Tag: pubmed

Nurs Open. 2026 May;13(5):e70535. doi: 10.1002/nop2.70535.

ABSTRACT

AIM: This study aimed to examine the supportive care needs of family caregivers of elderly patients with dementia and diabetes mellitus, and identify the associated factors to provide a scientific basis for the development of effective supportive care interventions.

DESIGN: Cross-sectional study.

METHODS: Using convenience sampling, we recruited 108 family caregivers of elderly patients with dementia and diabetes mellitus from five neighbourhood committees and 10 natural villages in a community in Xiamen, China. Data on caregivers’ demographics and supportive care needs were collected via questionnaires.

RESULTS: Caregivers reported high levels of need, with physiological, emotional and safety needs rated most highly. The Caregiver Burden Inventory score was a significant positive predictor of physiological, informational, safety, emotional and spiritual needs, but not of social needs. Physiological needs were associated with the caregiver’s occupation and economic status; informational needs with sex and education level; safety needs with sex and occupation; spiritual needs with age and economy; and emotional and social needs with cohabitation status, marital status and relationship to the patient.

CONCLUSIONS: Caregiver burden is a key factor associated with the supportive care needs of family caregivers of older adults with dementia and diabetes mellitus. Future interventions should consider both caregiver burden and individual characteristics to provide targeted, multi-level support.

PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

IMPLICATIONS: Assessment of caregiver burden should be integrated into community health practice to guide support strategies for caregivers of patients with chronic conditions.

IMPACT: The findings will guide community health professionals and policymakers in designing support programmes for caregivers managing complex chronic conditions.

PMID:42048613 | DOI:10.1002/nop2.70535

J Org Chem. 2026 Apr 28. doi: 10.1021/acs.joc.5c03133. Online ahead of print.

ABSTRACT

A DFT mechanistic study of C-H borylation of 3-fluorobenzotrifluoride catalyzed by Ir complexes ligated by 4,4′-bis(trifluoromethyl)-2,2′-bipyridine (btfbpy), 4,4′-ditert-butyl-2,2′-bipyridine (dtbpy), and 3,4,7,8-tetramethyl-1,10-phenanthroline (tmphen), respectively, demonstrates that the C-H bonds distal to the steric -CF₃ group exhibit comparable, albeit slightly lower, rate and regioselectivity-determining barriers compared to those proximal to -CF₃, which corroborates with the experimental observations of statistical regioselectivity among aryl C-H bonds in the absence of significant steric hindrance. Conversely, utilizing a bulky spirobipyridine ligand that imposes substantial steric hindrance at the activation sphere is predicted to effectively promote meta-to-fluorine regioselectivity, which was found to arise from the stronger catalyst-substrate interactions due to the steric-screening effect, leading to the lower C-H activation barrier.

PMID:42048608 | DOI:10.1021/acs.joc.5c03133

Lab Med. 2026 Apr 3;57(3):lmag021. doi: 10.1093/labmed/lmag021.

ABSTRACT

INTRODUCTION: A collective elevation in serum uric acid levels was detected among 98 military conscripts during routine physical examinations. This study aimed to determine the underlying causes and establish a traceable laboratory quality control framework for abnormal biochemical results.

METHODS: A systematic investigation encompassing analytical performance, preanalytical variables, and possible exogenous interferences was conducted. Population characteristics, including dietary habits and physical activity, were examined and experimentally validated. Ten participants were retested after controlling high-purine intake and exercise intensity.

RESULTS: No deviations were identified in analytical systems or preanalytical procedures. No interference from exogenous substances was observed. Controlled validation experiments demonstrated that consumption of chicken liver and high-intensity exercise increased serum uric acid levels by 21.22% and 20.85%, respectively (P < .001). After intervention, serum uric acid levels decreased by 32.40% on average, with levels in 70% of participants returning to the reference range.

DISCUSSION: The generalized serum uric acid elevation in conscripts was primarily attributed to combined effects of a high-purine diet and strenuous exercise. Establishing a traceable, standardized quality management model enables laboratories to accurately identify, verify, and resolve abnormal test results, enhancing analytical reliability and clinical data integrity.

PMID:42048558 | DOI:10.1093/labmed/lmag021

JMIR Med Inform. 2026 Apr 28;14:e79972. doi: 10.2196/79972.

ABSTRACT

BACKGROUND: Computed tomography pulmonary angiography (CTPA) is the standard imaging modality for diagnosing pulmonary embolism (PE), but diagnostic uncertainty is common due to technical limitations and vague language, leading to inconsistent interpretation and clinician frustration.

OBJECTIVE: This study develops a prompt-free, data-efficient method for assessing diagnostic certainty of PE in CTPA reports using small pretrained language models.

METHODS: This study examined 173 consecutive CTPA reports from UMass Memorial Health, each annotated by 3 radiologists for PE diagnostic certainty. We developed PECertainty, a lightweight, prompt-free model, and compared it with advanced large language model (LLM)-based methods under limited supervision settings. Baselines included prompt-free methods (support vector machine, random forest, and RoBERTa [Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach]) and prompt-dependent methods (LLM fine-tuning, in-context learning, and ADAPET [A Densely-Supervised Approach to Pattern Exploiting Training]; UNC Chapel Hill) with open-source Gemma3-4B (Google DeepMind) and Llama3.2-3B (Meta), and the proprietary GPT-3.5 (OpenAI). Sensitivity analyses evaluated performance with 1 to 10 training examples per category for the top performer. Model performance was evaluated against radiologist annotations. External validation on 420 CTPA reports from the Baystate Medical Center, with validation limited to distinguishing certain from uncertain reports. Interpretability of the top-performing models (PECertainty and GPT-3.5) was evaluated using integrated gradients and prompt-based explanations reviewed by radiologists.

RESULTS: Among prompt-dependent methods, GPT-3.5 fine-tuning (F1-score 0.86; 95% CI 0.71-1.0) and in-context learning (F1-score 0.87; 95% CI 0.71-1.0) performed best, and the performance of in-context learning consistently outperformed 0-shot learning for Gemma3-4B (F1-score 0.63, 95% CI 0.56-0.79 vs F1-score 0.45; 95% CI 0.29-0.56) and Llama3.2-3B (F1-score 0.54; 95% CI 0.41-0.71 vs F1-score 0.43, 95% CI 0.28-0.62). PECertainty demonstrated numerically better or equivalent performance compared with both the top-performing prompt-dependent methods and all prompt-free baselines. Compared with fine-tuned ClinicalBERT (Bidirectional Encoder Representations From Transformers Pretrained on Clinical Text), PECertainty achieved statistically significant improvements across all metrics (paired bootstrap significance test, P<.05). RoBERTa (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach) fine-tuning lagged (F1-score 0.52; 95% CI 0.35-0.71), and simple models such as support vector machine underperformed. In few-shot settings (10 examples/category), PECertainty (F1-score 0.80; 95% CI 0.59-0.94) outperformed both GPT-3.5 fine-tuning (F1-score 0.74; 95% CI 0.58-0.88) and in-context learning (F1-score 0.65; 95% CI 0.47-0.83). External validation on the Baystate dataset showed good generalization for distinguishing certain from uncertain cases (F1-score 0.77; 95% CI 0.70-0.83). Despite its strong performance, PECertainty was rated as less interpretable than fine-tuned GPT-3.5 by radiologists (t test, P<.05).

CONCLUSIONS: PECertainty enables accurate and data-efficient assessment of diagnostic certainty from free-text CTPA reports in low-resource settings. As an open-source, lightweight alternative to proprietary LLMs, it may support more precise communication between radiologists and referring physicians, with interpretability identified as a key direction for improvement.

PMID:42048554 | DOI:10.2196/79972

G3 (Bethesda). 2026 Apr 28:jkag106. doi: 10.1093/g3journal/jkag106. Online ahead of print.

ABSTRACT

Mungbean (Vigna radiata (L.) R. Wilczek) is a vital source of digestible proteins and is well-suited for the plant-based protein industry. In this study, we analyzed pod morphological traits in the Iowa Mungbean Diversity (IMD) panel of 372 genotypes (2022-23) using image-analysis-based phenotyping on 2,418 pod images. Pod morphological traits were extracted using deep learning image analysis, achieving excellent agreement with manual measurements (r>0.96 for pod length and seed per pod). Four complementary GWAS models identified 65 significant SNPs (-log10(P) ≥ 5.56) associated with pod curvature, length, width, and seed per pod traits. A significant SNP (5_35265704) on chromosome 4 was linked to pod dimensional traits, length, width, and curvature. A candidate gene, Virad04G0076900, located 15.6 kb from this SNP, is part of the GH3 gene family and has an Arabidopsis ortholog (AT4G27260) known for influencing organ elongation, pod, and seed development. Another SNP, 5_210437 on chromosome 6, has been found to be significantly associated with both pod length and seed per pod. A candidate gene, Virad06G0002400 (36.5 kb from this SNP), encodes a potassium transporter and shares homology with the Arabidopsis gene HAK5 (AT4G13420), known to influence pod growth. Image-based measurements achieved genomic prediction accuracies ranging from 0.61 to 0.85 across various traits, demonstrating comparable accuracy to manual methods for linear traits and up to 22% improvement for complex shape traits. These results highlight the potential of deep learning-assisted phenomics integrated with genomic tools to accelerate selection for improved pod architecture in mungbean breeding programs across the Midwestern United States and globally.

PMID:42048549 | DOI:10.1093/g3journal/jkag106

J Pharm Pharmacol. 2026 Apr 3;78(4):rgag037. doi: 10.1093/jpp/rgag037.

ABSTRACT

The application of Quality-by-Design approaches in the development of analytical methods has changed the way drugs are manufactured, switching from trial-and-error and one-variable-at-a-time methods to a structured, risk-based scientific framework. The Analytical Target Profile is at the heart of Quality-by-Design. It clearly lays out the criteria for how well the method will perform. The identification of Critical Quality Attributes and Critical Method Parameters through systematic risk assessment tools like the Ishikawa diagram and Failure Mode and Effect Analysis supports this. Statistical methods, particularly Design of Experiments, have been crucial for identifying and optimizing key variables. This leads to the development of a Method Operable Design Region (MODR). The MODR sets the limits for the analytical method’s reliable results, which means that changes can be made after approval without having to go through the whole process again. Box-Behnken and Central Composite are two common designs that are used to determine how various factors interact in order to ensure that methods perform effectively. Quality-by-Design-based control strategies combine lifecycle management and real-time monitoring to make sure that quality continues to improve more effectively. Literature screening and data organization were performed using Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). Reference management and duplicate removal were carried out using EndNote (Clarivate Analytics). Database searches were conducted across PubMed, Web of Science, Elsevier, and Google Scholar using predefined keywords.

PMID:42048548 | DOI:10.1093/jpp/rgag037

Int Rev Psychiatry. 2026 Feb-May;38(1-3):27-36. doi: 10.1080/09540261.2025.2523455. Epub 2025 Jun 30.

ABSTRACT

Psychiatric classification systems have evolved significantly over the past three decades. This article critically examines the transition from DSM-IV and ICD-10 to the more recent DSM-5-TR and ICD-11, analysing key conceptual shifts, including the redefinition of diagnostic criteria, the inclusion of emerging disorders, and the removal of outdated or stigmatizing categories. Particular attention is paid to the growing limitations of categorical models in capturing clinical heterogeneity and comorbidity. In response, dimensional approaches and precision psychiatry have gained prominence, aiming to integrate symptom severity, biomarkers, and neurobiological correlates into diagnostic decision-making. The manuscript explores future trajectories for psychiatric nosology, including the potential role of artificial intelligence, the recognition of novel syndromes such as eco-anxiety and digital addictions, and the need for culturally sensitive frameworks. It also provides stakeholder-specific recommendations-addressed to clinicians, researchers, policymakers, and educators-summarized in a dedicated table. These forward-looking strategies emphasize the importance of ethical, interdisciplinary, and inclusive practices in updating diagnostic systems. Ultimately, the future of psychiatric classification lies in striking a balance between scientific rigor, sociocultural relevance, and individual variability, ensuring that future manuals serve both clinical utility and human dignity.

PMID:42048530 | DOI:10.1080/09540261.2025.2523455

JMIR Med Inform. 2026 Apr 28;14:e82329. doi: 10.2196/82329.

ABSTRACT

BACKGROUND: Accurate prediction of surgical duration is critical for optimizing operating room scheduling and resource allocation. Existing models, however, exhibit limited applicability in pediatric urology due to the unique anatomical and developmental characteristics of children.

OBJECTIVE: This study aimed to develop and validate a specialty-tailored prediction framework for estimating the duration of pediatric urological surgeries.

METHODS: We integrated multisource heterogeneous data, encompassing patient demographics, surgical details, surgeon-specific features, and electronic medical record narratives, to develop a customized prediction system. Large language model techniques were used to extract semantic representations from unstructured clinical text, while a multihead perceptron architecture enabled the efficient fusion of structured and unstructured features. Pediatric-specific clinical variables, such as developmental stage and the severity of urinary tract malformations, were explicitly modeled to capture their impact on surgical duration.

RESULTS: The proposed approach achieved a mean absolute error of 11.39 minutes and a root mean square error of 15.58 minutes, markedly outperforming existing methods. Comparative analyses demonstrated that the Qwen-based structured preprocessing with text embeddings provided superior feature representation, surpassing both traditional long short-term memory and direct Embedding-3 approaches. Feature importance analysis identified the primary surgical procedure, surgical plan, and preoperative diagnosis as dominant predictive factors.

CONCLUSIONS: By combining innovative feature engineering with a tailored model architecture, the proposed framework substantially improves the accuracy of surgical duration prediction in pediatric urology. These findings offer robust technical support for precision operating room scheduling and hold significant clinical value in enhancing the efficiency of surgical resource utilization.

PMID:42048521 | DOI:10.2196/82329

JMIR Form Res. 2026 Apr 28;10:e85349. doi: 10.2196/85349.

ABSTRACT

BACKGROUND: Digital self-control tools (DSCTs) have emerged as technological interventions to address excessive smartphone usage and promote digital well-being. However, these tools face persistent challenges with user attrition and sustained engagement, compromising their long-term effectiveness. Current literature lacks an understanding of how observable behavioral indicators, as opposed to self-reported measures, are associated with user engagement and readiness to change in DSCTs.

OBJECTIVE: This study addresses three research questions (RQs): (RQ1) whether prompting passive DSCT users to reconfigure nudges increases subsequent user-nudge interaction, (RQ2) how engagement evolves over time and what behavioral divergence emerges between accepting and rejecting users, and (RQ3) whether observable in-app behavioral indicators are more strongly associated with intervention acceptance than traditional self-reported measures.

METHODS: We conducted a quasi-experimental study (N=252) targeting users who had disabled nudges. Participants were randomly assigned to receive a prompt to reconfigure their nudge settings during daily check-ins (n=138, experimental group) or to a control condition (n=114, no intervention). The experimental group was further classified into acceptance and rejection subgroups based on their response to the intervention. Data collection included DSCT configuration logs, usage-triggered nudge logs, and self-reported questionnaire responses. We analyzed user-nudge interaction ratios using difference-in-differences with permutation tests (RQ1) and nudge configuration parameters and manual app blocking using independent-samples t tests with Cohen d (RQ2) and compared behavioral indicators against self-reported measures using t tests and chi-square tests (RQ3).

RESULTS: Of the experimental participants, 46% (63/138) accepted the nudge reconfiguration prompt. Post intervention, the acceptance subgroup’s 7-day average user-nudge interaction ratio increased from 29.7% to 58.5% (peak of 65% on day 1), a significant increase even after controlling for the temporal decline observed in the control group (difference-in-differences=+36.3 percentage points, P<.001). The rejection subgroup’s decline was not significantly different from the control group’s decline (P=.82). The acceptance subgroup showed preexisting behavioral indicators of higher readiness to change, including 21.53% shorter consecutive usage thresholds (P=.03) compared to the rejection subgroup, with a directionally consistent but nonsignificant difference in cooldown length (+20.56%). Behavioral divergence in consecutive usage thresholds widened post intervention, with Cohen d increasing from -0.47 to -0.67 (P=.002). Acceptance subgroup participants demonstrated a significantly lower tendency to select leisure-oriented daily goals (15.6% vs 26.2%; chi-square P=.001, Cramer V=0.13). Self-reported measures of screen time goals and scrolling regret were not significantly associated with intervention acceptance (P>.10).

CONCLUSIONS: Observable in-app behavioral indicators, rather than self-reported measures, effectively differentiate intervention receptiveness. Study results suggest that effective DSCT design should incorporate adaptive strategies that recognize and respond to users’ readiness to change, as evidenced by their in-app behaviors, while preserving autonomy. Such systems are likely to outperform static interventions or designs that rely solely on self-reported preferences.

PMID:42048520 | DOI:10.2196/85349

Pol Merkur Lekarski. 2026;54(2):190-197. doi: 10.36740/Merkur202602114.

ABSTRACT

OBJECTIVE: Aim: To evaluate the impact of adding bempedoic acid to atorvastatin therapy on cardiovascular outcomes, including major adverse cardiovascular events (MACE), cardiac arrest, all-cause hospitalization, and myopathy, in patients with established coronary artery disease.

PATIENTS AND METHODS: Materials and methods: We conducted a retrospective cohort study using the TriNetX global health research network. Patients with established CAD were categorized based on their antilipemic therapy into two cohorts: atorvastatin monotherapy and atorvastatin plus bempedoic acid. Propensity score matching was employed to balance baseline characteristics between the cohorts. The primary outcome was the occurrence of MACE, while secondary outcomes included cardiac arrest events, all-cause hospitalization, and incidence of myopathy.

RESULTS: Results: After matching, 6,549 patients were included in each cohort. MACE occurred more frequently in the atorvastatin group (5.7%) compared to the combination therapy group (3.2%), with a hazard ratio of 1.606 (95% CI: 1.302-1.980, P<0.001). Cardiac arrest and all-cause hospitalizations were also higher in the atorvastatin group, with hazard ratios of 1.628 (95% CI: 1.041-2.544, P<0.001) and 1.418 (95% CI: 1.177-1.710, P<0.001), respectively. No significant difference in myopathy was observed (HR 0.915, 95% CI: 0.741-1.129).

CONCLUSION: Conclusions: Adding bempedoic acid to atorvastatin therapy appears to confer a protective effect in CAD patients by significantly reducing MACE, cardiac arrest, and hospitalizations without increasing myopathy risk. Prospective studies are warranted to confirm these findings.

PMID:42048509 | DOI:10.36740/Merkur202602114