Tag: nevin manimala

Proc Natl Acad Sci U S A. 2026 Apr 21;123(16):e2532451123. doi: 10.1073/pnas.2532451123. Epub 2026 Apr 13.

ABSTRACT

Tree crown damage from disturbance events strongly influences forest demography, yet its effect on stem growth remains poorly quantified, with both positive and negative impacts reported. Hurricanes provide a powerful natural experiment to examine these dynamics, generating a broad range of structural damage across individuals and forest stands. Here we assess how crown damage from Hurricane María (2017) affected poststorm stem growth in a wet subtropical forest in Puerto Rico by combining airborne LiDAR with field measurements for 1,082 trees. Unlike previous studies, paired pre- and posthurricane LiDAR assessment enabled us to quantify crown damage as a continuous, objective variable across the canopy. Using a causal inference framework, we separated individual- from neighborhood-level effects, defined as the damage that occurred within a 5 m radius of each tree. Repeated stem growth censuses allowed direct comparison of individual growth responses before and after the hurricane. Across the community, posthurricane stem growth rates were similar to prehurricane values. Larger and more heavily damaged trees exhibited moderately reduced growth, while neighborhood crown damage and mortality had no detectable effect. However, these damage effects were smaller than the influence of prehurricane growth rates, indicating that prehurricane individual vigor outweighed biomass loss and competitive release in shaping growth responses. These findings highlight the resilience of surviving trees in sustaining carbon uptake after a severe disturbance and challenge the assumption of strong postdamage growth suppression that is embedded in dynamic vegetation models.

PMID:41973916 | DOI:10.1073/pnas.2532451123

JMIR Nurs. 2026 Apr 13;9:e85385. doi: 10.2196/85385.

ABSTRACT

BACKGROUND: Nursing informatics is essential for digital health transformation; however, the technology acceptance of undergraduate nursing students in Saudi Arabia remains underexplored.

OBJECTIVE: This study examined factors influencing nursing students’ intention to use informatics technologies using the technology acceptance model.

METHODS: A cross-sectional survey was conducted with 132 undergraduate nursing students. Data were analyzed using descriptive, correlational, and hierarchical regression analyses.

RESULTS: Perceived usefulness (mean 3.68, SD 1.22) and perceived ease of use (mean 3.64, SD 1.32) were the strongest predictors of acceptance, together explaining 87% of the variance (R²=0.87; β=0.323 for usefulness, P<.001; β=0.195 for ease of use, P=.032). Only 25.8% (n=34) of the students often used electronic health records, while 31.8% (n=42) had no electronic health record experience, indicating a clear gap in practical informatics exposure.

CONCLUSIONS: Nursing students’ acceptance of informatics is primarily driven by its perceived usefulness and perceived ease of use. These findings highlight the urgent need to integrate practical, user-centered informatics training and clinical simulation into undergraduate nursing curricula to better prepare students for technology-based practice.

PMID:41973911 | DOI:10.2196/85385

J Proteome Res. 2026 Apr 13. doi: 10.1021/acs.jproteome.5c01112. Online ahead of print.

ABSTRACT

Congenital hypothyroidism (CH) is a genetic endocrine disorder that can cause developmental delays if it is untreated. In this study, NMR-based metabolomics was employed to analyze serum samples from CH children and healthy controls across different age groups. Multivariate statistical analysis screened for 17, 16, 33, and 21 differential metabolites in the respective age groups and identified seven common metabolites, including lysine, 1-methylhistidine, glycerophosphocholine, phosphocholine, β-glucose, lipids, and creatine. The results indicated that CH children experienced metabolic disturbances in multiple pathways, particularly glycerophospholipid metabolism and glycine, serine, and threonine metabolism. Following recursive feature elimination (RFE) for feature selection, the top five core metabolites were selected to construct an optimized artificial neural network (ANN) model for CH diagnosis, achieving a prediction accuracy of 89.4%. These findings suggest that the identified metabolites can be used as potential diagnostic biomarkers for CH in children. This may help improve the early diagnosis accuracy of CH, serve as a rapid screening tool for newborns, and provide an auxiliary diagnostic method for suspected CH cases to facilitate early clinical intervention.

PMID:41973905 | DOI:10.1021/acs.jproteome.5c01112

JMIR Cancer. 2026 Apr 13;12:e81054. doi: 10.2196/81054.

ABSTRACT

BACKGROUND: Residents in persistent poverty areas experience higher cancer mortality due to social determinants of health that negatively affect multiple factors, including health behaviors.

OBJECTIVE: This study aimed to characterize demographic, clinical, and social determinant of health factors among survivors of cancer in persistent poverty areas using electronic health record (EHR) data-including an embedded social risk screener and natural language processing (NLP) of social work notes-to inform community-engaged adaptation of lifestyle interventions.

METHODS: EHR data from a large multispecialty group practice were extracted for patients with cancer residing in zip codes inclusive of persistent poverty areas targeted for a health behavior intervention and receiving care between January 2018 and November 2023. Self-reported social determinant of health data were obtained using the Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE) and through NLP of social histories from a social work visit.

RESULTS: We identified 2672 unique patients with cancer, of whom 578 (21.6%) had PRAPARE data and 1597 (59.8%) had social history data available for analysis. The most common cancers among survivors (n=1420, 53.1% female; n=1299, 48.6% Black individuals; mean age 65.2, SD 13.7 years) included breast (n=536, 20.1%), prostate (n=400, 15%), and lymphoid or hematopoietic (n=323, 12.1%) cancer. Among survivors in persistent poverty areas (n=509, 19%; all with a high Social Vulnerability Index), 34.6% (176/509) were single, 55.4% (282/509) had Medicare coverage (with only 73/509, 14.3% having private insurance), 36.5% (186/509) had obesity, 63.9% (325/509) had hypertension, and 31.2% (159/509) had diabetes. Of survivors in persistent poverty areas with PRAPARE data, 15.8% (19/120) lacked transportation, 4.2% (5/120) lived with housing insecurity, and 6.7% (8/120) felt unsafe where they lived.

CONCLUSIONS: Innovative EHR and NLP approaches identified several socioeconomic and safety-related challenges along with opportunities for health behavior interventions to leverage Medicare coverage and target multiple comorbidities when adapting interventions for survivors of cancer living in persistent poverty areas.

PMID:41973902 | DOI:10.2196/81054

JACC Clin Electrophysiol. 2026 Mar 12:S2405-500X(26)00148-9. doi: 10.1016/j.jacep.2026.02.016. Online ahead of print.

ABSTRACT

BACKGROUND: The His-Alternative I (A Randomized Trial of His Pacing Versus Biventricular Pacing in Symptomatic Heart Failure Patients With Left Bundle Branch Block) trial was the first randomized European trial to compare cardiac resynchronization therapy (CRT) delivered by His bundle pacing (His-CRT) vs CRT delivered by conventional biventricular pacing (BiV-CRT).

OBJECTIVES: The goal of this study was to compare long-term lead performance, survival, and echocardiographic response between His-CRT and BiV-CRT.

METHODS: A total of 50 patients with symptomatic heart failure (HF), left ventricular ejection fraction ≤35%, and left bundle branch block were randomized 1:1 to undergo His-CRT or BiV-CRT. Following crossover at implantation, 19 patients received His-CRT and 31 received BiV-CRT. The primary analyses were conducted by these groups, with 5 years of follow-up. Outcomes included the occurrence of reinterventions, an endpoint of all-cause mortality or hospitalization for HF, and echocardiographic response (≥15% reduction in left ventricular end-systolic volume compared with baseline).

RESULTS: The median follow-up was 5.3 years (Q1-Q3: 4.6-5.7 years). More lead revisions (37% vs 3%; P = 0.003) and generator replacements (47% vs 10%; P = 0.005) occurred in the His-CRT group compared with the BiV-CRT group. However, no statistically significant differences in reinterventions and threshold development over time were observed between the His-CRT patients with implantation thresholds ≤2.5 V/1 millisecond and the BiV-CRT group. The risk of all-cause mortality or hospitalization for HF was similar between groups (HR: 0.32; 95% CI: 0.07-1.49; P = 0.147), and echocardiographic response was comparable between the 2 groups (89% in His-CRT and 90% in BiV-CRT; P = 1.0).

CONCLUSIONS: No statistically significant difference in long-term performance was detected between His-CRT with low implantation thresholds and BiV-CRT, and echocardiographic response was comparable.

PMID:41973899 | DOI:10.1016/j.jacep.2026.02.016

Child Dev. 2026 Apr 13:aacag031. doi: 10.1093/chidev/aacag031. Online ahead of print.

ABSTRACT

Compared to other developmental processes, such as cognitive control, there is relatively little consensus on the developmental trajectory of statistical learning (the ability to implicitly extract environmental regularities). The literature is further complicated as statistical learning may comprise distinct subtypes, differing in the regularities being tracked. Three statistical learning abilities and cognitive control were assessed in one hundred eighty-seven 5-12-year-olds (42.8% female, 84.6% White) at a university laboratory between March 2023 and August 2024. The ability to extract cue-based and nonadjacent statistics was age invariant, whereas adjacent dependency extraction yielded a greater benefit in younger than in older children. Despite research suggesting immature top-down guidance facilitates statistical learning, there was only weak evidence of an association with cognitive control, and the direction of these associations varied between “online” and “offline” measures of children’s sensitivity to adjacent dependencies. Importantly, the different subtypes were uncorrelated, supporting the notion that statistical learning is multifaceted.

PMID:41973827 | DOI:10.1093/chidev/aacag031

JMIR Serious Games. 2026 Apr 13;14:e80684. doi: 10.2196/80684.

ABSTRACT

Chronic health conditions impose substantial financial and operational burdens on the public health sector and insurance providers in the United Kingdom. While gamification demonstrates the potential for enhancing health behavior, a structured analysis linking to established behavioral frameworks is missing. We provide a viewpoint on whether, as health and life insurers transition from traditional risk assessment toward proactive risk reduction strategies, gamification offers an innovative mechanism to strengthen their prevention initiatives and insurer-insured relationships. We examine how gamification aligns with key theoretical models, including the Behavior Change Wheel and Behavior Change Techniques, and how gamification elements can be mapped onto them. This enables combining multiple Behavior Change Techniques into effective interventions, which provide engaging user experiences and promote intrinsic motivation. We distinguish gamification from mere incentivization, highlighting its potential for sustained health outcomes. We also explore the ethical and practical considerations of gamification in the insurance sector. We highlight the need for a robust ethical framework that preserves an individual’s ability to make free and informed decisions, while ensuring inclusivity and absence of discrimination based on personal characteristics that may affect their capacity to engage in healthy behaviors. Similarly, we highlight how privacy, transparency, and accountability need to be prioritized in the governance structure of gamification programs in the sector. Our analysis emphasizes that gamification has the potential to represent the new panacea for the insurance sector, if effective gamified interventions incorporate inclusive design principles, theoretical grounding, ethical accountability, and continuous refinement to ensure alignment with long-term public and individual health objectives. This viewpoint is the first to map gamification and behavioral change frameworks into a unified model for insurer-led health behavior interventions and encourage greater investment in gamified wellness products and the use of theory-driven behavioral science in insurance-led digital health tools.

PMID:41973825 | DOI:10.2196/80684

J Fam Psychol. 2026 Apr 13. doi: 10.1037/fam0001467. Online ahead of print.

ABSTRACT

Statistical adjustments (i.e., partialing) for between-partner correlations on the same variable and for correlations among multiple predictors within individuals are common in relationship research. Although useful, partialing can alter the construct validity or meaning of measured variables in ways that are typically not considered. In this study of 300 middle-aged and older couples, unadjusted, partner-partialed, partialed warmth and hostility component, and common fate model scores for observer-rated behavior during marital conflict discussions were compared using interpersonal circumplex-based spouse ratings of targets’ behavior during those discussions, as well as self-reports of marital quality (i.e., overall marital adjustment, support from spouse, conflict). Unadjusted scores for observer-rated affiliation-and its two components, warmth, and hostility-had expected associations with interpersonal circumplex-based spouse ratings and self-reports of marital quality. Compared to unadjusted scores, partner-partialing, analogous to the actor-partner interdependence model, resulted in significantly weaker associations of behavioral scores with the expected interpersonal content of spouse ratings, and weaker associations with reported marital quality. Partialing of warmth and hostility within individuals also resulted in weaker associations with spouse ratings and marital quality, and some shifts in the theme of spouse ratings. In contrast, common fate model scores had expected associations with these criteria that equaled or exceeded the magnitude for unadjusted scores. Thus, common forms of partialing in relationship research can weaken the construct validity of behavioral observation variables, and common fate model scores represent a viable alternative in some instances. Implications for the design, reporting, and evaluation of couple research are discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

PMID:41973814 | DOI:10.1037/fam0001467

Dev Psychol. 2026 Apr 13. doi: 10.1037/dev0002185. Online ahead of print.

ABSTRACT

The present study reports the natural statistics of everyday motor experiences, measured throughout the day using wearable inertial sensors. Using a large data set of infants’ real-time upright, sitting, prone, supine, and held experiences, we investigated how age and motor skill relate to the frequency and bout structure of body position. Our analyses replicated past survey and observational work by showing that older infants (11-14 months) spend more time sitting and upright compared with younger infants (4-7 months) and that the emergence of sitting and walking skills may contribute to these age differences. Furthermore, our analyses were novel in revealing that a larger share of younger infants’ bouts were longer-lasting several minutes and even over an hour. In contrast, older infants had a greater share of shorter bouts less than 1 min long, suggesting they experience a greater mix of positions. Within older infants, bout duration distributions also varied according to walking skill. We discuss the importance of understanding the natural statistics of motor experiences at different timescales for characterizing infants’ opportunities for motor learning and perceptual-motor exploration in daily life. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

PMID:41973804 | DOI:10.1037/dev0002185

J Health Organ Manag. 2026 Apr 14:1-15. doi: 10.1108/JHOM-12-2025-0889. Online ahead of print.

ABSTRACT

PURPOSE: The increasing use of artificial intelligence (AI) and large language models such as ChatGPT in healthcare has highlighted the need to understand physicians’ perceptions and readiness for clinical adoption.

DESIGN/METHODOLOGY/APPROACH: This cross-sectional study included 328 physicians. Data were collected through face-to-face surveys incorporating sociodemographic items, AI/ChatGPT knowledge and interaction questions, and two validated instruments (MAIRS and TAME-ChatGPT). Descriptive statistics, correlations, and simple linear regression were used to identify predictors of ChatGPT acceptance.

FINDINGS: Physicians demonstrated moderate awareness of AI, while nearly half lacked knowledge about ChatGPT. Among AI readiness dimensions, MAIRS-Ability was the strongest positive predictor of ChatGPT acceptance (β = 0.315, p < 0.001). Vision (β = 0.180, p = 0.001) and Ethics (β = 0.143, p = 0.010) also showed significant positive effects, whereas Cognition was not significant. Among sociodemographic variables, duration of medical practice (β = -0.295, p < 0.001) and marital status (β = -0.117, p = 0.035) negatively predicted ChatGPT acceptance. Knowledge about AI use in healthcare demonstrated the strongest positive association overall (β = 0.396, p < 0.001).

ORIGINALITY/VALUE: Physicians exhibit cautious but growing interest in ChatGPT. AI competence, ethical sensitivity, and demographic factors significantly shape acceptance. Structured AI training and clear ethical guidelines are essential to support safe and effective integration of generative AI tools into clinical practice.

PMID:41973799 | DOI:10.1108/JHOM-12-2025-0889