted therapies, and onabotulinumtoxinA. We evaluated demographics, comorbidities, and health care utilization metrics, including inpatient or outpatient dihydroergotamine (DHE) infusions, emergency department (ED) visits for headache, MRI brain orders, and patient EHR portal recency (MyChart). Marginal changes were defined as the percentage point change in outcomes between medication classes. Chi-squared tests and analysis of variance were used with significance set at p < 0.05.

RESULTS: A total of 1,572,698 patients were identified by our search criteria; 21.2% were prescribed no preventive medications and 2.5% were prescribed all 5 classes, meeting the study’s definition of RM. Health care utilization increased significantly with each additional medication class. The greatest marginal increases occurred between zero to 1 classes for MyChart access (43-day decrease), 1 to 2 classes for ED visits (+9.2%), and 4 to 5 classes for DHE administration (+6.1%) and MRI brain orders (+4.9%). Patients prescribed more preventive classes were older, a higher percentage female sex, White race, with public insurance, residence in the Northeast United States, and live in less socially vulnerable areas. Comorbidity burden increased progressively, with 94.6% of refractory patients having at least 1 comorbidity, most commonly anxiety (78.1%), depression (71.5%), hypertension (56.0%), and asthma (36.3%). All differences were statistically significant (p < 0.001).

DISCUSSION: Higher health care utilization, greater comorbidity burden, and distinct geographic patterns are observed with increasing numbers of preventive medication trials in chronic migraine. These findings highlight the complexity of RM and underscore the need for earlier identification and more equitable access to comprehensive migraine care.

PMID:42114071 | DOI:10.1212/CPJ.0000000000200615

JMIR Hum Factors. 2026 May 11;13:e67944. doi: 10.2196/67944.

ABSTRACT

BACKGROUND: There are around 20,000 mental health apps available in app stores. The Organisation for the Review of Care and Health Apps (ORCHA), a United Kingdom digital health compliance company, has assessed a number of digital mental health apps with regard to their quality, professional and clinical assurance, data privacy, and user experience. This study analyzes the data that were collected by ORCHA when they assessed mental health apps.

OBJECTIVE: This study aimed to examine the characteristics of mental health apps regarding their quality, target users, features, underpinning evidence, and data privacy.

METHODS: A dataset comprising ORCHA Baseline Review assessments of over 2000 digital health apps, including 436 mental health apps, was used. This study uses exploratory data analysis to gain insight into the quality and characteristics of mental health apps. Methods such as descriptive and inferential statistics, k-modes clustering, and association rule mining were used to explore the quality of mental health apps as well as reveal insights into the different cost types, target users, app features, data types, and evidence of app content.

RESULTS: Information provision, data capture, and data sharing were the most common features within the 436 mental health apps. The examined apps primarily targeted the following groups: adults (n=229, 52.5%), everyone (n=184, 42.2%), and teens (n=135, 31%). The cost of apps has not been linked to the quality of mental health apps, although paid apps or apps with in-app purchases may include additional services. Indicated user acceptance or benefit is the most common type of evidence provided by these mental health apps. A total of 241 (55.3%) apps included a qualified professional in app development, and 251 (57.6%) apps provided evidence within the app that the developer validated any guidance with relevant reliable information sources or references. Usage data and email were the most commonly collected data types. Association rule mining showed that email, IP address, name, and usage data are often co-collected by the same apps. K-modes cluster analysis showed that mental health apps can be categorized into 2 clusters, where one cluster of apps (n=182, 41.7%) collected more data than apps in the other cluster.

CONCLUSIONS: Mental health apps are commonly targeted for everyone to use, but many apps are targeted toward teens or adults. Our study suggests that many publicly available mental health apps did not take the precautions (such as the involvement of appropriate health professionals, literature references, or conducting tests) to ensure that their content is valid and research based. Greater effort on behalf of mental health app developers is needed to ensure that the public is provided with high-quality apps. Moreover, our study indicates that the mental health apps that collect more data tend to score better on the ORCHA Baseline Review assessment.

PMID:42114062 | DOI:10.2196/67944