Nevin Manimala

Appl Radiat Isot. 2025 May 8;223:111902. doi: 10.1016/j.apradiso.2025.111902. Online ahead of print.

ABSTRACT

Food counterfeiting is an emerging problem worldwide and the increasing consumption of fake products has brought food safety into major focus. In recent years, several analytical approaches were developed to prevent food counterfeiting. Among them, X-ray Fluorescence spectroscopy (XRF) is emerging as a fast and simple screening tool for food elemental analysis, with important applications in the agri-food sector. The present work explores the feasibility of using portable XRF device to verify the quality and the geographical origin of pistachio samples coming from different growing areas of Sicily (Italy), including pistachio samples form Bronte and Raffadali districts, recognized by the European Union with the Protected Designation of Origin (PDO) label. The XRF spectra and the yields extracted for the main identified elements were compared with each other by using Principal Component Analysis (PCA). Statistical analysis highlighted that pistachio samples clustered into distinct groups accordingly with their territory of origin, having a different elemental profile. Among the elements, K and Ca appear to act as discriminant markers, followed by Rb and Fe. Potassium mainly characterized the samples originating from Agrigento and Messina, whereas Ca, Rb and Fe the pistachio seeds harvested in Catania. Based on these results, the elemental composition detectable through XRF analysis could be used as a fingerprint to disentangle foodstuffs of different origin and to hinder the occurrence of food counterfeits concerning the branded products, in support of the traceability system. The possibility of assessing quality and traceability quickly, easily and in-situ, gives solid perspectives for a large-scale application of the XRF technique at all stages of the food chain.

PMID:40354687 | DOI:10.1016/j.apradiso.2025.111902

Rev Esc Enferm USP. 2025 May 12;59:e20240408. doi: 10.1590/1980-220X-REEUSP-2024-0408en. eCollection 2025.

ABSTRACT

OBJECTIVE: To assess the effect of a health promotion care model in adolescents and young adults with celiac disease.

METHOD: A quasi-experimental study. A total of 136 people participated, who, after obtaining informed consent, received a virtual intervention in August and September 2023. The data were analyzed using the Wilcoxon test.

RESULTS: Regarding the celiac symptom index, statistically significant differences were found, with a large effect size, where pretest scores were higher than posttest scores (p < 0.001). Regarding lifestyle, it was found that health-promoting behaviors presented statistically significant differences, with a large effect size. Pretest scores were lower than posttest scores (p < .001).

CONCLUSION: This groundbreaking study in Mexico demonstrated that health education significantly improves lifestyle and reduces symptoms in celiac patients who previously received limited attention to a gluten-free diet. It also highlights the crucial role of nurses as health educators in this field.

PMID:40354659 | DOI:10.1590/1980-220X-REEUSP-2024-0408en

Rev Esc Enferm USP. 2025 May 12;59:e20240181. doi: 10.1590/1980-220X-REEUSP-2024-0181en. eCollection 2025.

ABSTRACT

OBJECTIVE: The aim of this research was to examine the feasibility and effects of the “Rebuilding Myself” intervention to enhance adaptability of cancer patients to return to work.

METHODS: A randomized controlled trial with a two-arm, single-blind design was employed. The control group received usual care, whereas the intervention group received “Rebuilding Myself” interventions. The effects were evaluated before the intervention, mid-intervention, and post-intervention. The outcomes were the adaptability to return to work, self-efficacy of returning to work, mental resilience, quality of life, and work ability.

RESULTS: The results showed a recruitment rate of 73.17%, a retention rate of 80%. Statistically significant differences were found between the two groups in cancer patients’ adaptability to return to work, self-efficacy to return to work, mental resilience, and the dimension of bodily function, emotional function, fatigue, insomnia, and general health of quality of life.

CONCLUSION: “Rebuilding Myself” intervention was proven to be feasible and can initially improve cancer patients’ adaptability to return to work. The intervention will help provide a new direction for clinicians and cancer patients to return to work.

PMID:40354657 | DOI:10.1590/1980-220X-REEUSP-2024-0181en

J Med Internet Res. 2025 May 12;27:e67253. doi: 10.2196/67253.

ABSTRACT

BACKGROUND: Accurate mortality risk prediction is crucial for effective cardiovascular risk management. Recent advancements in artificial intelligence (AI) have demonstrated potential in this specific medical field. Qwen-2 and Llama-3 are high-performance, open-source large language models (LLMs) available online. An artificial neural network (ANN) algorithm derived from the SWEDEHEART (Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) registry, termed SWEDEHEART-AI, can predict patient prognosis following acute myocardial infarction (AMI).

OBJECTIVE: This study aims to evaluate the 3 models mentioned above in predicting 1-year all-cause mortality in critically ill patients with AMI.

METHODS: The Medical Information Mart for Intensive Care IV (MIMIC-IV) database is a publicly available data set in critical care medicine. We included 2758 patients who were first admitted for AMI and discharged alive. SWEDEHEART-AI calculated the mortality rate based on each patient’s 21 clinical variables. Qwen-2 and Llama-3 analyzed the content of patients’ discharge records and directly provided a 1-decimal value between 0 and 1 to represent 1-year death risk probabilities. The patients’ actual mortality was verified using follow-up data. The predictive performance of the 3 models was assessed and compared using the Harrell C-statistic (C-index), the area under the receiver operating characteristic curve (AUROC), calibration plots, Kaplan-Meier curves, and decision curve analysis.

RESULTS: SWEDEHEART-AI demonstrated strong discrimination in predicting 1-year all-cause mortality in patients with AMI, with a higher C-index than Qwen-2 and Llama-3 (C-index 0.72, 95% CI 0.69-0.74 vs C-index 0.65, 0.62-0.67 vs C-index 0.56, 95% CI 0.53-0.58, respectively; all P<.001 for both comparisons). SWEDEHEART-AI also showed high and consistent AUROC in the time-dependent ROC curve. The death rates calculated by SWEDEHEART-AI were positively correlated with actual mortality, and the 3 risk classes derived from this model showed clear differentiation in the Kaplan-Meier curve (P<.001). Calibration plots indicated that SWEDEHEART-AI tended to overestimate mortality risk, with an observed-to-expected ratio of 0.478. Compared with the LLMs, SWEDEHEART-AI demonstrated positive and greater net benefits at risk thresholds below 19%.

CONCLUSIONS: SWEDEHEART-AI, a trained ANN model, demonstrated the best performance, with strong discrimination and clinical utility in predicting 1-year all-cause mortality in patients with AMI from an intensive care cohort. Among the LLMs, Qwen-2 outperformed Llama-3 and showed moderate predictive value. Qwen-2 and SWEDEHEART-AI exhibited comparable classification effectiveness. The future integration of LLMs into clinical decision support systems holds promise for accurate risk stratification in patients with AMI; however, further research is needed to optimize LLM performance and address calibration issues across diverse patient populations.

PMID:40354652 | DOI:10.2196/67253

J Med Internet Res. 2025 May 12;27:e73758. doi: 10.2196/73758.

ABSTRACT

BACKGROUND: Redirecting avoidable presentations to alternative care service pathways (ACSPs) may lead to better resource allocation for prehospital emergency care. Stratifying emergency department (ED) presentations by admission risk using diagnosis codes might be useful in identifying patients suitable for ACSPs.

OBJECTIVE: We aim to cluster ICD-10 (International Statistical Classification of Diseases, Tenth Revision) diagnosis codes based on hospital admission risk, identify ED presentation characteristics associated with these clusters, and develop an exploratory classification to identify groups potentially suitable for ACSPs.

METHODS: Retrospective observational data from a database of all visits to the ED of a tertiary care institution for over 5 years (2016-2020) were analyzed. K-means clustering grouped diagnosis codes according to admission outcomes. Multivariable logistic regression was performed to determine the association of characteristics with cluster membership. ICD-10 codes were grouped into blocks and analyzed for cumulative coverage to identify dominant groups associated with lower hospital admission risk.

RESULTS: A total of 215,477 ambulatory attendances classified as priority levels 3 (ambulatory) and 4 (nonemergency) under the Patient Acuity Category Scale were selected, with a 17.3% (0.4%) overall admission rate. The mean presentation age was 46.2 (SD 19.4) years. Four clusters with varying hospital admission risks were identified. Cluster 1 (n=131,531, 61%) had the lowest admission rate at 4.7% (0.2%), followed by cluster 2 (n=44,347, 20.6%) at 19.5% (0.4%), cluster 3 (n=27,829, 12.9%) at 47.8% (0.5%), and cluster 4 (n=11,770, 5.5%) with the highest admission rate at 78% (0.4%). The four-cluster solution achieved a silhouette score of 0.65, a Calinski-Harabasz Index of 3649.5, and a Davies-Bouldin Index of 0.46. Compared to clustering based on ICD-10 blocks, clustering based on individual ICD-10 codes demonstrated better separation. Mild (odds ratio [OR] 2.55, 95% CI 2.48-2.62), moderate (OR 2.40, 95% CI 2.28-2.51), and severe (OR 3.29, 95% CI 3.13-3.45) Charlson Comorbidity Index scores increased the odds of admission. Tachycardia (OR 1.46, 95% CI 1.43-1.49), hyperthermia (OR 2.32, 95% CI 2.25-2.40), recent surgery (OR 1.31, 95% CI 1.27-1.36), and recent inpatient admission (OR 1.16, 95% CI 1.13-1.18) also increased the odds of higher cluster membership. Among 132 ICD-10 blocks, 17 blocks accounted for 80% of cluster 1 cases, including musculoskeletal or connective tissue disorders and head or lower limbs injuries. Higher-risk categories included respiratory tract infections such as influenza and pneumonia, and infections of the skin and subcutaneous tissue.

CONCLUSIONS: Most ambulatory presentations at the ED were categorized into low-risk clusters with a minimal likelihood of hospital admission. Stratifying ICD-10 diagnosis codes by admission outcomes and ranking them based on frequency provides a structured approach to potentially stratify admission risk.

PMID:40354643 | DOI:10.2196/73758

J Med Internet Res. 2025 May 12;27:e65397. doi: 10.2196/65397.

ABSTRACT

BACKGROUND: Domestic violence (DV) is a significant public health concern affecting the physical and mental well-being of numerous women, imposing a substantial health care burden. However, women facing DV often encounter barriers to seeking in-person help due to stigma, shame, and embarrassment. As a result, many survivors of DV turn to online health communities as a safe and anonymous space to share their experiences and seek support. Understanding the information needs of survivors of DV in online health communities through multiclass classification is crucial for providing timely and appropriate support.

OBJECTIVE: The objective was to develop a fine-tuned large language model (LLM) that can provide fast and accurate predictions of the information needs of survivors of DV from their online posts, enabling health care professionals to offer timely and personalized assistance.

METHODS: We collected 294 posts from Reddit subcommunities focused on DV shared by women aged ≥18 years who self-identified as experiencing intimate partner violence. We identified 8 types of information needs: shelters/DV centers/agencies; legal; childbearing; police; DV report procedure/documentation; safety planning; DV knowledge; and communication. Data augmentation was applied using GPT-3.5 to expand our dataset to 2216 samples by generating 1922 additional posts that imitated the existing data. We adopted a progressive training strategy to fine-tune GPT-3.5 for multiclass text classification using 2032 posts. We trained the model on 1 class at a time, monitoring performance closely. When suboptimal results were observed, we generated additional samples of the misclassified ones to give them more attention. We reserved 184 posts for internal testing and 74 for external validation. Model performance was evaluated using accuracy, recall, precision, and F₁-score, along with CIs for each metric.

RESULTS: Using 40 real posts and 144 artificial intelligence-generated posts as the test dataset, our model achieved an F₁-score of 70.49% (95% CI 60.63%-80.35%) for real posts, outperforming the original GPT-3.5 and GPT-4, fine-tuned Llama 2-7B and Llama 3-8B, and long short-term memory. On artificial intelligence-generated posts, our model attained an F₁-score of 84.58% (95% CI 80.38%-88.78%), surpassing all baselines. When tested on an external validation dataset (n=74), the model achieved an F₁-score of 59.67% (95% CI 51.86%-67.49%), outperforming other models. Statistical analysis revealed that our model significantly outperformed the others in F₁-score (P=.047 for real posts; P<.001 for external validation posts). Furthermore, our model was faster, taking 19.108 seconds for predictions versus 1150 seconds for manual assessment.

CONCLUSIONS: Our fine-tuned LLM can accurately and efficiently extract and identify DV-related information needs through multiclass classification from online posts. In addition, we used LLM-based data augmentation techniques to overcome the limitations of a relatively small and imbalanced dataset. By generating timely and accurate predictions, we can empower health care professionals to provide rapid and suitable assistance to survivors of DV.

PMID:40354642 | DOI:10.2196/65397

J Med Internet Res. 2025 May 12;27:e69657. doi: 10.2196/69657.

ABSTRACT

BACKGROUND: College students and young adults (18-25 years) frequently experience poor sleep quality, with insomnia being particularly prevalent among this population. Given the widespread use of digital devices in the modern world, electronic device-based sleep interventions present a promising solution for improving sleep outcomes. However, their effects in this population remain underexplored.

OBJECTIVE: We aimed to synthesize current evidence on the effectiveness of electronic device-based sleep interventions in enhancing sleep outcomes among college students and young adults.

METHODS: In total, 5 electronic databases (PubMed, CINAHL, Cochrane Library, Embase, and Web of Science) were searched to identify randomized controlled trials on digital sleep interventions. Sleep interventions, including cognitive behavioral therapy for insomnia, mindfulness, and sleep education programs delivered via web-based platforms or mobile apps, were evaluated for their effects on sleep quality, sleep parameters, and insomnia severity. Pooled estimates of postintervention and follow-up effects were calculated using Hedges g and 95% CIs under a random-effects model. Heterogeneity was assessed with I² statistics, and moderator and meta-regression analyses were performed to explore sources of heterogeneity. Evidence quality was evaluated using the Grading of Recommendations Assessment, Development, and Evaluations framework.

RESULTS: This study included 13 studies involving 5251 participants. Digital sleep interventions significantly improved sleep quality (Hedges g=-1.25, 95% CI -1.83 to -0.66; I²=97%), sleep efficiency (Hedges g=0.62, 95% CI 0.18-1.05; I²=60%), insomnia severity (Hedges g=-4.08, 95% CI -5.14 to -3.02; I²=99%), dysfunctional beliefs and attitudes about sleep (Hedges g=-1.54, 95% CI -3.33 to -0.99; I²=85%), sleep hygiene (Hedges g=-0.19, 95% CI -0.34 to -0.03; I²=0%), and sleep knowledge (Hedges g=-0.27, 95% CI 0.09-0.45; I²=0%). The follow-up effects were significant for sleep quality (Hedges g=-0.53, 95% CI -0.96 to -0.11; I²=78%) and insomnia severity (Hedges g=-2.65, 95% CI -3.89 to -1.41; I²=99%). Moderator analyses revealed several significant sources of heterogeneity in the meta-analysis examining the effects of digital sleep interventions on sleep outcomes. Variability in sleep quality was influenced by the sleep assessment tool (P<.001), intervention type and duration (P=.001), therapist guidance (P<.001), delivery mode (P=.002), history of insomnia (P<.001), and the use of intention-to-treat analysis (P=.001). Heterogeneity in insomnia severity was primarily attributed to differences in the sleep assessment tool (P<.001), while the effect size on sleep efficiency varied based on intervention duration (P=.02). The evidence quality ranged from moderate to very low certainty across measured outcomes.

CONCLUSIONS: Digital sleep interventions are effective in improving sleep quality and reducing insomnia severity, with moderate effects on dysfunctional beliefs and attitudes about sleep, sleep hygiene, and sleep knowledge. These interventions offer a viable approach to managing sleep problems in college students and young adults.

TRIAL REGISTRATION: PROSPERO CRD42024595126; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024595126.

PMID:40354636 | DOI:10.2196/69657

JMIR Form Res. 2025 May 12;9:e69138. doi: 10.2196/69138.

ABSTRACT

BACKGROUND: The growing prevalence of multiple chronic conditions (MCC) has significant impacts on health care systems and quality of life. Understanding the prevalence of MCC throughout adulthood offers valuable insights into the evolving burden of chronic diseases and provides strategies for more effective health care outcomes.

OBJECTIVE: This study estimated the prevalence and combinations of MCC among adult participants enrolled in the All of Us (AoU) Research Program, especially studying the variations across age categories.

METHODS: We conducted an exploratory analysis using electronic health record (EHR) data from adult participants (N=242,828) in the version 7 Controlled Tier AoU Research Program data release. Data analysis was conducted using Python in a Jupyter notebook environment within the AoU Researcher Workbench. Descriptive statistics included condition frequencies, the number of chronic conditions per participant, and prevalence according to age categories. The presence of a chronic condition was determined by documentation of one or more ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes for the respective condition. Age categories were established and aligned with diagnosis dates and stages of adulthood (early adulthood: 18-39 years; middle adulthood: 40-49 years; late middle adulthood: 50-64 years; late adulthood: 65-74 years; advanced old age: 75-89 years).

RESULTS: Our findings demonstrated that approximately 76% (n=183,753) of AoU participants were diagnosed with MCC, with over 40% (n=98,885) having 6 or more conditions and prevalence increasing with age (from 33.78% in early adulthood to 68.04% in advanced old age). The most frequently occurring MCC combinations varied by age category. Participants aged 18-39 years primarily presented mental health-related MCC combinations (eg, anxiety and depressive disorders; n=845), whereas those aged 40-64 years frequently had combinations of physical conditions such as fibromyalgia, chronic pain, fatigue, and arthritis (204 in middle adulthood and 457 in late middle adulthood). In late adulthood and advanced old age, hyperlipidemia and hypertension were the most commonly occurring MCC combinations (n=200 and n=59, respectively).

CONCLUSIONS: We report notable prevalence of MCC throughout adulthood and variability in MCC combinations by age category in AoU participants. The significant prevalence of MCC underscores a considerable public health challenge, revealed by distinct condition combinations that shift across different life stages. Early adulthood is characterized predominantly by mental health conditions, transitioning to cardiometabolic and physical health conditions in middle, late, and advanced ages. These findings highlight the need for targeted, innovative care modalities and population health initiatives to address the burden of MCC throughout adulthood.

PMID:40354632 | DOI:10.2196/69138

JMIR Mhealth Uhealth. 2025 May 12;13:e63022. doi: 10.2196/63022.

ABSTRACT

BACKGROUND: Exercise is a primary recommended treatment for knee osteoarthritis (KOA), as it helps alleviate symptoms and improves joint functionality. Personalized exercise programs, tailored to individual patient needs, have demonstrated promising results in maintaining physical fitness and enhancing overall well-being. In recent years, digital health applications have emerged as innovative tools for supervising and facilitating rehabilitation programs. Leveraging computer vision (CV) technology, these applications offer the potential to provide precise feedback and support personalized exercise interventions for patients with KOA in a scalable and accessible manner.

OBJECTIVE: This study aims to evaluate the impact of a CV-graded exercise intervention application over a 6-week period on clinical outcomes in patients with KOA . The outcomes were compared to those achieved through conventional exercise education by videos.

METHODS: A randomized controlled trial was conducted with 60 participants aged 60-80 years, recruited through community administrators between July 2023 and September 2023. Participants were randomly assigned to one of two groups: the graded exercise application group (n=32) and the exercise education brochure group (n=28). The primary outcomes assessed were short-term changes in pain, physical function, and stiffness as measured by the Western Ontario and McMaster Universities Arthritis Index (WOMAC). Secondary outcomes included assessments of participants’ affective state, self-efficacy, quality of life, and user experience.

RESULTS: The study recruited 60 participants, including 26 males and 34 females. Analysis revealed statistically significant improvements in physical function (P=.02) and self-efficacy (P=.04) in the graded exercise application group compared to the exercise education brochure group after the intervention. While improvements in pain and stiffness were observed in both groups, these changes were not statistically significant. In addition, participants in the graded exercise application group reported a positive user experience, highlighting the application’s usability and engagement features as beneficial to their rehabilitation process.

CONCLUSIONS: The findings suggest that the CV-based graded exercise intervention application effectively improves physical function and self-efficacy among patients with KOA . This digital tool demonstrates the potential to enhance the quality and personalization of exercise rehabilitation compared to traditional methods. Future studies should explore the application’s long-term efficacy and replicability in larger community-based populations, with a focus on sustained engagement and adherence to rehabilitation programs.

PMID:40354624 | DOI:10.2196/63022

JCO Oncol Pract. 2025 May 12:OP2400978. doi: 10.1200/OP-24-00978. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to determine if high-deductible health plan (HDHP) enrollment contributes to financial burden and hinders access to care for patients with multiple myeloma (MM).

MATERIALS AND METHODS: Patients diagnosed with MM from 2010 to 2020 were identified in Merative MarketScan, an employer-based health insurance database. Primary outcomes were total health care and out-of-pocket (OOP) costs in the year after diagnosis. Secondary outcomes included time to treatment initiation and stem-cell transplant receipt. Multivariable analyses using linear, logistic, and Cox regression were performed, as appropriate. Covariates included age, sex, year diagnosed, comorbidities, data provider, and stem-cell transplant receipt.

RESULTS: The cohort included 4,029 patients; 17.6% were enrolled on HDHPs. HDHP enrollees were younger (mean age, 54.9 v 55.5 years; P = .036). Over the first year, mean total and OOP costs were $406,401 in US dollars (USD) and $9,220 USD for HDHP enrollees, respectively, versus $386,802 USD (P = .027) and $7,021 USD (P < .001) for the standard plan enrollees. There was no statistically significant difference in total cost (β = 11; P = .999) but mean OOP costs were $2,544 USD (β = 2,544; P < .001) higher for HDHP enrollees after adjusting for covariates. The additional OOP costs incurred in the first 2 months, presumably because of deductibles, and after the deductible reset. Contrary to our hypothesis, HDHPs enrollees had shorter time to treatment initiation (median, 20 v 22 days; hazard ratio, 1.18; P < .001) and were more likely to receive a stem-cell transplant (55.1% v 47.6%; odds ratio, 1.25; P = .010), after adjusting for covariates.

CONCLUSION: Compared with standard plan enrollees, OOP costs were higher for HDHP enrollees in the year after diagnosis, but HDHP enrollment was not associated with delays in treatment initiation or reduced access to stem-cell transplant.

PMID:40354593 | DOI:10.1200/OP-24-00978