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Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. This study aimed to develop and validate machine-learning (ML) algorithms for accurate MASLD screening in a geographically diverse, large-scale population., Methods: Data from the prospective Fasa Cohort Study, initiated in rural Fars province, Iran (March 2014), were employed for this purpose. The required data were collected using blood tests, questionnaires, liver ultrasonography, and physical examinations. A two-step approach identified key predictors from over 100 variables: (1) statistical selection using mean decrease Gini in random forest and (2) incorporation of clinical expertise for alignment with known MASLD risk factors. The hold-out validation approach (with a 70/30 train/validation split) was utilized, along with 5-fold cross-validation on the validation set. Logistic regression, Naïve Bayes, support vector machine, and light gradient-boosting machine (LightGBM) algorithms were compared for model construction with the same input variables based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy., Results: A total of 6,180 adults (52.7% female) were included in the study, categorized into 4816 non-MASLD and 1364 MASLD cases with a mean age (±standard deviation [SD]) of 48.12 (±9.61) and 49.47 (±9.15) years, respectively. Logistic regression outperformed other ML algorithms, achieving an accuracy of 0.88 (95% confidence interval [CI]: 0.86-0.89) and an AUC of 0.92 (95% CI: 0.90-0.93). Among more than 100 variables, the key predictors included waist circumference, body mass index (BMI), hip circumference, wrist circumference, alanine aminotransferase levels, cholesterol, glucose, high-density lipoprotein, and blood pressure., Conclusion: Integration of ML in MASLD management holds significant promise, particularly in resource-limited rural settings. Additionally, the relative importance assigned to each predictor, particularly prominent contributors such as waist circumference and BMI, offers valuable insights into MASLD prevention, diagnosis, and treatment strategies., (© 2024 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.)

Background: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators., Methods: We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software., Results: Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD ( P <0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models., Conclusion: Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning., (© 2024 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.)

Background: Growing evidence shows the undisputable role of non-HDL-C and remnant cholesterol (remnant-C) in cardiovascular disease (CVD) risk assessment and treatment. However, the reference interval (RI) for these lipid parameters is not readily available. The aim of the present investigation was to determine the age and sex-specific RIs for non-HDL-C and remnant-C as well as other lipid parameters among a healthy population in southern Iran. We also report the RI of lipid parameters in rural and urban residents, smokers and post-menopausal women., Methods: Among 14063 participants of Bandare Kong and Fasa cohort studies, 792 healthy subjects (205 men and 578 women) aged 35-70 years were selected. Fasting blood samples were used for determination of total cholesterol (TC), triglycerides (TG) and HDL-C using colorimetric methods. Non-HDL-C and remnant-C were calculated using the valid formula. The 2.5th and 97.5th percentiles were calculated and considered as RI., Results: In the total population (n=792, age 35-70), RIs for non-HDL-C and remnant-C was 74.0-206.8 and 8.0-52.7 mg/dL, respectively. Age (35-44 and≥45 years) and gender-specific RIs for serum non-HDL-C and remnant-C were determined. Remnant-C and non-HDL-C level were different between sex and age categories. The mean value of all lipid parameters except HDL-C was higher in men, urban residents, subject with age≥45 years and smokers., Conclusion: This is the first study in which the RIs for non-HDL-C and remnant-C in southern Iran are reported. This may help physicians to conveniently use these lipid parameters for patient care and better cardiovascular risk assessment., (© 2024 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.)

The Spanish flu spread from September 23, 1918 to 1920. This disease was one of the historical catastrophes in Iran, and a large number of people in Tehran were infected. Evidence also shows that 5000-10000 out of the 250000 infected people died in Tehran over three years. Besides, an increase was detected in the prevalence of other diseases such as pericarditis, orchitis, mastoiditis, meningitis, optic neuritis, paralysis of the palate, mania, cholera, and dysentery. Overall, five percent of the city were destroyed, and the population and economic development were severely damaged. This study aims to evaluate the importance of the history of local medicine in Tehran, the spread of Spanish flu, World War I, and presence of Russian, Ottoman, and British troops in Iran during the flu outbreak. The critical role of Britain in artificial famine, malnutrition, and drug embargo was assessed, as well., (2022 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.)

In the past, communicable diseases caused the highest mortality in Iran. Improvements in socioeconomic status and living standards including access to safe drinking water, along with the inception of Health Houses in the 1980s, have changed disease patterns, decreasing the spread of and deaths from infectious and communicable diseases. The incidence and prevalence of non-communicable diseases (NCD), however, have now increased in Iran, accounting for nearly 80% of deaths and disabilities. Without interventions, NCD are predicted to impose a substantial human and economic burden in the next 2 decades. However, Iran's health system is not equipped with the necessary policies to combat this growth and must refocus and reform. Therefore, in the year 2013, the Ministry of Health and Medical Education funded a well-designed nationwide cohort study-Prospective Epidemiological Research Studies in IrAN (PERSIAN)-in order to assess the burden of NCD and investigate the risk factors associated with them in the different ethnicities and geographical areas of Iran. The PERSIAN Cohort, which aims to include 200000 participants, has 4 components: Adult (main), Birth, Youth and Elderly, which are being carried out in 22 different regions of Iran. Having an enormous dataset along with a biobank of blood, urine, hair and nail samples, the PERSIAN Cohort will serve as an important infrastructure for future implementation research and will provide the evidence needed for new healthcare policies in order to better control, manage and prevent NCD., (Copyright © 2017 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.)