Narges Norouzkhani

Background: Mindfulness‐Based Relapse Prevention (MBRP) combines mindfulness practice with relapse‐prevention strategies and has demonstrated efficacy in improving psychological well‐being among individuals with substance use disorders. This pilot study evaluated quality‐of‐life changes following an audio‐based mobile MBRP application delivered as an adjunct to methadone maintenance treatment (MMT) among individuals with opioid use disorder (OUD). Materials and Methods: This single‐arm before–after study included 14 male participants with OUD receiving MMT at two addiction treatment clinics. Participants used the MBRP application for 8 weeks alongside routine care. Quality of life was assessed using the SF‐36 at baseline, post‐intervention, and 3‐month follow‐up. Session completion was monitored through weekly clinician‐verified contacts. Substance use and relapse status were monitored descriptively as secondary clinical indicators. Results: Mean SF‐36 scores increased from 40.34 ± 11.26 at baseline to 66.57 ± 13.39 post‐intervention and remained above baseline at follow‐up (57.76 ± 10.89). Significant improvements were observed from baseline to post‐intervention (mean difference = 26.23, 95% CI: 17.73–34.73, p < 0.001, Cohen's d z = 1.78) and from baseline to follow‐up (mean difference = 17.42, 95% CI: 11.84–22.99, p < 0.001, Cohen's d z = 1.80). Session‐completion rate was 92.9% (mean = 7.43 sessions per participant). At follow‐up, 11 participants (78.6%) remained free of documented illicit opioid use; three (21.4%) had documented relapse events, reported descriptively given the uncontrolled design. Conclusion: Participation in the mobile MBRP application was associated with quality‐of‐life improvements remaining above baseline at 3‐month follow‐up, supporting feasibility in routine addiction‐treatment settings. Randomized controlled trials with addiction‐specific outcomes, standardized acceptability instruments, and objective engagement analytics are needed to establish effectiveness.

Background and Aims: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, behavioral disturbances, and loss of functional independence. As the global prevalence of AD continues to rise, traditional episodic care models are increasingly inadequate for providing continuous and individualized support. Wearable electronic devices offer a potential alternative by enabling non‐invasive, real‐time monitoring in everyday settings. This systematic review synthesizes current evidence on the applications, reported outcomes, and implementation challenges of wearable and wearable‐adjacent technologies in AD care. Methods: A systematic literature search was conducted in September 2025 across five databases: PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar, using keywords and MeSH terms related to wearable electronic devices, smart glasses, smartwatches, and Alzheimer's disease. The review protocol was prospectively registered in PROSPERO (CRD420251158703). Two independent reviewers screened records and extracted data in accordance with PRISMA 2020 guidelines. Methodological quality was assessed using a validated 10‐item appraisal tool. Results: Of 1399 identified records, ten studies met the eligibility criteria. For structured synthesis, the included technologies were organized into four architectural categories: body area network‐based sensor systems, augmented reality smart glasses, hybrid wearable–mobile platforms, and wearable‐adjacent assistive technologies. Across studies, the included technologies were applied to physiological monitoring, mobility and gait assessment, cognitive and spatial support, and caregiver communication. Findings suggest preliminary feasibility across these domains; however, small sample sizes, heterogeneous outcome measures, and predominantly early‐phase study designs limit the strength of available evidence. Conclusion: Current evidence suggests that wearable and wearable‐adjacent technologies may contribute to multiple aspects of AD care, particularly in home‐based settings. Translation into routine practice requires prospective, adequately powered studies with standardized outcome frameworks, alongside regulatory alignment and patient–caregiver co‐design to address persistent usability barriers.

Background and Aims: Metabolic syndrome (MetS) is associated with increased risks of cardiovascular disease and type 2 diabetes, with recent global prevalence estimates of approximately 28%–31% in adults. Artificial intelligence (AI) and machine learning offer potential to improve risk stratification beyond conventional statistical methods. This systematic review aims to synthesize evidence on AI applications for the diagnosis and prediction of MetS, while assessing the limited evidence on extensions for prevention and management. Methods: Following PRISMA and SAMPL guidelines, a systematic literature search was conducted in 2025 in PubMed, Web of Science, Scopus, and Google Scholar without date restrictions. Search terms included “artificial intelligence,” “machine learning,” “metabolic syndrome,” “diagnosis,” “prediction,” “prevention,” and “management.” Only English‐language original studies were included. Titles/abstracts and full texts were screened independently by two reviewers. Extracted data comprised study characteristics, sample sizes, AI algorithms, feature types, performance metrics, and key findings. Results: Of 1178 identified records, 64 full‐text articles were assessed for eligibility; 12 studies ( n = 12/64) met inclusion criteria. The majority addressed diagnostic or predictive tasks using algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), k‐nearest neighbors (KNNs), and random forests (RFs). Most models utilized non‐invasive features and reported area under the curve (AUC) values ranging from 0.82 to 0.99. Evidence for long‐term management or behavioral prevention was present in only a minority of studies ( n = 3/12) and remained largely exploratory. Across the reviewed literature, formal reporting of uncertainty measures (e.g., 95% confidence intervals) and model explainability techniques (e.g., SHAP, permutation importance) was consistently absent. Conclusion: AI demonstrates promise for non‐invasive diagnosis and risk prediction of MetS. However, applications in prevention and personalized management remain underdeveloped. Realizing the full clinical potential of AI will require rigorous external validation, enhanced model explainability, and educational promotion to bridge the current implementation gap.

Machine learning (ML) applied to diffusion tensor imaging (DTI) has emerged as a promising tool for detecting microstructural brain alterations in movement

Background and aim: Intraoperative hypotension is one of the most common surgical adverse effects, often associated with postoperative organ damage and complications. The use of non-invasive methods to predict hypotension during surgery could significantly reduce these complications. Recently developed machine learning algorithms show promise for improving the prediction of hypotension and enhancing detection during surgical procedures. Our meta-analysis aimed to evaluate the clinical performance of machine learning algorithms for predicting intraoperative hypotension. Method: A systematic search of PubMed, Scopus, Web of Science, and Google Scholar was conducted to retrieve articles published up to August 2024. Two independent researchers screened observational accuracy studies and also identified randomized controlled trials (RCTs) of AI-guided intraoperative management. Observational studies reporting diagnostic/prognostic accuracy metrics were included in the quantitative synthesis. After evaluating the quality of the studies, data extraction and analysis were accomplished. Results: Five observational studies, including a total of 42,509 participants, were included in this study. Four studies were conducted in South Korea, and one in Taiwan. The machine learning algorithms applied in these studies for electrocardiography, basal systolic, and diastolic blood pressure. These studies assessed model performance based on sensitivity, specificity, and the area under the ROC curve (AUROC). The overall effect size, represented in a forest plot, showed specificity of 0.73 (95% CI: 0.67–0.79), sensitivity of 0.75 (95% CI: 0.71–0.79), and an AUROC of 0.83 (95% CI: 0.77–0.89). In addition, we identified RCTs evaluating AI/HPI-guided intraoperative management, due to heterogeneous endpoints. Conclusion: Machine learning algorithms demonstrate acceptable performance and advantages in predicting intraoperative hypotension during surgical procedures. The application of these algorithms could be beneficial in future surgeries to reduce complications.

Background and Aims: Electronic prescribing (e‐prescribing) has emerged as a vital strategy to enhance medication safety, reduce healthcare errors, and streamline clinical workflows. However, its success is largely dependent on user acceptance and satisfaction. This descriptive, exploratory cross‐sectional study evaluated physicians' satisfaction with the Social Security Organization's electronic prescription system in two major cities of northeastern Iran—Mashhad and Sabzevar—where the system has been widely implemented in outpatient settings. Methods: A descriptive cross‐sectional study was conducted in 2021 involving 60 physicians selected via convenience sampling. Data were collected through a 36‐item researcher‐designed questionnaire, which assessed satisfaction with system performance, training quality, technical support, and expected benefits. The questionnaire demonstrated strong reliability (Cronbach's alpha = 0.918) and was validated by a panel of health IT experts and physicians. Data were analyzed using SPSS version 21. Group comparisons and correlation analyses were performed based on experience level, system usage duration, and self‐reported computer proficiency. Results: Respondents were predominantly 30–39 years old and reported high levels of digital literacy and motivation to adopt new technologies. Satisfaction was particularly high in areas such as prescription error reduction and the ability to transmit prescriptions to pharmacies electronically. However, a significant proportion of physicians expressed dissatisfaction with system speed, particularly with login and data entry times, and with the lack of adequate training. This led to a notable contradiction where 46.7% of participants acknowledged the system's effectiveness in reducing errors, yet the same proportion indicated they would not recommend it to colleagues. Expectations for improved patient care, workflow integration, and access to medication histories were also emphasized. Conclusion: Although the system demonstrated key functional benefits, significant limitations in technical performance, interface usability, and user support infrastructure hindered full acceptance. These findings offer initial, context‐specific insights and may serve as a foundation for broader, multi‐regional evaluations of e‐prescribing systems in Iran. Addressing these shortcomings through targeted training, interface optimization, and performance enhancements is critical to advancing physician satisfaction and long‐term system integration.

Background and Aim: Numerous studies on the impact of weight changes on nonalcoholic fatty liver disease are being conducted; therefore, this systematic review aims to critically discuss the impact of change, fluctuation, or variability in weight on the risk of nonalcoholic fatty liver disease in the general population. Methods: Data from three databases, PubMed/Medline, Google Scholar, and Scopus, which were available until June 2024, were used to compile the materials for our research. Only English‐language publications were submitted for this study, and they were evaluated based on their titles, abstracts, and complete texts and. Result: Eight studies (three cross‐sectional articles and five cohort articles) involving 147,601 participants from Japan, China, Korea, and the United States were included. The results of this study showed that weight loss had significantly increased odds of developing NAFLD independently and per 1 kg compared to the stable weight control group. (OR = 1.16, 95% CI: 1.118–1.208, p < 0.0001, I ² = 86%, OR = 1.186, 95% CI: 1.142–1.230, p < 0.0001, I ² = 89.2%). The findings of seven articles indicated that the odds of experiencing NAFLD among patients with increased weight was 0.697 (OR = 0.697, 95% CI: 0.391–1.002, p < 0.0001, I ² = 99.9%), suggesting a slightly reduced likelihood compared to the control group. The sensitivity analysis supported the robustness of the findings. Conclusion: Nonalcoholic fatty liver disease is increasing in industrialized nations and has a significant financial impact on people′s health and healthcare systems. It can be controlled by emphasis on reducing weight change and maintaining a healthy weight.

Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancers by analyzing genetic mutation profiles. Their application in prognosis remains underexplored. This systematic review and meta-analysis aim to evaluate the effectiveness of AI-based models, which refers to systems utilizing artificial intelligence to analyze data and make predictions, in predicting immunotherapy responses in gastrointestinal cancers using genetic mutation features. This study, adhering to PRISMA guidelines, aimed to evaluate AI networks for predicting gastrointestinal cancer prognosis in response to immunotherapy using genetic mutation features. A search in PubMed, WOS, and Scopus identified relevant studies. Data extraction and quality assessment were conducted, and statistical analysis included pooled estimates for sensitivity, specificity, accuracy, and AUC. Regression models and imputation methods addressed missing values, ensuring accurate and robust results. STATA version 18 was used to analyze the data. A total of 45 studies, all published in 2024, involving 14,047 participants in training sets and 10,885 participants in test sets, were included. The pooled results of AI model performance for gastrointestinal cancers based on genetic mutation features were: AUC = 0.86 (95% CI: 0.86–0.87), Sensitivity = 83% (95% CI: 83%-84%), Specificity = 72% (95% CI: 72%-73%), and Accuracy = 82% (95% CI: 82%-83%). Heterogeneity was low to moderate, and no publication bias was detected. Subgroup analysis showed higher AUC for gastric cancer models (AUC: 0.87) and lower for pancreatic cancer models (AUC: 0.52). AI networks demonstrate promising potential in predicting immunotherapy outcomes for gastrointestinal cancers based on genetic mutation features. This systematic review highlights their effectiveness in stratifying patients and optimizing treatment decisions. However, further large-scale studies are needed to validate AI models and integrate them into clinical practice for improved precision in cancer immunotherapy.

Mahdie Shojaei Baghini1, Mehdi Mohammadi1 and Narges Norouzkhani2 Author informationPDFCitations 1Information Management, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, IRAN 2Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IRAN Corresponding author. Correspondence: Narges Norouzkhani Department of Medical Informatics, Faculty of Medicine,

Background: Self-management education resources for inflammatory bowel disease (IBD) using concepts remain infrequent. We aim to describe the development and evaluation process of educational material for self-management in IBD based on patient preferences and expert opinions. Research design and methods: The method of this study includes two main phases of development and validation in five steps in the following order: (1) identification of information needs for patients with IBD; (2) content development with a comprehensive literature review and scientific texts related to IBD; (3) measuring the face validity of the content based on the expert opinions in the field of IBD; (4) validation of the content with the experts in the field of IBD; and (5) validation by target audiences. Results: The expert panel comprises ten gastroenterologists, nutritionists, psychologists, gynecologists, and nurses. The total suitability score is 79.5%. The final draft version of the educational self-management material was presented to 30 IBD patients who were satisfied (n = 24; 80%) with the material. Conclusions: This study shows the development process and is validated for face and content validity by the academic multidisciplinary expert panel and target group. Patients and their caregivers can use this content to cope with their disease.