Tag: pubmed

Plant Physiol Biochem. 2026 May 8;234:111354. doi: 10.1016/j.plaphy.2026.111354. Online ahead of print.

ABSTRACT

Brassinosteroids (BR) are the steroidal phytohormones, best known for their role in plant growth and enhancing tolerance to abiotic stress for the last two decades. We investigated the effects of 24-epibrassinolide (BR – 1, 2, and 3 μM) on tomato seedlings grown under 150 mM NaCl from physiological and metabolomics perspectives. A supervised multiblock orthogonal partial least squares ANOVA (AMOPLS) analysis was performed on the untargeted metabolomics data to dissect influential factors and identify discriminant metabolites. The results showed that exogenous BR recovered the impaired photosynthetic performance induced by NaCl, as observed by increased chlorophyll content and photochemical efficiency of PSII (Phi2), while reducing PhiNPQ. Moreover, the activities of the enzymes SOD, APX, and CAT increased by 30%, 55%, and 786.3%, respectively, in BR + NaCl compared to NaCl. Unsupervised and supervised statistics revealed that, while NaCl had a dominant effect on metabolic profiles, BR modulated specific pathways like amino acids, hormone crosstalk, and secondary metabolite biosynthesis. Among phenylpropanoids and nitrogen-containing compounds, the general accumulation of lignin- and glucosinolate-related metabolites in the combined BR and NaCl treatment, compared to NaCl, indicated that BR improved plant cell membrane integrity. In addition, metabolites linked to stress defense, such as proline, glycine betaine, D-sorbitol 6-phosphate, and secologanin, accumulated. The findings identified several novel metabolites, such as N-formyl-L-kynurenine and 7,8-dihydromonapterin, attributed to BR that may support the development of NaCl-tolerant tomato plants.

PMID:42119294 | DOI:10.1016/j.plaphy.2026.111354

Heart Lung. 2026 May 12;79:102831. doi: 10.1016/j.hrtlng.2026.102831. Online ahead of print.

ABSTRACT

BACKGROUND: Risk stratification of emergency department (ED) patients presenting with chest pain is particularly challenging in hospitals without catheterization laboratory facilities, often leading to unnecessary coronary referrals.

OBJECTIVES: To evaluate and compare the predictive performance of the SVEAT and HEART scores for 30-day major adverse cardiac events (MACE) in ED chest pain patients referred from non-PCI centers and to examine their potential to reduce unnecessary coronary referrals.

METHODS: This prospective observational study included 230 adult patients with non-traumatic chest pain who were referred to a coronary center from a secondary-level ED without PCI capability. SVEAT and HEART scores were calculated at presentation, and patients were followed for 30 days to identify MACE. Receiver operating characteristic (ROC) curves were used to evaluate diagnostic performance, and a DeLong test was applied for statistical comparison of the two scores.

RESULTS: Among 230 referred patients, 158 (68.7%) did not experience MACE. The SVEAT score demonstrated a higher area under the ROC curve (AUC:0.969; 95% CI:0.946-0.991) than the HEART score (AUC:0.948;95% CI:0.917-0.979) (p = 0.0457). While the HEART score had higher sensitivity (88.9%), the SVEAT score showed greater specificity (98.1%) and a superior positive predictive value (95.1% vs. 77.1%). Overall diagnostic accuracy was higher for the SVEAT score (92.6%).

CONCLUSION: Both the SVEAT and HEART scores are effective tools for predicting MACE in chest pain patients without STEMI in non-PCI EDs. However, the SVEAT score offers greater specificity and accuracy, supporting more individualized referral decisions in low-risk patients when used together with clinical judgment.

PMID:42119270 | DOI:10.1016/j.hrtlng.2026.102831

Clin Radiol. 2026 May 12;98:107356. doi: 10.1016/j.crad.2026.107356. Online ahead of print.

ABSTRACT

PURPOSE: To compare the performance of preoperative contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) in assessing index tumour size and detecting additional malignant lesions and to evaluate total disease extent using postoperative histopathology as the reference standard.

METHODS: Retrospective analysis of 52 women with biopsy-proven breast cancer who underwent both CEM and MRI between July 2019 and December 2023. Two radiologists independently reviewed studies at separate times to reduce recall bias and were blinded to pathology. Index lesion size, additional suspicious lesions, and total disease extent were recorded. Statistical analysis included intraclass correlation coefficient (ICC) for lesion size, Kappa statistics for additional lesion detection, and Bland-Altman plots to assess agreement with histopathology.

RESULTS: CEM detected 51 of the 52 (98%) index lesions; MRI detected all (100%). Mean index lesion size was similar (CEM 24.9 ± 22.9 mm vs MRI 25.2 ± 22.8 mm; ICC = 0.975). Additional lesions were identified in 23 of the 52 patients, with very good agreement between modalities (Kappa = 0.881, P<.001). Among 42 patients with histopathological data on total disease extent (including multifocal or multicentric disease), CEM measurements closely matched histopathology (mean 32.6 mm vs 32.6 mm), while MRI slightly overestimated extent (mean 35.0 mm vs 32.6 mm). Discrepancies >20 mm occurred in five patients, mainly in cases with non-mass enhancement.

CONCLUSION: CEM shows high concordance with MRI for measuring index lesion size and detecting additional suspicious lesions, with closer agreement to histopathology for total disease extent. CEM is a viable, efficient alternative to MRI for preoperative locoregional staging of breast cancer.

PMID:42119266 | DOI:10.1016/j.crad.2026.107356

Environ Int. 2026 May 9;212:110287. doi: 10.1016/j.envint.2026.110287. Online ahead of print.

ABSTRACT

COPD may be influenced by complex environmental interactions. Current evidence is limited to isolated stages and is lacking on the effects of mixture exposures. This study assessed the effects of environmental mixtures on COPD risk trajectories. We analyzed the impact of mixture exposures (air pollution, meteorological factors, built environment features) on COPD hospitalization/mortality for 67,235 participants. We applied Weighted Quantile Sum regression to quantify mixture effects and mixture contributions. In the WQS regression analysis, each quartile increase in the WQS index was associated with a 29.9% higher risk of transitioning from hospitalization to death, with NO₂ (21.8%) and SO₄^2- (19.4%) contributing the most. No significant associations were observed for the transition from baseline to hospitalization or the transition from baseline to death. Furthermore, we employed K-means clustering to identify distinct exposure patterns. Three exposure patterns were identified. Compared with the Urbanization-Dominant Pattern, the Urbanization-Ecology Balanced and Ecology-Dominant Patterns were inversely associated with transitions from baseline to hospitalization (17.5% and 9.0% lower risk, respectively) and to death (17.8% and 9.2% lower risk, respectively). The elderly had an 37.8% higher risk of the transition from hospitalization to death. Environmental mixtures significantly affect COPD trajectories. Controlling critical pollutants and optimizing ecological planning are essential for the COPD burden.

PMID:42119250 | DOI:10.1016/j.envint.2026.110287

Int Dent J. 2026 May 12;76(4):109617. doi: 10.1016/j.identj.2026.109617. Online ahead of print.

ABSTRACT

INTRODUCTION AND AIMS: Despite advances in AI-based periodontitis screening, quantifiable and interpretable biomarkers from intraoral photographs remain underexplored. Therefore, this study aimed to develop a deep learning pipeline for exposed root area quantification from photographs and to evaluate its predictive value for periodontitis risk within a multimodal framework integrating age.

METHODS: Intraoral photographs of the mandibular anterior sextant and covariate questionnaires were obtained from 269 participants. A fine-tuned YOLOv11 segmentation model quantified tooth and exposed root surface areas, from which the exposed root ratio (ERR) was derived. ERR was combined with age and self-reported data to train four machine learning models (logistic regression, SVM, random forest, gradient boosting) for periodontitis prediction. Performance was assessed using AUROC and permutation feature importance across different feature sets.

RESULTS: The YOLOv11 segmentation model achieved an overall [email protected] of 0.901, with mean Dice coefficients of 0.928 and 0.844 for tooth and exposed root, respectively. In the ≥35 age group, ERR-only models outperformed age-only models across all four machine learning algorithms, with statistically significant differences in 13 of 24 comparisons (mean ΔAUROC: 0.031-0.094, p < .05). Integration of ERR with age further improved predictive performance, yielding significant gains in 19 of 24 comparisons (mean ΔAUROC: 0.029-0.131, p < .05). Permutation feature importance analysis revealed ERR as the dominant predictor in the ≥45 age group, with importance scores of 0.391 and 0.366 for ERR compared to 0.151 and 0.273 for age in Gradient Boosting and Random Forest, respectively.

CONCLUSION: AI-derived ERR from mandibular anterior images is a reproducible, interpretable biomarker that outperforms age and enhances periodontitis prediction when combined with conventional risk factors.

CLINICAL RELEVANCE: AI-driven quantification of ERR from intraoral photographs offers a practical, non-invasive, and cost-effective screening tool for periodontitis risk assessment in primary care and community settings, particularly among middle-aged and older populations.

PMID:42119243 | DOI:10.1016/j.identj.2026.109617

J Pediatr Nurs. 2026 May 12;89:382-389. doi: 10.1016/j.pedn.2026.04.033. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to determine the Pediatric Quality of Life with PID according to their method of Ig administration, the care burden of their mothers, and the relationship between these two factors.

DESIGN AND METHODS: This descriptive and correlational study was conducted with children aged 2-18 years diagnosed with primary immunodeficiency and their mothers at a university hospital in Konya (n = 98). Data were collected using the “Child and Mother Diagnosis Form,” the “Pediatric Quality of Life Inventory (PEDSQL),” and the “Caregiving Burden Scale.”

RESULTS: According to both parent and child reports, there was no significant difference in Pediatric Quality of Life scores between IVIG and SCIG groups SCIG (p > 0.05). Similarly, maternal caregiving burden did not differ significantly by IG administration method or age range (p > 0.05). Notably, a strong negative correlation was identified between children’s quality of life and maternal care burden in mother-reported outcomes (r = -0.710, p = 0.000). However, this relationship was not statistically significant in child-reported outcomes.

CONCLUSION: In conclusion, the method of Ig administration (IVIG vs. SCIG) does not appear to influence pediatric quality of life or maternal caregiving burden. However, a significant link exists between these two variables: as the child’s quality of life decreases, the mother’s caregiving burden increases, especially according to parental reports.

PRACTICE IMPLICATIONS: Nursing interventions aimed at improving parental skills can be incorporated into parental skill-based treatments, such as SCIG, to reduce maternal care burden.

PMID:42119234 | DOI:10.1016/j.pedn.2026.04.033

Eur J Surg Oncol. 2026 May 6;52(7):111869. doi: 10.1016/j.ejso.2026.111869. Online ahead of print.

ABSTRACT

BACKGROUND: In 2020, immunotherapy entered first-line care for hepatocellular carcinoma (HCC). It remained uncertain whether population-level survival improved thereafter and whether adding immunotherapy to chemotherapy in routine practice was associated with additional benefit.

METHODS: A population-based SEER analysis was performed among HCC diagnosed in 2018-2022. Comparative analyses were performed between cases in the immunotherapy era (IEC) versus cases in the pre-immunotherapy era (PIEC). A prespecified chemotherapy subset compared chemotherapy + immunotherapy with chemotherapy alone. Competing-risks methods (Gray’s test; Fine-Gray) were applied with cancer-related death (CRD) as the event.

RESULTS: In total, 12056 patients were included (PIEC 7291; IEC 4765). After matching (n = 8796), better survival was observed in IEC versus PIEC (HR 0.905, 95% CI 0.846-0.968; P = 0.004). In the matched cohort, a lower CRD risk was also observed for IEC (sHR 0.807, 95% CI 0.750-0.868; P = 7.33 × 10^-9) with a matched-strata Gray’s P = 0.0105. In the chemotherapy subset (n = 3381; PSM n = 1884), the addition of immunotherapy was not associated with a statistically significant OS advantage after adjustment (HR 0.917; P = 0.166) or after matching (HR 0.914; P = 0.218), a pattern that remained consistent in the non-surgical subgroup (matched HR 0.885; P = 0.106). The matched CRD comparison was not significant (Gray’s P = 0.96), whereas an unmatched Fine-Gray model suggested a protective association (sHR 0.803, 95% CI 0.701-0.919; P ≈ 0.001).

CONCLUSIONS: Diagnosis in the post-approval immunotherapy era was associated with modestly improved OS and lower CRD at the population level. However, because this was an era-based rather than regimen-level comparison, the observed association cannot be attributed solely to immunotherapy uptake.

PMID:42119196 | DOI:10.1016/j.ejso.2026.111869

JMIR Mhealth Uhealth. 2026 May 12;14:e82276. doi: 10.2196/82276.

ABSTRACT

BACKGROUND: Nutrition apps offer scalable opportunities to support dietary behavior change and prevent chronic diseases. Their success depends on sustained user engagement, which is essential yet challenging to achieve and, consequently impacts the long-term effectiveness of these digital tools. Engagement strategies have been widely explored in digital health, but a comprehensive synthesis focusing on nutrition apps for adults is lacking.

OBJECTIVE: This scoping review aimed to map the current engagement approaches and metrics implemented in nutrition apps targeting adults and to identify how user engagement is defined across studies.

METHODS: We conducted a search of the PubMed, Scopus, Cochrane, and Web of Science databases for relevant studies published from January 1, 2013, to June 30, 2024. The inclusion criteria included original adult interventional or observational studies that evaluated nutrition apps and reported user‑engagement strategies or metrics. Two reviewers independently screened records in Covidence, with discrepancies resolved by a third reviewer. Data were charted across study characteristics, engagement strategies, and engagement metrics and then synthesized narratively.

RESULTS: A total of 59 studies that used apps to improve dietary behaviors were included in our analysis, including randomized controlled trials, observational trials, and mixed methods studies. Most of these apps were designed for adults who were overweight and obese. The studies were primarily conducted in North America and Europe and were randomized controlled trials or nonrandomized intervention studies, with varying durations and sample sizes. Engagement strategies varied widely, and engagement was typically measured by frequency of specific function use and frequency of app use, followed by retention rate. The most common engagement strategies reported in studies were push notifications (n=29, 49%), behavioral theory integration (n=24, 41%), personalization and customization (n=19, 32%), and goal‑setting features (n=18, 31%). Only 31% (n=18) of studies provided an explicit definition of “user engagement,” and definitions were highly heterogeneous. Engagement measurement was dominated by quantitative system‑recorded metrics, including time and frequency of using specific functions (n=38, 64%), app use frequency (n=34, 58%), and retention (n=17, 29%). Few studies assessed qualitative or long‑term engagement dimensions, and long‑duration studies rarely integrated adaptive or contextualized engagement mechanisms. Research apps more frequently used theory‑driven strategies compared with commercial apps, which tended to emphasize streamlined user experience.

CONCLUSIONS: Although several engagement strategies are commonly used, their implementation is inconsistent and often lacks grounding in conceptual frameworks. Research in the future needs to prioritize the use of common definitions for user engagement and measurement criteria while implementing user-centered design methods and using multiple research approaches to study the complex patterns of user engagement. The evidence base for engagement strategies needs strengthening because it will support the development of sustainable nutrition mobile health interventions.

PMID:42119138 | DOI:10.2196/82276

JMIR Med Inform. 2026 May 12;14:e84326. doi: 10.2196/84326.

ABSTRACT

BACKGROUND: The widespread adoption of electronic health records has resulted in the generation of large volumes of clinical notes. Learning algorithms and large language models can be trained on these resources, but they are susceptible to noise-irrelevant or noninformative data. This sensitivity can lead to significant challenges, including performance degradation and the generation of inaccurate predictions or “hallucinations.” This study addresses a critical challenge in clinical informatics: efficiently filtering millions of documents for relevance before advanced language model processing, particularly in resource-constrained environments.

OBJECTIVE: We present a novel framework for determining document relevance in clinical settings using a chronic obstructive pulmonary disease (COPD) dataset.

METHODS: We developed a novel framework using weak supervision and domain-expert heuristics to generate “silver standard” labels for training data and gold standard expert-annotated labels, creating 2 datasets to optimize the model during the development phase and subsequent testing phase. Various text representation techniques (bag of words, term frequency-inverse document frequency, lightweight document embeddings, compression-based features, and Unified Medical Language System concept extraction) were evaluated. These representations were used to train random forest, extreme gradient boosting, and k-nearest neighbor classifiers. Models were optimized on a small expert-annotated dataset and evaluated on a held-out test set.

RESULTS: The combination of lightweight document embedding with a random forest classifier demonstrated the best performance, achieving a precision of 0.73, recall of 0.86, and F1-score of 0.80 (95% CI 0.76-0.87) for identifying relevant COPD documents. This significantly outperformed baseline heuristics (precision=0.70; recall=0.38; F1-score=0.50, 95% CI 0.43-0.56) and other tested methods.

CONCLUSIONS: Our study presents a novel framework for identifying COPD-relevant clinical documents using lightweight embedding and machine learning. This approach effectively filters pertinent documents, enhancing information retrieval precision. The framework’s scalability and minimal annotation needs make it promising for diverse health care applications, potentially optimizing clinical outcomes through efficient document selection for data-driven decision support systems.

PMID:42119137 | DOI:10.2196/84326

Stud Health Technol Inform. 2026 May 7;335:236-241. doi: 10.3233/SHTI260090.

ABSTRACT

BACKGROUND: Machine learning models for predicting major adverse cardiovascular events (MACE) often generalize poorly across populations, and multinational development is limited by data-sharing constraints.

OBJECTIVES: We investigate whether federated learning (FL) can reduce the generalization gap of MACE prediction models across international clinical cohorts while preserving data privacy.

METHODS: Using harmonized electronic medical record (EMR) data from Austria, Brazil, and the USA, we train federated and local XGBoost and multilayer perceptron (MLP) models and evaluate performance using AUROC.

RESULTS: Our preliminary results show that the performance of local models degrades substantially on external cohorts, particularly when trained on smaller datasets. FL reduces this gap, with the greatest gains observed when compared to models trained on smaller cohorts and evaluated on the largest cohort. Local models performed best in-country, and XGBoost consistently outperformed MLPs.

CONCLUSION: Federated learning improves cross-site generalizability of MACE prediction models, with trade-offs between global robustness and local performance.

PMID:42119126 | DOI:10.3233/SHTI260090