Tag: pubmed

Gerontologist. 2026 May 21:gnag111. doi: 10.1093/geront/gnag111. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: We tested the feasibility and preliminary effects of the Mindfulness Self-Compassionate Care (MASC) program for dementia caregivers’ stress triggered by the care recipients’ neuropsychiatric symptoms.

RESEARCH DESIGN AND METHODS: Single-blind Stage 1B pilot randomized (2:1) controlled trial compared 6 weeks of MASC (n = 45) with a time and dose-matched education control (n = 23; Healthy Living for Caregivers [HLC]). T-tests examined within and between-group differences.

RESULTS: Most feasibility benchmarks were met. Mechanistic targets of mindfulness (p < .001; d = .58), self-compassion (p =.04; d = .32), self-efficacy (p < .001, d=.58), and distress from neuropsychiatric symptoms (p=.04, d=.33) showed small to medium pre-post improvements in the MASC group. Mechanistic validity was established through significant correlations between change in stress and change in mindfulness (r =-.58, p <.001), compassion (r =-.43, p =.003), self-compassion (r =-.77, p = <.000), self-efficacy (r =-.50, p =.001), and distress from dementia related neuropsychiatric symptoms (r =.33, p =.03). Pre-post improvements in MASC were not statistically significantly different compared with HLC for stress (t (64) =-1.07, p= .29) or other outcomes.

DISCUSSION AND IMPLICATIONS: The current study demonstrated feasibility and mechanistic target engagement and validity for MASC. As expected in an underpowered feasibility trial, clinical outcomes did not improve more than control, but trends favored MASC. These findings support a fully powered Stage 2 trial to test the efficacy of MASC compared with HLC.

PMID:42166728 | DOI:10.1093/geront/gnag111

Am J Respir Crit Care Med. 2026 May 21:aamag253. doi: 10.1093/ajrccm/aamag253. Online ahead of print.

NO ABSTRACT

PMID:42166725 | DOI:10.1093/ajrccm/aamag253

Genetics. 2026 May 21:iyag132. doi: 10.1093/genetics/iyag132. Online ahead of print.

ABSTRACT

In many bacterial species, strong genetic structure is present, where individuals are clustered into genetically distinct groupings within the species. However, high rates of homologous recombination have also been observed in many of these species, high enough that simple models of evolution predict that such genetic structure should be eliminated. One proposed resolution to this contradiction is the presence of recurrent bursts of reproduction caused by rapid adaptation or microepidemics. We investigate this hypothesis using coalescent simulations of the simplest bursty reproduction model. Our simulations show that bursts of reproduction can indeed create genetic structure even when recombination is so frequent that all structure would be eliminated in the absence of bursts. This genetic structure is only possible when there is a burst of reproduction that is sufficiently large and recent in the population’s history. We describe the mechanism by which a burst creates genetic structure and analyze its distinctive effect on the patterns of diversity, focusing on the distribution of pairwise genetic distances and its relationship to the fraction of identical blocks along the genome. Interestingly, genetic structure from bursts of reproduction can appear among pairs of samples which do not share any genetic material by clonal descent, a feature which cannot be observed in populations whose structure is just a consequence of limited recombination. However, we find that for other statistics beyond the distribution of pairwise distances, the simplest model of bursty reproduction cannot entirely reproduce the distributions observed in nature.

PMID:42166720 | DOI:10.1093/genetics/iyag132

JMIR Med Educ. 2026 May 21;12:e85892. doi: 10.2196/85892.

ABSTRACT

BACKGROUND: Mobile learning (mLearning) is widely used in medical education. Previous research has focused on technology acceptance and intervention effectiveness, but rarely on their integration. Using realist evaluation, this study investigated the conditions under which mLearning is adopted and associated with learning-related outcomes in an authentic curricular setting.

OBJECTIVE: This study aimed to examine how learner context and engagement patterns shape mLearning use and outcomes, while secondarily contextualizing its association with examination performance.

METHODS: A quasi-experimental study was conducted among fifth-semester undergraduate medical students at a German medical school across 2 consecutive summer semesters (2023 and 2024). Students were offered a voluntary, app-based mLearning course in microbiology, delivered via the eSquirrel platform. The course comprised interactive tasks, incorporating elements of gamification and spaced-repetition features. Data sources included nonreactive in-app usage logs, baseline academic performance data, demographic information, and postsemester questionnaire responses. Usage profiles were derived using cluster analysis. Context-mechanism-outcome patterns were explored by relating app usage status to academic performance and survey responses.

RESULTS: Of the 245 eligible students, 220 (89.8%) participated in the study; 110 (50%) used the app. In 2024, app users (n=64, 58%) initially appeared to outperform nonusers (n=46, 42%) in the oral microbiology examination (mean grade 2.3, SD 1.1 vs 2.8, SD 1.3; t63,0=1.90; 1-sided P=.03). After adjustment, these differences were largely explained by baseline academic performance, with only limited evidence of an independent intervention effect. Cluster analysis of app users identified 3 engagement profiles: continuous low-intensity use (n=60, 54%), increased use before the examination (n=31, 28%), and use at the beginning of the semester (n=19, 17%). Cluster 2 reported the greatest enjoyment, satisfaction, perceived learning gains, and examination performance in microbiology.

CONCLUSIONS: Nonreactive in-app usage data provided valuable insights into student engagement. The effectiveness of mLearning was not universal. Examination-oriented use, associated with more strategic and self-regulated study behavior, was linked to more favorable learning outcomes. Future research needs to address equity concerns, as higher-performing students tended to benefit most, as well as explore adaptive, context-sensitive approaches to support diverse learners.

PMID:42166712 | DOI:10.2196/85892

Eur Heart J. 2026 May 21:ehag336. doi: 10.1093/eurheartj/ehag336. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Myocardial infarction (MI) stands as a prominent manifestation of cardiovascular events. Most of the regenerative effects of stem cell therapies for MI are paracrine. A clinically translatable strategy that harnesses regenerative secretome while enabling minimally invasive delivery is needed. This study evaluated Regenerative Encapsulated Secretome as Cardiac Acellular Therapy (RESCAT), a formulation composed of cardiac stromal cell-derived secretome encapsulated in microparticles and embedded within a hyaluronic acid hydrogel, delivered via intrapericardial injection in a porcine model of MI.

METHODS: MI was induced using minimally invasive techniques. RESCAT was administered through clinically feasible intrapericardial delivery. Cardiac structure and function were assessed longitudinally in vivo. After the endpoint, infarct size and cardiomyocyte cell-cycle activity were assessed with histology. Single-nucleus RNA sequencing was performed to characterize cardiomyocyte transcriptional states and identify molecular pathways associated with therapeutic response.

RESULTS: RESCAT-treated pigs showed improved cardiac function and reduced infarct size compared with control groups. Enhanced cardiomyocyte cell-cycle activity and alterations in cardiomyocyte functional state were also observed. Single-nucleus transcriptomic analysis identified an FN1-expressing cardiomyocyte subtype linked to the activation of the PI3K-Akt pathway, which plays a role in cell survival and growth.

CONCLUSIONS: Intrapericardial delivery of RESCAT promotes functional and structural cardiac recovery in a clinically relevant porcine MI model. These findings support a minimally invasive, off-the-shelf acellular therapeutic strategy that enhances endogenous repair mechanisms and provides a foundation for translational development in ischaemic heart disease.

PMID:42166700 | DOI:10.1093/eurheartj/ehag336

Biometrics. 2026 Apr 9;82(2):ujag086. doi: 10.1093/biomtc/ujag086.

ABSTRACT

Survivorship (or selection) bias arises within statistical analyses where the observed data are subject to some underlying selection process prior to entry into the sampled data. For example, within capture-recapture studies, a primary selection mechanism is the survival until initial capture time. The common Cormack-Jolly-Seber model conditions on the first time an individual is observed, leading to potential survivorship bias. However, while the issue of survivorship bias has been well studied in many fields, there has been little exploration within the capture-recapture framework. In particular, we focus on individual (continuous) random effect Cormack-Jolly-Seber models, where it is assumed that individuals have different survival probabilities, specified to be from some common underlying distribution. We discuss the implications of the survivorship bias within the data collection process, and describe a novel modeling approach that accounts for the survivorship bias within an ecologically sensible manner. Using simulated data, we demonstrate the significant impact of ignoring the survivorship bias present in the data. We fit the corrected model to a guillemot data set and demonstrate that even with relatively mild selection bias, the individual heterogeneity variability is substantially underestimated when ignoring this survivorship bias.

PMID:42166193 | DOI:10.1093/biomtc/ujag086

Biometrics. 2026 Apr 9;82(2):ujag091. doi: 10.1093/biomtc/ujag091.

ABSTRACT

This paper considers survival analysis of large-scale data distributed across heterogeneous network nodes. We propose a novel method, the Distributed Spanning-Tree-Based Fused Lasso (DSTFL), for Cox regression in distributed settings. By employing a minimum spanning tree-based fusion framework, the method can reduce computational and communication burdens, facilitating scalability to large datasets. Additionally, we develop an efficient alternating direction method of multipliers algorithm for the optimization with privacy protection. We establish large-sample properties and clustering consistency for the proposed estimator. Simulation studies demonstrate that DSTFL improves computational efficiency, clustering performance, and robustness compared to existing methods. An application to Surveillance, Epidemiology, and End Results gastric cancer data illustrates how DSTFL identifies geographically structured survival heterogeneity and heterogeneous covariate effects across regions.

PMID:42166192 | DOI:10.1093/biomtc/ujag091

Biometrics. 2026 Apr 9;82(2):ujag089. doi: 10.1093/biomtc/ujag089.

ABSTRACT

Mixed membership models are frequently utilized to capture complex individual heterogeneity in multivariate and longitudinal data. A key aspect of mixed membership modeling involves determining the number of extreme profiles (classes), a task traditionally managed through inefficient criterion-based methods. This task is particularly challenging when the predictors within the models are latent and derived from multiple observed variables using exploratory factor analysis. In this paper, we consider an innovative mixed membership latent variable model, which consists of an exploratory factor model to identify latent factors and a mixed membership model with latent predictors. We develop an efficient approach that integrates parameter estimation and model selection for the number of factors, extreme profiles, and the structure of the factor loading matrix. Our approach comprises a modified stochastic search item selection algorithm to automatically determine the number of latent factors and their associated manifest variables and a Bayesian penalized method to select the number of extreme profiles. We validate our methodology through extensive simulation studies, demonstrating its accuracy and efficiency in both parameter estimation and model selection. Applying this method to data from the Parkinson’s Progression Markers Initiative, we identify clinically important latent traits and distinct disease profiles. The results underscore our model’s enhanced ability to depict the intricate individual heterogeneity present in Parkinson’s disease patients.

PMID:42166191 | DOI:10.1093/biomtc/ujag089

Biometrics. 2026 Apr 9;82(2):ujag080. doi: 10.1093/biomtc/ujag080.

ABSTRACT

Longitudinal biomarker data and health outcomes are routinely collected in many studies to assess how biomarker trajectories predict health outcomes. Existing methods primarily focus on mean biomarker profiles, treating variability as a nuisance. However, excess variability may indicate system dysregulations that may be associated with poor outcomes. In this paper, we address the long-standing problem of using variability information of multiple longitudinal biomarkers in time-to-event analyses by formulating and studying a Bayesian joint model. We first model multiple longitudinal biomarkers, some of which are subject to limit-of-detection censoring. We then model the survival times by incorporating random effects and variances from the longitudinal component as predictors through threshold regression that admits nonproportional hazards. We demonstrate the operating characteristics of the proposed joint model through simulations and apply it to data from the Study of Women’s Health Across the Nation to investigate the impact of the mean and variability of follicle-stimulating hormone and anti-Müllerian hormone on age at the final menstrual period.

PMID:42166190 | DOI:10.1093/biomtc/ujag080

Biometrics. 2026 Apr 9;82(2):ujag078. doi: 10.1093/biomtc/ujag078.

ABSTRACT

A clustered adaptive intervention (cAI) is a prespecified sequence of decision rules that guides practitioners on how best-and based on which measures-to tailor cluster-level intervention to improve outcomes at the level of individuals within the clusters. A clustered sequential multiple assignment randomized trial (cSMART) is a type of trial that is used to inform the empirical development of a cAI. A common analytic goal in a cSMART focuses on assessing causal effect moderation by candidate tailoring variables. We introduce a clustered Q-learning framework with the M-out-of-N cluster bootstrap using data from a cSMART to evaluate whether a set of candidate tailoring variables may be useful in defining an optimal cAI. This approach could construct confidence intervals (CIs) with near-nominal coverage to assess parameters indexing the causal effect moderation function. Specifically, it allows reliable inferences concerning the utility of candidate tailoring variables in constructing a cAI that maximizes a mean end-of-study outcome even when “non-regularity,” a well-known challenge, exists. Simulations demonstrate the numerical performance of the proposed method across varying non-regularity conditions and investigate the impact of varying numbers of clusters and intra-cluster correlation coefficients on CI coverage. Methods are applied on the ADEPT dataset to inform the construction of a clinic-level cAI for improving evidence-based practice in treating mood disorders.

PMID:42166189 | DOI:10.1093/biomtc/ujag078