Medical Journal of the Islamic Republic of Iran最新文献

{"title":"Cardiovascular Risk Factors and Demographic Characteristics in Iranian Adults with Noise-Induced Hearing Impairment: A Cross-Sectional Study.","authors":"Mahdi Soheyli, Mobin Obeidinia, Arya Shirani, Lena Nazemi, Zahra Rampisheh, Hajar Nikbakht, Mahdi Chinichian, Batool Tayefi","doi":"10.47176/mjiri.40.23","DOIUrl":"10.47176/mjiri.40.23","url":null,"abstract":"<p><strong>Background: </strong>Hearing loss (HL), including noise-induced hearing loss (NIHL), is a prevalent health issue that affects millions of individuals worldwide and ranks as the third most common disability. This study examines the relationship between HL and cardiovascular risk factors (RFs) such as hypertension (HTN), diabetes mellitus (DM), and dyslipidemia (DLP) within the Iranian adult population.</p><p><strong>Methods: </strong>This study was carried out at Iran University of Medical Sciences and involved 1,996 individuals undergoing routine health checks, including audiometry and lab tests.</p><p><strong>Results: </strong>The study indicated that older age (per each additional year of age, aOR=1.14, 95%CI: 1.12-1.16, <i>P</i><0.001) and male gender (aOR=3.08, 95%CI: 1.84-5.14, <i>P</i><0.001) were significantly associated with HL, while a higher educational level served as a protective factor (tertiary education compared to a primary school degree, aOR=0.35, 95%CI: 0.24-0.5, <i>P</i><0.01). Although initial analyses suggested associations between HL and cardiovascular RFs, these associations lost significance after adjusting for confounding variables. Notably, the presence of a single cardiovascular RF was significantly linked to HL in the total sample (aOR=1.37, 95%CI: 1.04-1.81, <i>P</i>=0.02) and in men (aOR=1.41, 95%CI: 1.05-1.90, <i>P</i>=0.02), but not in women.</p><p><strong>Conclusion: </strong>This research emphasizes the significant association between hearing loss and demographic factors such as age, gender, and educational attainment in Iranian adults. These findings underscore the necessity for inclusive hearing health strategies that take these factors into account in both occupational and public health contexts.</p>","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"23"},"PeriodicalIF":0.0,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13403713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consumer Health Information: A Narrative Review.","authors":"Mohammad Javad Dehnavi, Nooshin Peyman, Mohammad Javad Mansourzadeh, Paria Amiri, Seyed Hojjat Khatamifar, Fatemeh Sheikhshoaei","doi":"10.47176/mjiri.40.22","DOIUrl":"10.47176/mjiri.40.22","url":null,"abstract":"<p><strong>Background: </strong>Consumer Health Information (CHI) encompasses the dissemination of information and the cultivation of appropriate attitudes toward healthcare, as well as specific professional skills, aimed at altering behaviors and enhancing the health status of consumers. The aim of this study was to introduce the concept, features, and associated issues of CHI, provide an overview of current research, and examine the information-seeking behavior of health information consumers.</p><p><strong>Methods: </strong>On February 28, 2025, the keywords \"consumer health information,\" \"health literacy,\" and their related terms were searched in PubMed, Web of Science, Google Scholar, and grey literature sources. Out of 1,058 retrieved records, 55 met the inclusion criteria and were analyzed to extract the relevant characteristics.</p><p><strong>Results: </strong>The findings of this study indicate that consumer health information (CHI) is a dynamic concept that encompasses the provision of information and the enhancement of skills related to healthcare, with the aim of influencing behavior and improving consumers' health. A review of the existing literature demonstrates that the evaluation components of this concept encompass a range that includes comprehensiveness, credibility, readability, and usefulness. Furthermore, the Internet, social media, and artificial intelligence tools serve as the primary platforms for searching for and accessing such information. The results also suggest that challenges such as misinformation, information overload, and limitations in health literacy significantly influence information-seeking behavior, underscoring the necessity for increased attention to the role of librarians as trusted information intermediaries.</p><p><strong>Conclusion: </strong>The necessity of disseminating accurate and reliable health information tailored to consumers' needs is underscored by the increasing trend of scientific output in this domain in recent years. It is essential to develop standard guidelines and regulations, as well as to create appropriate infrastructure for CHI at the macro level. Furthermore, conducting economic studies to evaluate the return on investment and utility of CHI can aid policymakers in their decision-making processes.</p>","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"22"},"PeriodicalIF":0.0,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13404470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epigenetic Mechanisms of Suicidal Behavior Formation: A Review.","authors":"Aruzhan Tussupova, Roza Tatayeva, Sholpan Koygeldinova, Zhannat Bazarbayeva, Zhibek Sembaeva, Aiman Mussina","doi":"10.47176/mjiri.40.21","DOIUrl":"10.47176/mjiri.40.21","url":null,"abstract":"<p><strong>Background: </strong>Suicidal behavior is a complex and multifactorial phenomenon rooted in both psychological and biological mechanisms. In recent years, there has been an increasing focus on epigenetic factors, which modulate the influence of environmental factors on the expression of genes associated with emotional and cognitive regulation.</p><p><strong>Review of the literature: </strong>This review aims to provide a comprehensive analysis of contemporary scientific data concerning the epigenetic mechanisms implicated in the pathogenesis of suicidal behavior. It synthesizes and systematizes findings published between 2013 and 2024, emphasizing DNA methylation, histone modifications, and non-coding RNA (microRNA and long non-coding RNA) regulation as key molecular pathways involved in impaired neuroplasticity, dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, and dysfunction of neurotransmitter systems.</p><p><strong>Discussion: </strong>The review highlights the role of epigenetic regulation in genes associated with the serotonergic system (SLC6A4), neuroendocrine stress response (FKBP5), and HPA axis regulation (SKA2, NR3C1, NR3C2), as well as relatively novel candidate genes such as CACNA1C, SIRT1, and IMPA2. It underscores how stress-induced and trauma-related epigenetic modifications contribute to suicidal vulnerability and may underlie the biological heterogeneity observed among individuals exhibiting suicidal behavior. The potential reversibility of epigenetic changes positions them as attractive targets for therapeutic and preventive strategies.</p><p><strong>Conclusion: </strong>Epigenetic changes induced by environmental stressors, traumatic experiences, or mental disorders possess diagnostic and prognostic potential. The integration of epigenetic biomarkers into clinical research may enhance the early identification of individuals at risk, support the development of personalized interventions, and contribute to new approaches in suicide prevention. Overall, epigenetics offers a promising framework for bridging molecular biology with psychiatry and deepening our understanding of the biological mechanisms underlying suicidality.</p>","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"21"},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13403714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Retracted Publications in the Diabetes Literature.","authors":"Somayeh Parvin, Mahshid Lotfi, Mohsen Nouri","doi":"10.47176/mjiri.40.20","DOIUrl":"10.47176/mjiri.40.20","url":null,"abstract":"<p><strong>Background: </strong>Retraction serves as a critical corrective mechanism in scientific publishing, preventing the spread of flawed or misleading data. This study aimed to systematically analyze the characteristics of retracted articles within the field of diabetes research, highlight potential vulnerabilities in the integrity of diabetes research, and address gaps in understanding the nature and implications of research misconduct in this crucial medical discipline.</p><p><strong>Methods: </strong>This study aimed to analyze retracted publications in the field of diabetes published from 1978 to 2024 using the Web of Science database. Information from the selected articles was obtained through the Retraction Watch Database. Extracted details included the journal title, type of article, country of origin, publication and retraction dates, number of authors, and reasons for retraction. The collected data were analyzed using descriptive statistical methods.</p><p><strong>Results: </strong>Between 1978 and 2024, our review revealed 316 retracted articles. The journals Science (n = 1517) and The Lancet (n = 742) accounted for the highest number of these highly cited retractions, while the journals Biomed Research International (n = 31) and Diabetes (n = 15) accounted for the most cited of these retractions. The most common reasons for retraction were investigations initiated by the journal or publisher and concerns related to data integrity. The years with the highest number of retractions were 2023 (n = 91) and 2016 (n = 40), whereas the majority of retracted articles were initially published in 2022 and 2021 (n = 26). Collaborative research involving authors from China (n = 137), the United States (n = 71), and India (n = 24) was most frequently associated with these retractions.</p><p><strong>Conclusion: </strong>Safeguarding the integrity of scientific literature, particularly in high-stakes fields such as diabetes research, requires a fundamental shift from reactive retractions to multilateral proactive systemic reform. Cultivating an ethical research culture (changing incentives to reward research quality and transparency over quantity by Institutions and funders); empowering researchers (providing the skills for rigorous and ethical research through mandatory, ongoing ethics training); and ensuring vigilant publishing (enforcing clear policies, rapidly investigating issues, and performing transparent retractions to protect the scientific record by journals).</p>","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"20"},"PeriodicalIF":0.0,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13404472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Tradition to Transformation: Policy Solutions for Advancing Medical Education Toward Entrepreneurial Universities.","authors":"Mohammad Mahboubi, Taha Nasiri, Sadra Taheri Maleki, Majid Ghorbanniya Delavar, Babak Rastegarimehr","doi":"10.47176/mjiri.40.19","DOIUrl":"10.47176/mjiri.40.19","url":null,"abstract":"<p><strong>Background: </strong>The higher education system is undergoing a significant transformation, shifting from its traditional role of knowledge production to embracing the concept of entrepreneurial universities. This shift aims to align with global development processes by not only generating knowledge and ideas but also translating them into actionable outcomes. In this article, we explore solutions toward (Third-Generation University) Entrepreneurial Universities and propose policy solutions to facilitate this transition.</p><p><strong>Methods: </strong>This study adopts a qualitative approach, utilizing an analytical research framework for policy development. The research aims to identify \"policy solutions for transforming to third-generation universities by implementing changes in medical education.\" The study was conducted in three stages: a comprehensive literature review, the Delphi method, and the Policy dialogue (policy discussions) to finalize policy options.</p><p><strong>Results: </strong>Several solutions were identified, including the development of support models for entrepreneurial activities, structural and organizational reforms, capacity-building for medical sciences universities based on regional needs, and fostering mutual and effective collaboration with industry. Among these, three key policy options were prioritized: (1) Revising and reforming medical education programs, (2) Formulating support models for entrepreneurial activities, and (3) Restructuring the functional framework of universities.</p><p><strong>Conclusion: </strong>The process of driving change is multi-faceted and time-intensive. To ensure success, changes and innovative approaches should be introduced through educational programs and structured curricula. Implementing effective change models and providing necessary support to students across various medical science disciplines are critical steps in this transformation.</p>","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13403711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic Accuracy of Artificial Intelligence in Predicting Admission Status, Intensive Care Requirements, and Mortality in the Emergency Department: A Systematic Review and Meta-Analysis.","authors":"Seyed Mohammad Hosseini Kasnavieh, Seyed Hossien Shaker, Maryam Milanifard, Roxana Hessam, Ali Saghandian Tousi, Marjan Ghadesi","doi":"10.47176/mjiri.40.18","DOIUrl":"10.47176/mjiri.40.18","url":null,"abstract":"<p><strong>Background: </strong>Predicting patient outcomes in the emergency department is crucial for effective resource management and enhancing the quality of care. Recent advancements in artificial intelligence have facilitated accurate predictions of patient outcomes; however, consistent evidence regarding the diagnostic accuracy of these models in the emergency department remains limited. Therefore, the aim of the present study was to evaluate the diagnostic accuracy of artificial intelligence in predicting admission status, intensive care requirements, and in-hospital mortality in the emergency department.</p><p><strong>Methods: </strong>In the present study, the PubMed, Embase, Cochrane Library, and Web of Science databases were searched from January 2020 to November 2025 using targeted keywords. A total of 34 relevant studies were included in the analysis. Meta-analysis was conducted using Stata v.17 software.</p><p><strong>Results: </strong>The overall diagnostic sensitivity and specificity of the models for predicting admission were 0.77 (95% CI, 0.60-0.93) and 0.78 (95% CI, 0.62-0.95), respectively. For critical care, sensitivity was 0.85 (95% CI, 0.57-1.00) and specificity was 0.86 (95% CI, 0.57-1.00). For mortality, sensitivity was 0.83 (95% CI, 0.60-1.00) and specificity was 0.90 (95% CI, 0.67-1.00).</p><p><strong>Conclusion: </strong>Artificial intelligence models, encompassing both machine learning and deep learning, serve as effective tools for predicting the conditions of emergency patients. The findings indicate that AI holds significant potential to enhance clinical decision-making within the emergency department.</p>","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13404239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elastofibroma Dorsi: Demographic, Clinical, and Radiological Assessment: A Retrospective Study.","authors":"Kaveh Samimi, Peyman Kamali Hakim, Fahimeh Zeinalkhani, Hadise Zeinalkhani, Mohammad Ghaffari, Nasrin Ahmadinejad, Hirbod Nasiri Bonaki, Sina Delazar, Hamid Rajabi","doi":"10.47176/mjiri.40.17","DOIUrl":"10.47176/mjiri.40.17","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Elastofibroma dorsi (ED) is a rare, slow-growing benign soft tissue mass that typically develops beneath the scapula. It often remains asymptomatic and is incidentally discovered during imaging conducted for other reasons. When symptoms do manifest, they are generally mild and associated with shoulder movement. Due to the potential for ED to mimic more serious conditions on imaging, it is crucial to recognize its characteristic features. This study aimed to evaluate the demographic, clinical, and imaging characteristics of ED in a cohort of patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This retrospective observational study was conducted using imaging and clinical records archived at a single radiology center. Data were collected from two distinct periods (2013-2016 and 2022-2024) to enhance the study's statistical power and achieve a larger sample size. Scans were included if both the scapular and axillary regions were clearly visible and if complete clinical data were available. Patients with poor image quality, incomplete records, or a history of scapular surgery or malignancy were excluded. Three experienced radiologists independently evaluated the images for predefined features of ED. Cases with disagreement or atypical findings were referred for biopsy. Clinical data, including age, sex, handedness, BMI, symptoms, and occupation, were recorded. Lesion characteristics were quantitatively assessed, and statistical tests were employed to examine associations between clinical variables, lesion features, and interobserver agreement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The prevalence of ED was 1.1% based on 13,042 imaging records. The majority of patients (69%) were women. The overall mean age ± SD was 62.7 ± 7.3 years, with the highest frequency observed in individuals over 50 years of age. Among right-handed patients, 82% (n=112) had unilateral lesions on the right side, while 75% (n=6) of left-handed patients exhibited unilateral lesions on the left side. A total of 63.3% (n=91) of the patients were asymptomatic, with the prevalence of symptoms significantly higher in women (43.4%) compared to men (22.2%). The overall mean lesion size was 55 × 36 mm, which was significantly larger in the heavy manual labor group (mean area = 3060 mm2, SD=450 mm2, &lt;i&gt;P&lt;/i&gt;&lt;0.05). In the subgroup analysis of bilateral lesions, the dominant side demonstrated a significantly larger mean lesion area than the non-dominant side (1536 mm2 vs. 1120 mm2, &lt;i&gt;P&lt;/i&gt;&lt;0.05). Furthermore, we found that symptomatic patients also had a markedly higher mean lesion area (2257 mm2 vs. 1705 mm2). The mean ± SD HU of the lesions was 33 ± 4.1 (range, 19-48), compared to 46 ± 3.7 (range, 35-55) for the adjacent muscle.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Elastofibroma dorsi is more prevalent among women. Most patients remain asymptomatic, and the majority of lesions present unilaterally, in accordance with handedness. Occupational physical activity levels were p","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13404395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Processing for Diagnosing Psoriasis: A Machine Learning Approach to Classify Skin Lesions into Psoriasis Subtypes.","authors":"Hoorie Masoorian, Marsa Gholamzadeh, Alireza Firooz, Reza Safdari","doi":"10.47176/mjiri.40.16","DOIUrl":"10.47176/mjiri.40.16","url":null,"abstract":"<p><strong>Background: </strong>Psoriasis is a chronic autoimmune skin condition that affects 2-3% of the global population and manifests in various subtypes, including plaque, guttate, inverse, pustular, and erythrodermic psoriasis. Accurate subtype differentiation is crucial for effective treatment, but traditional diagnostic methods are time-consuming and prone to observer variability. This study aims to develop a machine learning model that classifies psoriasis lesions into the five primary subtypes using convolutional neural networks (CNNs) and transfer learning, offering a scalable tool to assist clinicians in diagnosing psoriasis and making informed treatment decisions.</p><p><strong>Methods: </strong>This is a methodological-developmental study that develops and evaluates a deep learning model for psoriasis subtype classification. The dataset was obtained from from Kaggle, applying image augmentation techniques (rotation, translation, shearing, flipping, zoom) to enhance dataset diversity. A pre-trained Visual Geometry Group 16-layer architecture (VGG16) model was used for feature extraction, with a custom classification head added, incorporating ReLU-activated dense layers and dropout regularization to mitigate overfitting. The model was trained and evaluated using accuracy and loss metrics, with early stopping and model checkpointing for optimization.</p><p><strong>Results: </strong>The model achieved 96% accuracy on the training dataset and 90% on the test dataset, demonstrating strong generalization. A confusion matrix analysis confirmed accurate differentiation between the five subtypes.</p><p><strong>Conclusion: </strong>This study developed a deep learning model that accurately classifies psoriasis subtypes, utilizing CNNs and transfer learning. The model was integrated into a web-based tool, providing real-time diagnostic assistance for clinicians. This AI-driven system has the potential to enhance diagnostic accuracy, improve clinical workflows, and offer scalable solutions for psoriasis management, particularly in areas with limited access to dermatologists.</p>","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13403715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Mental Health Literacy Among Iranian Middle School Students: A Quasi-Experimental Intervention.","authors":"Zahra Saboohi, Ava Sadat Hoseini, Azade Sadat Hoseini, Sadegh Ghasemzadeh Rishkani, Mahnaz Solhi","doi":"10.47176/mjiri.40.15","DOIUrl":"10.47176/mjiri.40.15","url":null,"abstract":"<p><strong>Background: </strong>Stigma and a lack of knowledge are significant barriers to seeking mental health support, particularly among adolescents. In Iran, there is a scarcity of interventional studies addressing these issues, and the findings from existing research are often inconsistent. This study translated and culturally adapted the Canadian Guidance about Mental Health and the High School Curriculum for implementation in Iranian schools to address this gap. This study aims to determine the effect of this educational intervention on the mental health literacy of Iranian students.</p><p><strong>Methods: </strong>This research employed a quasi-experimental design involving a sample of two hundred middle school children aged 14.28 ± 0.7 years, ranging from 13 to 16 years old. The educational intervention for the experimental group consisted of six sessions, each lasting 45 to 60 minutes. Data were collected using the Knowledge and Attitudes to Mental Health Scales (KAMHS). The data were analyzed using inter-group and intra-group repeated measures ANOVA, with a significance cutoff of <i>P</i><0.05.</p><p><strong>Results: </strong>There were no significant differences in demographic characteristics between the intervention and control groups. However, the intervention group demonstrated significant improvements in mental health behaviors, a reduction in stigma, and an increase in help-seeking, while the control group showed no such changes (<i>P</i><0.05).</p><p><strong>Conclusion: </strong>The designed intervention was effective in increasing knowledge and attitudes.</p>","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13404075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Academic Influencers on X (former Twitter) and Their Impact on Research Metrics: A Narrative Review and Correlation Analysis of Top 100 Altmetric Articles (2017-2021).","authors":"Mashallah Torabi, Fatemeh Hadavandsiri, Houman Kazemzadeh, Maryam Goodarzi","doi":"10.47176/mjiri.40.14","DOIUrl":"10.47176/mjiri.40.14","url":null,"abstract":"<p><strong>Background: </strong>Social media platforms, particularly X (former Twitter), are increasingly used by researchers to disseminate scientific knowledge. However, the extent to which X engagement influences traditional academic impact remains unclear. This study examines the association between X mentions, Altmetric Attention Scores (AAS), and citation counts for highly visible research outputs.</p><p><strong>Methods: </strong>A narrative review of studies on academic influencers in medical fields was combined with a quantitative correlation analysis of the top 100 Altmetric articles (2017-2021). Data included AAS, X mentions, and citation counts from Dimensions. Pearson correlation coefficients were calculated to assess year-by-year relationships. A systematic search was conducted in PubMed and Google Scholar on August 3, 2024, using MeSH terms and Boolean operators.</p><p><strong>Results: </strong>Strong positive correlations were found between X mentions and AAS (r = 0.673-0.944, <i>P</i> < 0.05). Correlations between AAS and citations were weaker and variable (r = 0.075-0.504, <i>P</i> < 0.05 in 2020 and 2021). The narrative review revealed mixed associations between social media influence and academic productivity across specialties.</p><p><strong>Conclusion: </strong>X amplifies research visibility but has limited predictive power for long-term citation impact. These findings inform research evaluation by highlighting altmetrics as complementary tools. Future studies should explore multi-platform data and address global disparities in digital influence.</p>","PeriodicalId":18361,"journal":{"name":"Medical Journal of the Islamic Republic of Iran","volume":"40 ","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13404390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148605100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}