Tag: pubmed

J Plast Reconstr Aesthet Surg. 2026 Apr 15;117:94-103. doi: 10.1016/j.bjps.2026.04.005. Online ahead of print.

ABSTRACT

BACKGROUND: Precise preoperative assessment and selection of optimal perforators are critical to optimize outcomes in DIEP flap microsurgical breast reconstruction (BR). Although CTA is widely used, color-coded duplex sonography (CCDS) offers a radiation-free and cost-effective alternative. This study presents a standardized step-by-step protocol for duplex-based perforator mapping and evaluates its application in a prospective case series.

METHODS: We prospectively enrolled patients who underwent unilateral or bilateral DIEP flaps BR between January 2022 and January 2025. All patients received preoperative CCDS mapping to identify the dominant perforator. Variables recorded included number, location, size, and flow characteristics of perforators, flap weight, and flap-related complications.

RESULTS: A total of 62 flaps were performed. The average number of perforators identified per flap was 3.19 (SD: 1.39), with a mean of 1.35 (SD: 0.54) perforators harvested. The mapped dominant perforator matched the intraoperative finding in 50/62 cases (80.64%), with agreement being statistically higher in unilateral flaps than in bilateral flaps (88.63% vs. 61.11%; p=0.018). The choice of perforator row was statistically different between the unilateral and bilateral flaps (p=0.045), with 70.4% of medial row in unilateral and 55.5% of lateral row in bilateral flaps. Postoperative complications occurred in 6 flaps (9.68%): fat necrosis (n=3), partial flap necrosis (n=2), and total flap loss (n=1).

CONCLUSION: Using our step-by-step approach, preoperative CCDS is known to be a reliable, reproducible, and non-invasive tool for assessing perforators in DIEP flap reconstruction. This examination may represent a feasible, radiation-free adjunct that can be performed directly by the microsurgeon preoperatively.

LEVEL OF EVIDENCE: IV.

PMID:42048682 | DOI:10.1016/j.bjps.2026.04.005

JMIR Ment Health. 2026 Apr 28;13:e81213. doi: 10.2196/81213.

ABSTRACT

BACKGROUND: Suicide helplines increasingly employ chat services to aid those in urgent need, but the wording and structure of text-driven exchanges may affect their effectiveness.

OBJECTIVE: Given the association of cognitive distortions with depression and anxiety, this study investigated their prevalence in the language of individuals seeking help from the Dutch 113 suicide helpline.

METHODS: We observed the prevalence of cognitive distortions for both help seekers and counselors in a large volume of chat sessions (N=71,148) of the Dutch 113 suicide chat helpline using natural language processing. The results were compared to 2 large collections of online text data from Dutch social media and web content.

RESULTS: We found that nearly all types of cognitive distortions are more prevalent in the language of help seekers compared to the control group of helpline counselors. Distortions of the personalizing, emotional reasoning, and mental filtering types were, respectively, 20.22, 7.87, and 4.53 times more prevalent among help seekers, revealing a distinct pattern of thought and language among individuals affected by suicidality.

CONCLUSIONS: Our results raise the prospect of improving the effectiveness of online therapeutic interventions that target cognitive distortions through lexical analysis that detects the cognitive and lexical markers of suicidality.

PMID:42048667 | DOI:10.2196/81213

JMIR Public Health Surveill. 2026 Apr 28;12:e81288. doi: 10.2196/81288.

ABSTRACT

BACKGROUND: Antimicrobial resistance is a public health crisis exacerbated by the irrational use of antibiotics, particularly in low- and middle-income countries. Pakistan, one of the highest consumers of antibiotics globally, faces unique challenges, including unregulated sales, overuse of broad-spectrum antibiotics, and inadequate stewardship programs.

OBJECTIVE: This study aimed to analyze antibiotic consumption trends in Pakistan from 2016 to 2023, project future use through 2028, and evaluate the subsequent implications for antimicrobial resistance and antimicrobial stewardship programs.

METHODS: Antibiotic sales data were retrieved for Pakistan from the IQVIA MIDAS database spanning 2016 to 2023. Data were converted to defined daily doses (DDDs) and DDD per 1000 inhabitants per day (DID) using the World Health Organization Anatomical Therapeutic Chemical classification system. Data cleaning, statistical analyses, and data visualization were performed using R software (version 4.3.2) and Microsoft Excel. Trends were analyzed using linear regression, while future projections (2024-2028) were developed using trend-based models. Descriptive analysis was performed, and visualizations were used to illustrate findings.

RESULTS: The total antibiotic consumption in Pakistan from 2016 to 2023 was 12.88 billion DDDs. Broad-spectrum penicillins and fluoroquinolones, each accounting for 37.7 DID, were the most consumed classes. The analysis revealed significant increases in the consumption of macrolides (+76%; rising from 2.26 to 3.99 DID) and cephalosporins (+36%; from 2.87 to 3.89 DID) from 2016 to 2023, with macrolides projected to reach 5.79 DID by 2028. Reserve antibiotics, including oxazolidinones (+354%; from 0.03-to 0.014 DID) and glycylcycline (+236%; from 0.001 to 0.0003 DID), also showed appreciable increases, reflecting greater reliance on last-line therapies. In contrast, aminoglycosides (-36%; from 0.013 to 0.14 DID) and narrow-spectrum penicillins (-30%; from 0.008 to 0.005 DID) experienced notable declines.

CONCLUSIONS: The study highlights a concerning overreliance on broad-spectrum and reserve antibiotics in Pakistan, thus underscoring the urgent need for robust antimicrobial stewardship programs and stricter regulation of over-the-counter antibiotic sales to rationalize antibiotic use. Future efforts should focus on addressing gaps in prescribing practices, improving diagnostic capacity, and monitoring stewardship program outcomes to mitigate resistance development and preserve antibiotic efficacy.

PMID:42048666 | DOI:10.2196/81288

JMIR Form Res. 2026 Apr 28;10:e86203. doi: 10.2196/86203.

ABSTRACT

BACKGROUND: Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including heart rate variability (HRV), supporting analysis of pregnancy-related conditions and personalized care throughout the perinatal period. While recent studies demonstrate the utility of personal DHTs in tracking pregnancy-related symptoms, they often rely on aggregate statistical methods that overlook individual variability.

OBJECTIVE: This study aims to compare aggregate and individual-level analyses of DHT data for pregnancy-related conditions, using the comprehensive BUMP (Better Understanding the Metamorphosis of Pregnancy) dataset to highlight the importance of individual variability and data heterogeneity.

METHODS: We analyzed wearable and self-reported data from 256 participants enrolled in the BUMP study (January 2021 to May 2022), including HRV, sleep, and fatigue measured via Oura Rings and smartphone surveys. Individual-level (N-of-1) trajectories were evaluated and compared with aggregate results to uncover personal and collective trends. A statistical method was developed to assess the influence of adverse events and severe symptoms, while case studies explored confounding and modifying factors underlying heterogeneity. Comprehensive statistical analysis included the coefficient of determination, Kolmogorov-Smirnov tests, likelihood ratio tests, and Welch t tests, with interindividual variability flagged based on high-variability thresholds.

RESULTS: Substantial interindividual variability was observed across all features. Only 4.76% (12/256) of participants exhibited an HRV inflection at the aggregate week-33 inflection point, with a coefficient of variation of 14.24%. The median value of the gestational week in individual fatigue troughs was 23 (IQR 8; range 8-38) weeks, differing from aggregate estimates. Distributional comparisons showed no statistically significant differences in individual-level model fit (R²) by pregnancy complications or age (P values ranging from .06 to .99 across all model fit comparisons). Case studies further highlighted both intraindividual and interindividual differences, emphasizing the importance of considering external factors, such as adverse events and severe symptoms.

CONCLUSIONS: Our findings show that aggregate wearable data often fail to generalize across populations, oversimplifying pregnancy-related physiological and subjective changes. This simplification can obscure individual trajectories, leading to generalized insights that may not reflect many pregnant women’s experiences. Our results highlight the impact of heterogeneity on pregnancy outcomes, emphasizing the need to move beyond one-size-fits-all models and leverage DHT for personalized care.

PMID:42048637 | DOI:10.2196/86203

J Phys Chem B. 2026 Apr 28. doi: 10.1021/acs.jpcb.6c01076. Online ahead of print.

ABSTRACT

Elucidating the microscopic clustering of molecules is pivotal to understanding the macroscopic behavior of complex liquids. However, resolving specific cluster contributions from congested vibrational signatures remains a formidable challenge due to severe spectral overlap. Herein, we present a high-throughput computational framework that integrates molecular dynamics sampling and density functional theory calculations with constrained geometry optimization to reconstruct infrared (IR) and excess IR spectra. By statistically weighting the spectra of thousands of cluster isomers, this method quantitatively reproduces experimental measurements without relying on a priori structural assumptions. Applied to TBA-CCl₄ mixtures, it shows that the concentration-dependent IR changes are governed by the redistribution of cluster populations rather than simple hydrogen-bond weakening. Experimentally, this evolution is reflected mainly in band-shape changes and the gradual emergence of a high-frequency shoulder. Furthermore, we demonstrate that the complex features in the excess IR spectra arise not from a single dominant species but from the cooperative contributions of multiple coexisting hydrogen-bonded networks, specifically the competitive balance between the formation of small chain oligomers and the dissociation of large size clusters. This spectroscopically driven strategy provides a robust tool for deconvoluting molecular-level heterogeneity in complex condensed phases, effectively bridging the gap between atomistic simulations and spectroscopic observables.

PMID:42048632 | DOI:10.1021/acs.jpcb.6c01076

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