Leila Nikniaz

Since its emergence in Wuhan, China in December 2019, SARS-CoV-2 has triggered a global pandemic characterized by a wide spectrum of clinical manifestations. Diabetes mellitus (DM) holds a special place among the various underlying diseases associated with worse outcomes of COVID-19. Therefore, the present study was designed to investigate the clinical characteristics and chest CT findings of hospitalized COVID-19 patients in Marand, Iran, with a specific focus on determining whether pre-existing diabetes mellitus is associated with distinct patterns of clinical findings and pulmonary involvement. This retrospective descriptive-comparative study was conducted on 553 hospitalized COVID-19 patients at Ayatollah Koh Kamari Hospital in Marand County from March 2019 to March 2022. The material used was a questionnaire, including demographic, clinical, history of diabetes mellitus, laboratory, and radiological reports from the patients' records. The mean age of the patients was 61.99±1.44 years and 57.8 percent of the patients were women. The most common comorbidities were diabetes, hypertension, and dyslipidemia. CRP was elevated in 67.6 percent of the total patients. In the total patient population, 48.6 percent of patients showed pulmonary involvement on chest CT scan. The most common distribution pattern was peripheral (39.3%), and multilobular involvement (18.1%) was the most common anatomic location of involvement; followed by the left and right lower lobes. Ground glass opacity (GGO) parenchymal involvement was the most common in the total patient population (48.8%) and was significantly more common among diabetic patients (54.9% vs. 44.9%; P = 0.02). In contrast, consolidation involvement was significantly more common in nondiabetic patients (28.3% vs. 13.5%; P < 0.01). Right upper lobe and left lower lobe involvement were also more common in non-diabetic patients (P = 0.03 each), while crazy-paving pattern and multilobular distribution did not show significant differences between groups. Previously diagnosed diabetes mellitus was associated with distinct CT patterns in COVID-19, with GGO being predominant in diabetic patients and consolidation being more characteristic of non-diabetic patients. These findings may contribute to the risk stratification and clinical management of diabetic patients with COVID-19.

Oxidative stress is a key pathological mechanism of multiple sclerosis (MS) and α‐tocopherol is recognized as a primary chain‐breaking antioxidant. This study is aimed at investigating the relationship between serum α‐tocopherol and the pathogenesis of MS This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement and the Joanna Briggs Institute (JBI) manual for evidence synthesis. Scopus, PubMed, Web of Science, and Embase were searched in March 2025. The risk of bias was evaluated using the critical appraisal tool developed by JBI. All statistical analyses were performed with comprehensive meta‐analysis software (CMA3). Out of 506 records evaluated during the title/abstract screening phase, five case‐control studies were included. Studies encompassed a total of 389 participants, comprising 211 individuals diagnosed with MS and 178 healthy controls. The levels of α‐tocopherol were found to be lower in MS patients compared to the healthy group (standardized mean difference [SMD] = −1.17; 95% CI: −2.02, −0.31; p = 0.007, I ² = 91.5%, p < 0.001; predictive values: −3.13, 0.79). Although subgroup analyses were performed based on sample size, sex, and phase of the diseases, the source of heterogeneity was not found. Current evidence supports the possible association of serum α‐tocopherol and MS pathogenesis; however, considering the restricted number of included studies (five papers), the retrospective nature of the studies, and substantial heterogeneity in the conducted quantitative synthesis, multicenter and large‐scale prospective studies are recommended.

The prevalence of overweight and obesity among children and adolescents is increasing around the world, and childhood obesity is associated with health status in adulthood. Although active video games (AVGs) have been proposed as a promising strategy to increase physical activity in youth, evidence regarding their impact on obesity-related anthropometric outcomes and nutritional behaviors, particularly among overweight or obese children and adolescents, remains inconsistent. This systematic review aims to summarize the current knowledge regarding the effects of AVGs on anthropometric indices and nutritional behaviors among children and adolescents. A comprehensive systematic search was done on Web of Science, Scopus, PubMed, and Google Scholar databases without date restrictions until December 2025 by relevant keywords. All relevant original randomized controlled trials (RCTs) in English that evaluated the effects of AVGs on anthropometric indices and nutritional behaviors among children and adolescents were eligible for the present review. The quality of included RCTs was assessed by the Cochrane Handbook. The meta-analysis was conducted using the random model effect and STATA software. In total, 39 RCTs comprising 3232 children and adolescents were included in this review. The results of the meta-analysis indicated that playing with AVGs significantly reduced the zBMI (SMD = -0.033, 95% CI (-0.060, -0.007), p = 0.013), BMI (SMD = -0.744, 95% CI (-0.839, -0.649), p < 0.001), and body fat percentage (SMD = -0.516, 95% CI (-0.785, -0.248), p < 0.001) of children and adolescents, but not body weight (SMD = 0.269, 95% CI (-0.115, 0.654), p = 0.170) of them. Moreover, the subgroup analyses revealed that AVGs significantly decreased zBMI, BMI, and body fat percentage in the studies on adolescent and subjects aged > 11 years, with sample size ≥ 100, and intervention duration > 12 weeks. Furthermore, the RCTs reported that playing AVGs significantly increased energy expenditure among children and adolescents and could cause developing healthier consumption behaviors, but did not impact the food or energy intake of children and adolescents. The findings of the current review revealed that playing with AVGs could improve anthropometric indices especially among adolescents. However, more studies are required to confirm these achievements. The online version contains supplementary material available at 10.1186/s12887-026-06619-7.

Background: Vitamin D is a fat-soluble vitamin essential for various physiological functions. Its deficiency can lead to numerous health problems, making it a significant global health concern. However, limited data are available regarding its prevalence in East Azerbaijan province. This study aimed to assess the prevalence of vitamin D deficiency in this region and explore its association with demographic factors. Methods: This cross-sectional study was conducted in the urban and rural areas of East Azarbaijan province in 2018. A total of 1500 households from Tabriz, Marand, Bonab, Osku, Varzeghan, and Khodaafarin were examined. The socio-demographic questionnaires were completed, and serum vitamin D levels were measured using the ELISA method. The relationship between vitamin D status and demographic factors was analyzed using SPSS. P values less than 0.05 were considered statistically significant. Results: A total of 2816 individuals were investigated in the present study. Overall, more than 76.9% of the participants exhibited varying degrees of vitamin D deficiency, with a significantly higher prevalence in rural areas (64.6%) compared to Tabriz city (37.7%) (P0.05). Conclusion: Vitamin D deficiency is significantly more prevalent in rural areas than in Tabriz city, with a higher incidence in women compared to men. Recognizing this issue is crucial for implementing preventive measures. Further studies are needed to confirm these findings and elucidate the underlying mechanisms.

Background: Over the past two decades, it has been demonstrated that anxiety disorders are the most common mental disorders in the general population worldwide. This study aimed to provide a prevalence of how generalized anxiety disorder (GAD) within the population of East Azerbaijan province. The results of this research can contribute to improving the availability and quality of mental health care. Methods: A cross-sectional study was conducted from May to November 2018, involving 150 clusters of 10 households each, totaling 1500 households from Tabriz, Marand, Bonab, Osku, Varzeqan, and Khoda Afarin. A general questionnaire was utilized to assess key risk factors, including age, gender, marital status, education level, and employment status of the participants. The GAD-7 questionnaire was used to evaluate symptoms of GAD. Data were analyzed using SPSS software (v 18; SPSS, Inc., Chicago, IL). A chi-square test with a 95% confidence interval was applied, and P values less than 0.05 were considered statistically significant. Results: A total of 2816 people participated in this study. Approximately 40% of the surveyed individuals exhibited different levels of anxiety. The overall prevalence of generalized anxiety in Tabriz and surrounding cities was 39.8%. Tabriz exhibited a notably higher prevalence of generalized anxiety compared to other cities in the province (43.2% in Tabriz vs 36.8% in other cities, P=0.04). The prevalence of generalized anxiety was found to be remarkably (P<0.05) higher in women (46.6% in women vs. 32.4% in men). As individuals age, the occurrence of generalized anxiety tends to rise, peaking at the age of 55, after which it gradually decreases. However, the age group of 45-55 years exhibited the highest rates of generalized anxiety (43% in the 45-55 age group). The GAD levels did not show any significant variation in marital status (P<0.05). Conclusion: The results of this study indicate that being urban-dwelling, female, and aged 45-55 are significant factors affecting the prevalence of anxiety disorders. These findings highlight the need for targeted mental health interventions for at-risk groups.

Chest wall masses can be caused by trauma, infections, inflammation, or cancer. These tumors can be benign or malignant and originate from different structures of the chest wall. This systematic review aimed to investigate the histopathological characteristics of chest wall masses. The study followed the PRISMA guidelines. PubMed, Scopus, and Web of Science databases were searched for case series that reported the histopathological features of chest wall masses, without any restrictions on the date. The risk of bias was assessed using the JBI critical appraisal tools. Nine studies were included in the final review. Studies included a total of 1,279 patients with chest wall masses, with diverse age ranges. Biopsy methods such as fine-needle aspiration biopsy (FNAB), cutting needle biopsy, and surgical resection biopsy were used to evaluate the pathology of the masses. The rate of malignancy in chest wall masses varied depending on the biopsy method used, ranging from 27.12% in needle biopsy to 47.16% in surgical resection biopsy. The overall rate of malignancy in chest wall tumors was 31.27%. About one-third of the chest wall masses are malignant, emphasizing the importance of accurate diagnosis and appropriate treatment selection. Choosing the proper biopsy method is crucial for achieving successful outcomes and reducing mortality rates. Further research with larger sample sizes and improved reporting is needed to enhance our understanding of chest wall tumor pathology and improve patient outcomes.

Background: Dyslipidemia, characterized by variations in plasma lipid profiles, poses a global health threat linked to millions of deaths annually. Objectives: This study focuses on predicting dyslipidemia incidence using machine learning methods, addressing the crucial need for early identification and intervention. Methods: The dataset, derived from the Lifestyle Promotion Project (LPP) in East Azerbaijan Province, Iran, undergoes a comprehensive preprocessing, merging, and null handling process. Target selection involves five distinct dyslipidemia-related variables. Normalization techniques and three feature selection algorithms are applied to enhance predictive modeling. Result: The study results underscore the potential of different machine learning algorithms, specifically multi-layer perceptron neural network (MLP), in reaching higher performance metrics such as accuracy, F1 score, sensitivity and specificity, among other machine learning methods. Among other algorithms, Random Forest also showed remarkable accuracies and outperformed K-Nearest Neighbors (KNN) in metrics like precision, recall, and F1 score. The study’s emphasis on feature selection detected meaningful patterns among five target variables related to dyslipidemia, indicating fundamental shared unities among dyslipidemia-related factors. Features such as waist circumference, serum vitamin D, blood pressure, sex, age, diabetes, and physical activity related to dyslipidemia. Conclusion: These results cooperatively highlight the complex nature of dyslipidemia and its connections with numerous factors, strengthening the importance of applying machine learning methods to understand and predict its incidence precisely.