Objective:Infertility arises from diverse etiologies, with polycystic ovarian syndrome (PCOS) being a leading cause among women, while male factors account for approximately 50% of cases. Selecting the most appropriate assisted reproductive technology (ART) for each couple is crucial. This study aimed to develop a dynamic mathematical model to predict ART success rates based on infertility etiology, laboratory tests, and clinical findings. Materials and Methods:In this retrospective study, data from 1,374 cases were reviewed, and a sample of 502 couples from the Yazd Research and Clinical Center for Infertility, Yazd, Iran, was analyzed (April 2016-February 2017). Participants were evaluated according to body mass index (BMI), anti-Müllerian hormone (AMH) levels, number of transferred embryos, and infertility etiology of (PCOS, male factor, or both). Couples were categorized into eight classes based on infertility susceptibility, embryo quality (A, B, C), positive beta human chorionic gonadotropin (β-hCG), clinical pregnancy outcomes, and fertility status. Results:The model estimated ART success rates within subgroups considering all relevant factors. Higher BMI (>30 kg/m²) and lower AMH (<3.5 ng/mL) were associated with reduced success rates. Intracytoplasmic sperm injection (ICSI) showed higher predicted success across all etiologies. Mathematical analysis indicated system stability, and backward bifurcation highlighted the importance of increasing recovery rates (positive clinical pregnancy) to reduce the primary reproduction number (R₀) below one and control infertility. No Hopf bifurcation was observed, indicating the absence of periodic fluctuations in treatment outcomes. Conclusion:This dynamic model provides a framework to predict ART success and understand the interactions between clinical and biological factors in infertile couples. It may assist clinicians in optimizing individualized treatment strategies based on patient-specific characteristics.

INTRODUCTION:The study investigates the relationship between blood lipid components and metabolic disorders, specifically high-density lipoprotein cholesterol (HDL-C), which is crucial for cardiovascular health. It uses logistic regression (LR), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), XGBoost (XGB), and neural networks (NN) algorithms to explore how blood factors affect HDL-C levels in the bloodstream. METHOD:The study involved 9704 participants, categorized into normal and low HDL-C levels. Data was analyzed using a data mining approach such as LR, DT, RF, KNN, XGB, and NN to predict HDL-C measurement. Additionally, DT was used to identify the predictive model for HDL-C measurement. RESULT:This study identified gender-specific hematological predictors of HDL-C levels using multiple ML models. Logistic regression exhibited the highest performance. NHR and LHR were the most influential predictors in males and females, respectively, with SHAP analysis confirming their critical roles alongside LYM, NEUT, and WBC in HDL-C classification. DISCUSSION:The results show that blood inflammation plays a role in HDL-C homeostasis. The mechanisms of these relationships are not fully understood, but a complex interplay between inflammation and HDL-C levels as well as cardiometabolic health is evident. These findings support the pathophysiological role of inflammatory pathways in cardiometabolic disorders and provide insights into how modulation of hematological inflammation may contribute to disease prevention or treatment.

Dyslipidemia as a modifiable risk factor for chronic non-communicable diseases has become a worldwide concern. We aim to explore different anthropometric measures as predictors of dyslipidemia using various machine learning methods. From the baseline of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, a total of 9,640 participants were included in the analysis. Among them, 1,388 participants did not have dyslipidemia, while 8,252 participants had dyslipidemia. Various anthropometric indices were examined, including waist-to-height ratio (WHtR), body roundness index (BRI), abdominal volume index (AVI), weight-adjusted waist index (WWI), lipid accumulation product (LAP), visceral adiposity index (VAI), conicity index (C-index), body surface area (BSA), body adiposity index (BAI), and waist-to-hip ratio (WHR). The association between these indices and dyslipidemia was assessed using logistic regression (LR), decision tree (DT), random forest (RF), neural networks (NN), K-nearest neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) models. Based on our LR model, we found that several factors included, BAI, BSA, age, and WHR were significant. For example, for each unit increase in WHR, the odds of dyslipidemia increase by 9 time (OR = 90.29, 95

Background The aim was to establish a 10-year dyslipidemia incidence model, investigating novel anthropometric indices using exploratory regression and data mining. Methods This data mining study was conducted on people who were diagnosed with dyslipidemia in phase 2 ( n = 1097) of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, who were compared with healthy people in this phase ( n = 679). The association of dyslipidemia with several novel anthropometric indices including Conicity Index (C-Index), Body Roundness Index (BRI), Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), Abdominal Volume Index (AVI), Weight-Adjusted-Waist Index (WWI), A Body Shape Index (ABSI), Body Mass Index (BMI), Body Adiposity Index (BAI) and Body Surface Area (BSA) was evaluated. Logistic Regression (LR) and Decision Tree (DT) analysis were utilized to evaluate the association. The accuracy, sensitivity, and specificity of DT were assessed through the performance of a Receiver Operating Characteristic (ROC) curve using R software. Results A total of 1776 subjects without dyslipidemia during phase 1 were followed up in phase 2 and enrolled into the current study. The AUC of models A and B were 0.69 and 0.63 among subjects with dyslipidemia, respectively. VAI has been identified as a significant predictor of dyslipidemias (OR: 2.81, (95% CI: 2.07, 3.81)) in all models. Moreover, the DT showed that VAI followed by BMI and LAP were the most critical variables in predicting dyslipidemia incidence. Conclusions Based on the results, model A had an acceptable performance for predicting 10 years of dyslipidemia incidence. Furthermore, the VAI, BMI, and LAP were the principal anthropometric factors for predicting dyslipidemia incidence by LR and DT models.

High-sensitivity C-reactive protein (hs-CRP) is a biomarker of inflammation predicting the incidence of different health pathologies. In this study, we aimed to evaluate the association between hematological and demographic factors with hs-CRP levels using decision tree (DT) and linear regression (LR) modeling. This study was conducted on a population of 9704 males and females aged 35 to 65 years recruited from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study. We utilized a data mining approach to construct a predictive model of hs-CRP measurements, employing the DT methodology. DT model was used to predict hs-CRP level using biochemical factors and clinical features. A total of 9,704 individuals were included in the analysis, with 57% of them being female. Except for fasting blood glucose (FBG), hypertension (HTN), and Type 2 diabetes mellites (T2DM), all variables showed significant differences between the two groups. The results of the LR models showed that variables such as anxiety score, depression score, Systolic Blood Pressure, Cardiovascular disease, and HTN were significant in predicting hs-CRP levels. In the DT models, depression score, FBG, cholesterol, and anxiety score were identified as the most important factors in predicting hs-CRP levels. DT model was able to predict hs-CRP level with an accuracy of 72.1% in training and 71.4% in testing of both genders. The proposed DT model appears to be able to predict the hs-CRP levels based on anxiety score, depression scores, fasting blood glucose, systolic blood pressure, and history of cardiovascular diseases.

AbstractBackgroundArterial stiffness is a crucial factor in determining an increase in systolic blood pressure and pulse pressure and can also predict the development of cardiovascular disease (CVD). The purpose of this study was to examine the relationship between arterial stiffness and future CVD.MethodsOut of the original 9704 participants in the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study, we randomly selected 363 healthy participants, 226 normal subjects (who reported symptoms of CVD but were not confirmed) and 292 individuals who had experienced a major cardiovascular event. The SphygmoCor XCEL System (AtCor Medical Incorporation) was utilized to measure pulse wave velocity (PWV), central augmentation index (CAI), cardio‐ankle vascular index (CAVI) and central aortic pressure (CAP). A multivariate multiple regression model was used to analyse the factors associated with non‐invasive arterial stiffness parameters (PWV, CAVI, CAP and CAI) after adjusting for potential confounders. All statistical analyses were conducted using SPSS 21 with a significance level of 0.05.ResultsThe mean PWV was significantly higher in patients who had experienced a confirmed CVD event (P < 0.001). The multivariate multiple regression model results, after adjusting for potential confounders, showed a significant association between PWV and the CVD group (normal vs. healthy and event vs. healthy), as well as between hypertension and obesity with PWV and diabetes with CAI (P < 0.05).ConclusionsPWV was found to be associated with CVD and its related risk factors such as diabetes, obesity and hypertension. It may be more effective than other arterial stiffness parameters in predicting CVD in clinical settings.

BackgroundHigh triglyceride (TG) affects and is affected of other hematological factors. The determination of serum fasted triglycerides concentrations, as part of a lipid profile, is crucial key point in hematological factors and significantly affect various systemic diseases. This study was carried out to assess the potential relation between the concentration of TG and hematological factors.MethodOur sample size was 9704 participants beginning in 2007 and ending in 2020 aged between 35 and 65 years, sourced from the MASHAD cohort (northeastern Iran). Machine learning methodologies, specifically logistic regression, decision tree, and random forest algorithms, were utilized for data analysis in the investigation of individuals with normal and high TG levels.ResultsThe highest Gini score belongs to RLR (Red cell distribution width/Lymphocyte) (236.10), RPR (Red cell distribution width/Platelets) (215.78), and PHR (Platelets/high-density lipoprotein) (273.66). We also found that factors such as age are statistically associated with the level of TG in women probably due to the drop in menopausal estrogen. RF model showed to have higher accuracy in predicting the TG level in both males and females.ConclusionOur model assessed the association between serum TG with several hematological factors like RLR, RPR, and PHR. Other hematological factors also have been reported to be related to the TG level. As these results give us new insights into the association of TG on various hematological factors and their possible interactions with each other. future studies are needed to provide sufficient data for the mechanism and the pathophysiology of the findings.

ABSTRACT Background Unbalanced levels of serum total cholesterol (TC) and its subgroups are called dyslipidemia. Several anthropometric indices have been developed to provide a more accurate assessment of body shape and the health risks associated with obesity. In this study, we used the random forest model (RF), decision tree (DT), and logistic regression (LR) to predict total cholesterol based on new anthropometric indices in a sex‐stratified analysis. Method Our sample size was 9639 people in which anthropometric parameters were measured for the participants and data regarding the demographic and laboratory data were obtained. Aiding the machine learning, DT, LR, and RF were drawn to build a measurement prediction model. Results Anthropometric and other related variables were compared between both TC <200 and TC ≥200 groups. In both males and females, Lipid Accumulation Product (LAP) had the greatest effect on the risk of TC increase. According to results of the RF model, LAP and Visceral Adiposity Index (VAI) were significant variables for men. VAI also had a stronger correlation with HDL‐C and triglyceride. We identified specific anthropometric thresholds based on DT analysis that could be used to classify individuals at high or low risk of elevated TC levels. The RF model determined that the most important variables for both genders were VAI and LAP. Conclusion We tend to present a picture of the Persian population's anthropometric factors and their association with TC level and possible risk factors. Various anthropometric indices indicated different predictive power for TC levels in the Persian population.

BACKGROUND:Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices. METHODS:This was a prospective cohort study comparing people with (n = 1256) and without (n = 5193) diabetes mellitus in phase II of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study. The association of several anthropometric indices in phase I, including Body Mass Index (BMI), Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Visceral Adiposity Index (VAI), Weight-Adjusted-Waist Index (WWI), Body Roundness Index (BRI), Body Surface Area (BSA), Conicity Index (C-Index) and Lipid Accumulation Product (LAP) with T2DM incidence (in phase II) were examined; using Logistic Regression (LR) and Decision Tree (DT) analysis. RESULTS:BMI followed by VAI and LAP were the best predictors of T2DM incidence. Participants with BMI < 21.25 kg/m2 and VAI ≤ 5.9 had a lower chance of diabetes than those with higher BMI and VAI levels (0.033 vs. 0.967 incident rate). For BMI > 25 kg/m2, the chance of diabetes rapidly increased (OR = 2.27). CONCLUSIONS:BMI, VAI, and LAP were the best predictors of T2DM incidence.

Background: Ovarian reserve is one of the most important factors that influences the success of assisted reproductive technology (ART). Recently, the role of anti-m & uuml;llerian hormone (AMH) in ART has been investigated as a marker for the prediction of ovarian response. We aim to examine this relationship within a large Iranian population. Materials and Methods: In this cross-sectional study, we obtained data from 1000 infertile couples who referred to the Research and Clinical Centre of Yazd Infertility Clinic for in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI). Serum AMH levels, oocyte count, numbers of fertilised oocytes, endometrial thickness, and percentage of mature oocytes were measured. The relationship between AMH serum levels and the number and quality of oocytes and embryos in ART cycles was analysed. Results: In the linear regression model, the log of the variables total dose of gonadotropin, two pronuclei (2PN), log oestradiol, total embryos, duration of stimulation, number of embryos transferred, protocol, and cause of infertility were significant predictors of log AMH. Conclusion: There appears to be a relationship between serum AMH levels in the early follicular phase and ovarian reserve. Higher serum AMH levels were also associated with shorter ART cycles.

INTRODUCTION:Elevated levels of low-density lipoprotein-cholesterol (LDL-C) is a significant risk factor for the development of cardiovascular diseases (CVD)s. Furthermore, studies have revealed an association between indices of the complete blood count (CBC) and dyslipidemia. We aimed to investigate the relationship between CBC parameters and serum levels of LDL. METHOD:In a prospective study involving 9704 participants aged 35-65 years, comprehensive screening was conducted to estimate LDL-C levels and CBC indicators. The association between these biomarkers and high LDL-C (LDL-C≥130 mg/dL (3.25 mmol/L)) was investigated using various analytical methods, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) methodologies. RESULT:The present study found that age, hemoglobin (HGB), hematocrit (HCT), platelet count (PLT), lymphocyte (LYM), PLT-LYM ratio (PLR), PLT-High-Density Lipoprotein (HDL) ratio (PHR), HGB-LYM ratio (HLR), red blood cell count (RBC), Neutrophil-HDL ratio (NHR), and PLT-RBC ratio (PRR) were all statistically significant between the two groups (p<0.05). Another important finding was that red cell distribution width (RDW) was a significant predictor for higher LDL levels in women. Furthermore, in men, RDW-PLT ratio (RPR) and PHR were the most important indicators for assessing the elevated LDL levels. CONCLUSION:The study found that sex increases LDL-C odds in females by 52.9 %, while age and HCT increase it by 4.1 % and 5.5 %, respectively. RPR and PHR were the most influential variables for both genders. Elevated RPR and PHR were negatively correlated with increased LDL levels in men, and RDW levels was a statistically significant factor for women. Moreover, RDW was a significant factor in women for high levels of HDL-C. The study revealed that females have higher LDL-C levels (16 % compared to 14 % of males), with significant differences across variables like age, HGB, HCT, PLT, RLR, PHR, RBC, LYM, NHR, RPR, and key factors like RDW and SII.

Abstract Background: There are different etiologies of infertility. Poly Cystic Ovarian Syndrome (PCOS) is a leading cause of infertility among women, and the male factor accounts for about 50 % of the total number of infertile couples. As with various etiologies, various treatments are required. Choosing the most suitable procedure of Assisted Reproductive Technology (ART) for individuals is of great importance. Therefore, mathematical models are needed to estimate the success rate of ART for every individual and to determine which treatment suits the best for each couple. Objective:In this study, we aim to present a new dynamic model that can predict the success rate of ART models for each base on the etiology of their infertility, lab tests, and clinical findings. Materials and Methods For this purpose, 502 participants were recruited and evaluated based on their BMI, AMH levels, the number of transferred embryos, and the etiology of infertility (PCOS, male factor, or both). For this purpose, the community is divided into eight classes: infertile susceptible couples, couples with the cause of infertility PCOS, male factor or both, couples with embryo quality A, B, and C, coupled with the positive βHCG test, couples with the positive result of clinical pregnancy and fertile couples. Results: Our dynamic models evaluate the success rate of ART in our subgroups considering all of the effective factors such as BMI, AMH level, number of transferred embryos, and the etiology of infertility. We have found that (BMI > 30 kg/m2) and low AMH levels (< 3.5ng/mL) indicate lower success rates. Moreover, ICSI was more promising for all the underlying etiologies of male factor, PCOS, or both. Conclusion: In conclusion, our new mathematical model is presented to investigate the dynamics and diagnose the cause of infertility in couples. It is shown that the equilibrium point free of disease is asymptotically stable. In this system, backward bifurcation occurs. When backward bifurcation occurs, we must reduce the primary reproduction number to less than one to eradicate the disease. In our system, we see that the factors that cause the reduction of the primary reproduction number are the factors used to eliminate infertility (the essential reproduction R0 depends on the recovery rate in infertile couples with positive clinical pregnancy (ξ). Therefore, backward bifurcation depends on the possibility of recovery. As the rate of recovery increases, the primary reproduction number R0 decrease and controls couple’s infertility. When male factors and treatment methods cause infertility are IVF and IV F&ICSI, we demonstrate that there is no Hopf bifurcation, i.e., no periodic orbit emerges or vanishes due to a change in the stability of a fixed point.

Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35–65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF model gave high accuracy, precision, specificity, and AUC. Previous studies suggested the positive relationship of triglyceride-glucose (TyG) index with T2DM, so we considered the association of TyG index with hematological factors. We found this association was aligned with their results regarding T2DM, except MCHC. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM. Our model provides valuable information to predict T2DM like age and WBC.

Objective: This cohort study aimed to determine the prevalence and risk factors of latent tuberculosis infection among healthcare workers during the COVID-19 pandemic.Methods: A one-year cohort study was conducted in a referral hospital in Kashan, involving 176 medical, educational, and cleaning personnel. Initial evaluations and tuberculin skin tests were performed, followed by a one-year follow-up period. Data were analyzed using SPSS version 26 software.Results: Among the participants, 26.1% (46 individuals) tested positive for latent tuberculosis infection. Age was a significant risk factor, with a 3.6% increase in latent tuberculosis infection risk with each advancing year. Men had 2.19 times (1.10-4.35) the chance of having a latent infection compared to women. Hospital staff were 3.7 times more at risk of tuberculosis infection than students. Among the hospital job categories, nursing assistants had the highest chance of tuberculosis infection, 6.77 times higher than medical students, followed by cleaning staff and nurses. The ICU, General, and Obstetrics and Gynecology departments had an infection chance of 2.46 (1.11-5.46) compared to other departments. No new positive cases were detected during the follow-up period.Conclusion: This study contributes to the understanding of latent tuberculosis infection prevalence and its risk factors among healthcare workers during the COVID-19 pandemic. The findings highlight the importance of infection control measures and targeted interventions to protect healthcare workers from occupational tuberculosis exposure.

BackgroundPregnant women are a high-risk population for mental health effects during a pandemic.ObjectiveThis study aims to examine the association of perceived risk toward COVID-19 viral infection acquisition and maternal mental distress.MethodsIn a cross-sectional study, a total of 392 pregnant women were recruited. Data gathered using the perceived stress scale, State-Trait anxiety inventory, Beck depression inventory, and protective behaviour were assessed. Linear regression analysis was applied in both unadjusted and adjusted models to assess the association between the exposure and outcome variables.ResultsIn all five unadjusted and adjusted models, the perceived risk of COVID-19 acquisition remained a highly significant predictor for stress, anxiety factor 1 and 2, depression, and protective behaviours (P<0.001).ConclusionCOVID-19 may be an important additional stress source for pregnant women.

This chapter consolidates research on cultural beliefs and attitudes that serve as barriers to the management of sexual healthcare among Western, Asian, and Middle Eastern practicing physicians in the USA. The chapter first reviews evidence from the research literature to demonstrate how physicians from these populations have viewed and experienced various cultural challenges, particularly since discussion of sexuality is considered taboo within their cultures. Second, it presents data from two research studies, conducted on Iranian-American women and physicians, on issues related to sexuality and sexual healthcare management. Third, using case studies of two physicians and two women, it highlights some of the current issues of these participants, described by their narratives regarding culture, medical practice, and training. These factors, as well as life experiences, have shaped their perceptions and attitudes toward sexuality and sexual health. Lastly, we offer recommendations for physicians coming from, and working with, sub-populations within larger cultural systems. These recommendations proactively provide effective sexual healthcare services, including the use of sexual history taking, as part of their patient’s routine checkups.

Obesity continues to be a health burden to society and new efforts may be needed to combat this epidemic. This study aims to investigate the contribution of parents education and level of income, food environment (grocery stores and fast food restaurants), and built environment (perceived safety, availability/quantity of parks) on childhood obesity. This cross-sectional observational study explored whether parents education and income level, built environment, and food environment can affect children with obesity. Participants were selected from 3 separate elementary schools located in an urban community with higher risk to have children with obesity in Montclair, California. Children living in families with low incomes have 2.31 times greater odds to be affected by obesity than children living in higher income homes. Children whose parents did not feel safe in their neighborhoods had odds of obesity 2.23 times greater than those who reported their neighborhoods as safe. Age also appeared to be a risk factor, and the odds of children affected by obesity among children 8 to 9 years was 0.79, and the odds of being affected by obesity among children 10 to 11 years of age was 0.36, when compared to children 6 to 7 years old. Findings suggest that low family income, perceptions of neighborhoods as unsafe, and young age are associated with higher body mass index (BMI) percentiles among children living in poor neighborhoods in Montclair, California.

Introduction. Knowledge on effective management strategy for sexual healthcare (i.e., sexual history taking, sexually transmitted infections, sexual dysfunctions) used by Iranian-American physicians remains a serious gap within current literature. Having this knowledge, and its impact on their patients, is essential, since discussions of sexually related topics are taboo in Iranian societies. Aim. We examined the Iranian-American physicians’ sexual healthcare management offered to their patients, within the context of the barriers, and the attitudes, inhibiting the discussion and provision of sexual healthcare. Methods . A self- administrated questionnaire was designed. 1,550 survey instruments were sent to Iranian-American physicians practicing in California. Factor analysis performed to detect relationships between correlated variables within the data. Results. 348 questionnaires (23% response rate) were returned. Four factors related to the effectiveness of sexual health care management were identified, which were internally consistent (a range of Cronbach’s alpha=0.89 to 0.94). Factors: (1) female sexual dysfunction, (2) history of sexual intercourse, (3) STI, and knowledge of disease, (4) male sexual dysfunction. Significant associations were found between variables: clinical specialty, religious affiliation, age, gender, and place of graduation. Conclusion. Results show all four factors may significantly impact the effectiveness of sexual health care management by Iranian-American physicians which can potentially influence quality of sexual healthcare for patients. Additional studies from this population and other subpopulations of US physicians are needed to design new strategies that reflect on physicians’ management on sexual healthcare delivery. If confirmed in other studies, our findings could have implications for training of medical graduates globally.