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Background: Metabolic syndrome (MetS) is a cluster of risk factors and the Framingham risk score (FRS) is a useful metric for measuring the 10-year cardiovascular disease (CVD) risk of the population. The present study aimed to determine the 10-year risk of cardiovascular disease using the Framingham risk score in people with and without MetS in a large Iranian cohort study. Methods: This cross-sectional study was done using the Fasa cohort. Participants aged ≥ 35 years old were recruited to the study from 2015 to 2016. The FRS was calculated using age, sex, current smoking, diabetes, systolic blood pressure (SBP), total cholesterol, and high-density lipoprotein (HDL) cholesterol. MetS was defined as the presence of three or more of the MetS risk factors including triglyceride (TG) level ≥ 150 mg dl− 1, HDL level < 40 mg dl− 1 in men and < 50 mg dl− 1 in women, systolic/diastolic blood pressure ≥ 130/≥85 mmHg or using medicine for hypertension, fasting blood sugar (FBS) level ≥ 100 mg dl− 1 or using diabetes medication and abdominal obesity considered as waist circumference (WC) ≥ 88 cm for women and ≥ 102 cm for men. Multiple logistic regressions were applied to estimate the 10- year CVD risk among people with and without MetS. Results: Of 8949 participants, 1928 people (21.6%) had MetS. The mean age of the participants with and without Mets was 50.4 ± 9.2 years and 46.9 ± 9.1 years respectively. In total 15.3% of participants with MetS and 8.0% of participants without MetS were in the high-risk category of 10-year CVD risk. Among participants with MetS gender, TG, SBP, FBS and in people without MetS gender, TG, SBP, FBS, and HDL showed strong associations with the predicted 10-year CVD risk. Conclusion: Male sex and increased SBP, TG, and FBS parameters were strongly associated with increased 10-year risk of CVD in people with and without MetS. In people without MetS, reduced HDL-cholestrol was strongly associated with increased 10-year risk of CVD. The recognition of participant's TG, blood pressure (BP), FBS and planning appropriate lifestyle interventions related to these characteristics is an important step towards prevention of CVD. [ABSTRACT FROM AUTHOR]

Purpose: We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community adults in order to investigate the predictive capability. We retrospectively analyzed a subset of data from the published Fasa cohort study using individual standard classifiers as well as ensemble learning algorithms. Methods: We measured segmental body composition using the Tanita Analyzer BC-418 MA (Tanita Corp, Japan). The following features were input to our machine learning model: fat-free mass, fat percentage, basal metabolic rate, total body water, right arm fat-free mass, right leg fat-free mass, trunk fat-free mass, trunk fat percentage, sex, age, right leg fat percentage, and right arm fat percentage. We performed classification into diabetes vs. no diabetes classes using linear support vector machine, decision tree, stochastic gradient descent, logistic regression, Gaussian naïve Bayes, k-nearest neighbors (k = 3 and k = 4), and multi-layer perceptron, as well as ensemble learning using random forest, gradient boosting, adaptive boosting, XGBoost, and ensemble voting classifiers with Top3 and Top4 algorithms. 4661 subjects (mean age 47.64 ± 9.37 years, range 35 to 70 years; 2155 male, 2506 female) were analyzed and stratified into 571 and 4090 subjects with and without a self-declared history of diabetes, respectively. Results: Age, fat mass, and fat percentages in the legs, arms, and trunk were positively associated with diabetes; fat-free mass in the legs, arms, and trunk, were negatively associated. Using XGBoost, our model attained the best excellent accuracy, precision, recall, and F1-score of 89.96%, 90.20%, 89.65%, and 89.91%, respectively. Conclusions: Our machine learning model showed that regional body fat compositions were predictive of diabetes status. [ABSTRACT FROM AUTHOR]

Background & Objectives: Diabetes is a known chronic disease affecting more than 400 million people and causing millions of deaths worldwide annually. The World Health Organization predicts that by 2030, diabetes will become the seventh leading cause of death. In the last decades, numerous chemotherapy approaches have been used. However, herbal remedies have accounted for more than 70 percent of the total administered antidiabetic treatments during the last decade. In this study, we aimed to develop a novel natural antidiabetic therapy with Trigonella foenum-graecum (T. foenum-graecum) L. and Citrullus colocynthis (C. colocynthis) (L.). Based on past studies, using C. colocynthis singly can cause diarrhea, abdominal pain, and heartburn, hence in order to reduce these complications, we applied this plant in combination with T. foenum-graecum based on the recommendations of Persian medicine references. Materials & Methods: We admitted 34 diabetics aged 30 -65 years old to design a double-blind parallel randomized controlled clinical trial. We prepared therapeutic capsules containing a combination of 40 mg of C. colocynthis and 350 mg of T. foenum-graecum L. We divided participants into an interventional (n=17) and placebo (n=17) groups and measured fasting blood sugar (FBS) levels, lipid profile, and liver function tests initially. After two weeks, all participants returned for a visit, and FBS and blood sugar (BS) were measured. At the end (12th week), all outcomes were measured and analyzed using an independent t-test and Chi -squared (χ²). Results: The FBS levels decreased significantly in the intervention group compared to that of the control group (171.8±54.9 versus 149.6±46.4, p=0.024), but the improvement s in the lipid profile and liver function were not statistically significant. Conclusion: This study unveiled that combination of T. foenum-graecum L. and C. colocynthis L. at safe levels significantly decreased the FBS levels among T2DM patients. Further studies are needed to provide more efficient antidiabetic effects with the low side effects. [ABSTRACT FROM AUTHOR]

Background: Factors contributing to the development of hypertension exhibit significant variations across countries and regions. Our objective was to predict individuals at risk of developing hypertension within a 5-year period in a rural Middle Eastern area. Methods: This longitudinal study utilized data from the Fasa Adults Cohort Study (FACS). The study initially included 10,118 participants aged 35–70 years in rural districts of Fasa, Iran, with a follow-up of 3,000 participants after 5 years using random sampling. A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). Hyperparameter tuning was performed using various combinations of hyperparameters to identify the optimal model. Synthetic Minority Over-sampling Technology (SMOTE) was used to balance the training data, and feature selection was conducted using SHapley Additive exPlanations (SHAP). Results: Out of 2,288 participants who met the criteria, 251 individuals (10.9%) were diagnosed with new hypertension. The LGBM model (determined to be the optimal model) with the top 30 features achieved an AUC of 0.67, an f1-score of 0.23, and an AUC-PR of 0.26. The top three predictors of hypertension were baseline systolic blood pressure (SBP), gender, and waist-to-hip ratio (WHR), with AUCs of 0.66, 0.58, and 0.63, respectively. Hematuria in urine tests and family history of hypertension ranked fourth and fifth. Conclusion: ML models have the potential to be valuable decision-making tools in evaluating the need for early lifestyle modification or medical intervention in individuals at risk of developing hypertension. [ABSTRACT FROM AUTHOR]

Introduction: The application of machine learning (ML) is increasingly growing in biomedical sciences. This study aimed to evaluate factors associated with type 2 diabetes mellitus (T2DM) and compare the performance of ML methods in identifying individuals with the disease in an Iranian setting. Methods: Using the baseline data from Fasa Adult Cohort Study (FACS) and in a sex‐stratified manner, we studied factors associated with T2DM by applying seven different ML methods including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K‐Nearest Neighbours (KNN), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB) and Bagging classifier (BAG). We further compared the performance of these methods; for each algorithm, accuracy, precision, sensitivity, specificity, F1 score, and Area Under Curve (AUC) were calculated. Results: 10,112 participants were recruited between 2014 and 2016, of whom 1246 had T2DM at baseline. 4566 (45%) participants were males, aged between 35 and 70 years. For males, age, sugar consumption, and history of hospitalization were the most weighted variables regarding their importance in screening for T2DM using the GBM model, respectively; these variables were sugar consumption, urine blood, and age for females. GBM outperformed other models for both males and females with AUC of 0.75 (0.69–0.82) and 0.76 (0.71–0.80), and F1 score of 0.33 (0.27–0.39) and 0.42 (0.38–0.46), respectively. GBM also showed a sensitivity of 0.24 (0.19–0.29) and a specificity of 0.98 (0.96–1.0) in males and a sensitivity of 0.38 (0.34–0.42) and specificity of 0.92 (0.89–0.95) in females. Notably, close performance characteristics were detected among other ML models. Conclusions: GBM model might achieve better performance in screening for T2DM in a south Iranian population. [ABSTRACT FROM AUTHOR]

Background and Aims. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a worldwide pandemic, activates signaling cascades and leads to innate immune responses and secretion of multiple chemokines and cytokines. Long noncoding RNAs (lncRNAs) have a crucial role in inflammatory pathways. Through our search on the PubMed database, we discovered that existing research has primarily focused on examining the regulatory impacts of five lncRNAs in the context of viral infections. However, their role in regulating other conditions, including SARS-CoV-2, has not been explored. Therefore, this study aimed to investigate the expression pattern of lncRNAs in the peripheral blood mononuclear cells (PBMC) and their potential roles in SARS-CoV-2 infection. Potentially significant competing endogenous RNA (ceRNA) networks of these five lncRNAs were found using online in-silico techniques. Methods. Ethylenediaminetetraacetic acid (EDTA) blood samples of the control group consisted of 45 healthy people, and a total of 53 COVID-19-infected patients in case group, with a written informed consent, was collected. PBMCs were extracted, and then, the RNA extraction and complementary DNA (cDNA) synthesis was performed. The expression of five lncRNAs (lnc ISR, lnc ATV, lnc PAAN, lnc SG20, and lnc HEAL) was assessed by real-time PCR. In order to evaluate the biomarker roles of genes, receiver operating characteristic (ROC) curve was drawn. Results. Twenty-four (53.3%) and 29 (54.7%) of healthy and COVID-19-infected participants were male, respectively. The most prevalent symptoms were as follows: cough, general weakness, contusion, headache, and sore throat. The results showed that three lncRNAs, including lnc ISR, lnc ATV, and lnc HEAL, were expressed dramatically higher in the case group compared to healthy controls. According to ROC curve analysis, lnc ATV has a higher AUC and is a better biomarker to differentiate COVID-19 patients from the healthy controls. Then, using bioinformatics methods, the ceRNA network of these lncRNAs enabled the identification of mRNAs and miRNAs with crucial functions in COVID-19. Conclusion. The considerable higher expression of ISR, ATV, and HEAL lncRNAs and the significant area under curve (AUC) in ROC curve demonstrate that these RNAs probably have a potential role in controlling the host innate immune responses and regulate the viral replication of SARS-CoV-2. However, these assumptions need further in vitro and in vivo investigations to be confirmed. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the main fatal cancers. Cell signaling such as Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling substantially influences the process of gene expression and cell growth. Long non-coding RNAs (lncRNAs) play regulatory roles in cell signaling, cell proliferation, and cancer fate. Hence, lncRNAs can be considered biomarkers in cancers. The inhibitory or activating effects of different lncRNAs on the JAK/STAT pathway regulate cancer cell proliferation or tumor suppression. Additionally, lncRNAs regulate immune responses which play a role in immunotherapy. Mechanisms of lncRNAs in CRC via JAK/STAT regulation mainly include cell proliferation, invasion, metastasis, apoptosis, adhesion, and control of inflammation. More profound findings are warranted to specifically target the lncRNAs in terms of activation or suppression in hindering CRC cell proliferation. Here, to understand the lncRNA cross-talk in CRC through the JAK/STAT signaling pathway, we collected the related in vitro and in vivo data. Future insights may pave the way for the development of novel diagnostic tools, therapeutic interventions, and personalized treatment strategies for CRC patients. [ABSTRACT FROM AUTHOR]

Aim: Prediabetes is a strong predictor for type 2 diabetes mellitus (T2DM). However, the possible effects of other factors on the above association are unknown. This study aimed to determine factors associated with the progression of prediabetes to T2DM. Materials and methods: In the present prospective cohort study, 757 individuals with prediabetes included in the Fasa cohort study were identified and followed for 5 years in order to diagnose T2DM. The baseline demographic, medical, and clinical records of participants were surveyed. Latent class analysis (LCA) and Cox regression model were applied to evaluate potential factors affecting the rate of progression of prediabetes to T2DM. p values less than 0.05 were considered significant. Results: The annual cumulative incidence rate of diabetes was 124.1 per 1000 population. A total of 3 different latent classes were among participants including fatty liver & DII class (FLDc), with 18.58%, metabolic syndrome class (MSc), with 31.44%, and high-risk class (HRc), with 49.98% of the participants. Based on the results of Cox multivariate regression analysis, the risk of progression to T2DM was significantly raised with the presence of FLDc (HR = 2.37, 95% CI: 1.19, 4.74). Conclusion: Our findings show that the cumulative diabetes incidence was alarmingly high in Iran. Also, the cooccurrence of factors such as high fatty liver index, high DII score, metabolic syndrome, and waist-to-hip ratio increased the risk of diabetes in prediabetic individuals. These results point out the importance of applying effective preventive interventional programs for prediabetic individuals. [ABSTRACT FROM AUTHOR]

Background: Addiction increases the risk of different lifelong disorders. However, there are limited studies evaluating the effects of opioid use disorder (OUD) on thyroid function. The present study aimed to compare the thyroid function of individuals with and without OUD. Methods: This cross-sectional study was conducted on 700 eligible participants of the Persian Cohort of Fasa, Iran. Pregnant women and participants with false or missing data were excluded from the study. Remained participants were divided into case and control groups based on the recorded history of OUD. Frozen plasma samples of the cohort bank were used to determine the levels of T3, T4, and thyroid-stimulating hormone (TSH). The thyroid function was compared between the two groups using the Mann-Whitney test (P < 0.05). Results: The mean age of the final studied population (n = 648) was 54.0 ± 9.8 years, including 336 men (49.1%) and 197 participants with OUD (28.8%). The median levels of TSH, T4, and T3 were 2.91 ± 4.61, 9.26 ± 3.65, and 1.22 ± 0.49, respectively. The case group had significantly higher TSH (3.72 ± 6.2 vs. 2.58 ± 3.75, P < 0.001) and lower T4 (8 ± 3.6 vs. 9.8 ± 3.5, P < 0.001). Also, T3 was slightly lower in the case group (1.1 ± 0.5 vs. 1.3 ± 0.5; P = 0.369), although this association was only significant in female opium users (P < 0.001). Conclusions: The present findings revealed that OUD caused a reduction in T4 while increasing TSH. Therefore, OUD may lead to the development of primary hypothyroidism, which needs to be investigated in future studies. [ABSTRACT FROM AUTHOR]

Background: Oral lichen planus (OLP) is a T cell-mediated chronic autoimmune disease, whose pathogenesis and etiology are not entirely understood. OLP is characterized by subepithelial lymphocyte infiltration and elevated intra-epithelial lymphocytes. The majority of lamina propria lymphocytes are CD4+ T cells. CD4+ helper T (Th) cells play a crucial role in activating CD8+ cytotoxic T cells (CTLs) through interactions and cytokine production. Th1 and Th2 cells are well-accepted to be associated with OLP pathogenesis. However, OLP treatment is challenging yet, the more information we have about the pathology of OLP, the easier it will be treated. With the discovery of Th17 cells in recent years and the demonstration of their role in autoimmune disease, many researchers started to investigate the role of Th17 in the pathogenesis of OLP. Methods: To make up this review, studies covering the role of TH17 in different types of lichen planus were selected from major databases. Results: As we review in this article, Th17 cells and their signature cytokines play an important role in OLP pathogenesis. As well, utilizing some anti-IL-17 antibodies showed promising results in improving the disease; however, more studies are still needed to better understand and treat OLP. [ABSTRACT FROM AUTHOR]

Innate and acquired immunity responses are crucial for viral infection elimination. However, genetic variations in coding genes may exacerbate the inflammation or initiate devastating cytokine storms which poses severe respiratory conditions in coronavirus disease‐19 (COVID‐19). Host genetic variations in particular those related to the immune responses determine the patients' susceptibility and COVID‐19 severity and pathophysiology. Gene polymorphisms such as single nucleotide polymorphisms (SNPs) of interferons, TNF, IL1, IL4, IL6, IL7, IL10, and IL17 predispose patients to the severe form of COVID‐19 or severe acute respiratory syndrome coronavirus‐2 (SARS‐COV‐2). These variations mainly alter the gene expression and cause a severe response by B cells, T cells, monocytes, neutrophils, and natural killer cells participating in a cytokine storm. Moreover, cytokines and chemokines SNPs are associated with the severity of COVID‐19 and clinical outcomes depending on the corresponding effect. Additionally, genetic variations in genes encoding toll‐like receptors (TLRs) mainly TLR3, TLR7, and TLR9 have been related to the COVID‐19 severe respiratory symptoms. The specific relation of these mutations with the novel variants of concern (VOCs) infection remains to be elucidated. Genetic variations mainly within genes encoding proinflammatory cytokines, cytokine receptors, and TLRs predispose patients to COVID‐19 disease severity. Understanding host immune gene variations associated with the SARS‐COV‐2 infection opens insights to control the pathophysiology of emerging viral infections. [ABSTRACT FROM AUTHOR]

Methicillin-resistant Staphylococcus aureus (MRSA) has evolved vast antibiotic resistance. These strains contain numerous virulence factors facilitating the development of severe infections. Considering the costs, side effects, and time duration needed for the synthesis of novel drugs, seeking efficient alternative approaches for the eradication of drug-resistant bacterial agents seems to be an unmet requirement. Nickel nanoparticles (NiNPs) have been applied as prognostic and therapeutic cheap agents to various aspects of biomedical sciences. Their antibacterial effects are exerted via the disruption of the cell membrane, the deformation of proteins, and the inhibition of DNA replication. NiNPs proper traits include high-level chemical stability and binding affinity, ferromagnetic properties, ecofriendliness, and cost-effectiveness. They have outlined pleomorphic and cubic structures. The combined application of NiNPs with CuO, ZnO, and CdO has enhanced their anti-MRSA effects. The NiNPs at an approximate size of around 50 nm have exerted efficient anti-MRSA effects, particularly at higher concentrations. NiNPs have conferred higher antibacterial effects against MRSA than other nosocomial bacterial pathogens. The application of green synthesis and low-cost materials such as albumin and chitosan enhance the efficacy of NPs for therapeutic purposes. [ABSTRACT FROM AUTHOR]

Background: The ocular complication caused by diabetes is one of the most common reasons of blindness in the world. This study aimed to investigate the effect of educational intervention on eye care practice of type II diabetic patients based on health belief model (HBM) in Fasa city.Methods: This study was a quasi-experimental study on 100 patients with type II diabetes referred to the diabetes center in Fasa city, Fars province, Iran, in 2019. Data were collected using a valid self-reported questionnaire including demographic variables, knowledge, and HBM (perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and cues to action), and eye care performance (based on self-report) and the level of HbA1cof both groups were measured before and three months after the educational intervention. The experimental group received training in eight sessions; each session lasted for 50 to 55 minutes. In order to analyze the studied data, SPSS 22 software (SPSS Inc., IBM, Chicago, IL, USA), Chi-square, independent t-test, and paired t-test have been used. P < 0.05 was considered as statistically significant.Results: The results showed that the mean scores of knowledge (P < 0.001) and HBM components (P < 0.001) in the experimental and control groups after intervention have a significant difference. After the training program, eye care performance in the experimental group was better than that in the control group (P < 0.001). Furthermore, HbA1c (P < 0.001) improved significantly in the experimental group compared to the control group.Conclusions: Planning and implementing education using the HBM to improve eye care performance in diabetic patients are very effective and beneficial. Moreover, educational programs based on health education and health promotion models for diabetic patients for preventing side effects caused by diabetes should be performed. [ABSTRACT FROM AUTHOR]

Background: In here, we evaluated pattern of metastasis and cross-compared clinicopathological features between different age groups with breast cancer (BC). Methods: This study was conducted in the Shiraz Breast Cancer Registry (largest BC registry in Iran). Patients were classified as < 30 years old (group 1), 30–60 years old (group 2), and > 60 years old (group 3). The three age groups were compared regarding clinical and baseline characteristics. Results: Overall, 564 individuals entered group 1, 4519 group 2, and 670 group 3. Group 1 had lower rates of tumor necrosis ( p < 0.001), higher lymphatic or vascular invasion ( p = 0.002), estrogen receptor-negative individuals, and HER2 -positive individuals ( p ≤ 0.001). Younger groups had more stage 3 BC (31.1, 25.6, and 19.7% for groups 1, 2, and 3, respectively) ( p = 0.016), grade 3 BC (27.4, 20.6, and 16.5% for groups 1, 2, and 3, respectively) ( p = 0.001), and grade 3 nucleus (43.1, 34.5, and 27.6% for groups 1, 2, and 3, respectively) ( p < 0.001). Group 1 had higher rates of regional metastasis (4.7 vs. 1.5 and 2.1% for groups 2 and 3, respectively). Younger individuals had higher rates of brain metastasis (13.3, 5.4, and 1.1% for groups 1, 2, and 3, respectively). Moreover, those > 60 years old had more lung metastasis (33 vs. 12.6 and 6.7% for groups 2 and 1, respectively) ( p < 0.001). Younger groups had more < 5-year recurrence (16.3, 11.7, and 8.9%, for groups 1, 2, and 3, respectively) ( p = 0.023). Conclusion: Pattern and site of recurrence changes according to age in BC. This brings up the question whether age is an independent predictor of organ of metastasis or is site of metastasis the result of other clinicopathological determinants which differ between age groups. [ABSTRACT FROM AUTHOR]