Serum Zinc and Copper Concentrations and Dyslipidemia as Risk Factors of Cardiovascular Disease in Adults: Data Mining Techniques.

This study aimed to examine the relationship between serum cholesterol levels and the ratio of zinc (Zn) and copper (Cu) in the blood serum and the incidence of cardiovascular disease (CVD). In Phase I of the study, 9704 individuals between the age of 35 and 65 years were recruited. Phase II of the cohort study comprised 7561 participants who completed the 10-year follow-up. The variables which were measured at the baseline of the study included gender, age, systolic blood pressure (SBP), diastolic blood pressure (DBP); biochemical parameters including serum Cu, Zn, copper-zinc ratio (Cu/Zn), zinc-copper ratio (Zn/Cu); fasted lipid profile consisting of triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL) as well as fasting serum glucose, and triglycerides-glucose (TyG) index. Decision tree (DT) and logical regression (LR) models were applied to examine the relationship between the aforementioned factors and CVD. CVD was diagnosed in 837 individuals (378 males and 459 females) out of 7561 participants. According to the LR models, SBP, TC, HDL, age, Zn/Cu, and TyG index for males and SBP, age, TyG index, HDL, TC, Cu/Zn, and Cu for females had the highest correlation with CVD (p-value ≤ 0.033). Based on the DT algorithm, 88% of males with SPB < 129.66 mmHg, younger age (age < 53 years), TyG index < 9.53, 173 ≤ TC < 187 mg/dL, and HDL ≥ 32 mg/dL had the lowest risk of CVD. Also, 98% of females with SBP < 128 mmHg, TyG index < 9.68, age < 44, TC < 222 mg/dL, and HDL ≥ 63.7 mg/dL had the lowest risk of CVD. It can be concluded that the Zn/Cu for men and Cu/Zn for women, along with dyslipidemia and SBP, could significantly predict the risk of CVD in this cohort from northeastern Iran.

Association of atherosclerosis indices, serum uric acid to high-density lipoprotein cholesterol ratio and triglycerides-glucose index with hypertension: A gender-disaggregated analysis.

This study assessed the association between atherosclerosis indices, serum uric acid to high-density lipoprotein cholesterol ratio (UHR) and triglyceride-glucose (TyG) index and the prevalence of hypertension among MASHAD cohort participants. In this cross-sectional study, the participants were divided into hypertensive and non-hypertensive subjects. The atherosclerosis indices, UHR and TyG index of the two groups were compared. Logistic regression analyses were used to determine the associations of these indices with hypertension in both sex. Receiver operating characteristic (ROC) curve analysis was used to establish the cut-off values for differentiating hypertensive from non-hypertensive subjects. p-values < .05 were considered statistically significant. Data related to 9675 subjects (3035 hypertensive and 6640 non-hypertensive) were analyzed. The mean values of atherosclerosis indices, UHR and TyG index were significantly higher (p < .001) in the hypertensives compared to non-hypertensives. After adjustment for potential confounders, among men, the TyG index (OR = 1.360; 95% CI: 1.210-1.530; p < .001) remained an independent factor for hypertension. Among women, atherogenic index of plasma (OR = 1.005; 95% CI: 1.002-1.007; p < .001), UHR (OR = 1.043; 95% CI: 1.026-1.060; p < .001) and TyG index (OR = 1.519; 95% CI: 1.376-1.677; p < .001) remained independent factors for hypertension. ROC curve analysis revealed that compare to the other indices, TyG index had a better predictive value for hypertension in both sex, especially in women.

The associations of oxidative stress and inflammatory markers with obesity in Iranian population: MASHAD cohort study.

BACKGROUND: Low-grade inflammation and stress oxidative condition play a role in the pathogenesis of obesity, and the serum levels of these markers, such as pro-oxidant-antioxidant balance (PAB), high-sensitivity C-reactive protein (hs-CRP), and uric acid may indicate obesity progression. In this study, we aimed to investigate the relationship between obesity with PAB, hs-CRP, and uric acid in the Iranian population. METHODS: This study was derived from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study. A total of 7985 subjects aged 35 to 65 years were divided into three groups according to body mass index (BMI) as: normal, overweight and obese groups. Anthropometric indices and biochemical parameters such as PAB, superoxide dismutase type 1 (SOD1), hs-CRP, and uric acid were measured in all the participants. We evaluated the association of obesity with inflammatory factors by using multivariate regression analysis. Also, those participants with hypertension, an endocrine disorder, history of cardiovascular diseases and diabetes mellitus were excluded from the study. RESULTS: There was a positive significant correlation between BMI and serum PAB, hs-CRP and uric acid (p < 0.001). While no statistically significant relation was observed between BMI and SOD1 (p = 0.85). Multivariate regression analysis showed that the risk of overweight and obesity increased 1.02 and 1.03-fold according to increase 10 units of PAB raise in comparison to reference group (normal weight) [(odds ratio (OR): 1.02, 95% CI (1.01-1.03)] and [OR: 1.03, 95% CI (1.01-1.04)], respectively). In addition, hs-CRP serum concentration was significantly associated with a high risk of obesity [(OR: 1.02; 95% CI (1.01-1.03)]. While the high levels of serum uric acid were associated with increased odds of overweight and obesity risk [OR: 1.4; CI (1.39-1.58) and OR: 1.76; CI (1.63-1.89), respectively]. CONCLUSIONS: Generally, we showed a significant association between BMI and serum PAB, hs-CRP values and uric acid levels, suggesting the role of these factors as risk stratification factors for obesity.

Prediction of cardiovascular disease risk by serum zinc and copper concentrations and anthropometric measurements.

INTRODUCTION: We aimed to investigate the association between cardiovascular disease (CVD) and various anthropometric indices, as well as the serum levels of copper (Cu) and zinc (Zn), copper-zinc ratio (Cu/Zn ratio) and zinc-copper ratio (Zn/Cu ratio), in a large population sample from northeastern Iranian. METHOD: 9704 individuals aged 35 to 65 were enrolled in the first phase of the study. After a 10-year follow-up, 7560 participants were enrolled into the second phase. The variables used in this study included demographic characteristics, such as gender and age; biochemical parameters including: serum Zn, Cu, Cu/Zn ratio, and Zn/Cu ratio; anthropometric parameters including: waist circumference (WC), body mass index (BMI), and waist-to-hip ratio (WHR). The relationship between the aforementioned indices and CVD was examined using decision tree (DT) and logistic regression (LR) models. RESULTS: A total of 837 individuals were diagnosed with CVD among the 7560 participants. LR analysis showed that BMI, age, WH zinc-copper ratio (Zn/Cu ratio), and serum Zn/Cu ratio were significantly associated the development of CVD in men, and WHR, age, BMI, serum Cu, and Cu/Zn ratio in women. DT analysis showed that, age was the most important predictor of CVD in both genders. 71% of women, older than 49 years, with a WHR≥ 0.89, serum Cu< 75 (µg/dl), BMI≥ 22.93 (kg/m2), and serum Cu≥ 14 (µg/dl), had the highest risk of CVD. In men, among those who were ≥ 53 years, with a WHR≥ 0.98, serum Zn/Cu ratio< 1.69, and BMI≥ 22.30, had the highest risk of CVD. CONCLUSION: Among Iranian adult population, BMI, age, and WHR were one of the predictors of CVD for both genders. The Zn/Cu ratio was CVD predictor for men while Cu/Zn ratio was CVD predictor for women.

White blood cell and platelet distribution widths are associated with hypertension: data mining approaches.

In this paper, we are going to investigate the association between Hypertension (HTN) and routine hematologic indices in a cohort of Iranian adults. The data were obtained from a total population of 9704 who were aged 35-65 years, a prospective study was designed. The association between hematologic factors and HTN was assessed using logistic regression (LR) analysis and a decision tree (DT) algorithm. A total of 9704 complete datasets were analyzed in this cohort study (N = 3070 with HTN [female 62.47% and male 37.52%], N = 6634 without HTN [female 58.90% and male 41.09%]). Several variables were significantly different between the two groups, including age, smoking status, BMI, diabetes millitus, high sensitivity C-reactive protein (hs-CRP), uric acid, FBS, total cholesterol, HGB, LYM, WBC, PDW, RDW, RBC, sex, PLT, MCV, SBP, DBP, BUN, and HCT (P < 0.05). For unit odds ratio (OR) interpretation, females are more likely to have HTN (OR = 1.837, 95% CI = (1.620, 2.081)). Among the analyzed variables, age and WBC had the most significant associations with HTN OR = 1.087, 95% CI = (1.081, 1.094) and OR = 1.096, 95% CI = (1.061, 1.133), respectively (P-value < 0.05). In the DT model, age, followed by WBC, sex, and PDW, has the most significant impact on the HTN risk. Ninety-eight percent of patients had HTN in the subgroup with older age (≥58), high PDW (≥17.3), and low RDW (<46). Finally, we found that elevated WBC and PDW are the most associated factor with the severity of HTN in the Mashhad general population as well as female gender and older age.

The relationship between anthropometric indices and the presence of hypertension in an Iranian population sample using data mining algorithms.

Hypertension (HTN) is a common chronic condition associated with increased morbidity and mortality. Anthropometric indices of adiposity are known to be associated with a risk of HTN. The aim of this study was to identify the anthropometric indices that best associate with HTN in an Iranian population. 9704 individuals aged 35-65 years were recruited as part of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study. Demographic and anthropometric data of all participants were recorded. HTN was defined as a systolic blood pressure (SBP) ≥ 140 mmHg, and/ or a diastolic blood pressure (DBP) ≥ 90 mmHg on two subsequent measurements, or being treated with oral drug therapy for BP. Data mining methods including Logistic Regression (LR), Decision Tree (DT), and Bootstrap Forest (BF) were applied. Of 9704 participants, 3070 had HTN, and 6634 were normotensive. LR showed that body roundness index (BRI), body mass index (BMI) and visceral adiposity index (VAI) were significantly associated with HTN in both genders (P < 0.0001). BRI showed the greatest association with HTN (OR = 1.276, 95%CI = (1.224, 1.330)). For BMI we had OR = 1.063, 95%CI = (1.047, 1.080), for VAI we had OR = 1.029, 95%CI = (1.020, 1.038). An age < 47 years and BRI < 4.04 was associated with a 90% probability of being normotensive. The BF indicated that age, sex and BRI had the most important role in HTN. In summary, among anthropometric indices the most powerful indicator for discriminating hypertensive from normotensive patients was BRI.

Association between biochemical and hematologic factors with COVID-19 using data mining methods.

BACKGROUND AND AIM: Coronavirus disease (COVID-19) is an infectious disease that can spread very rapidly with important public health impacts. The prediction of the important factors related to the patient's infectious diseases is helpful to health care workers. The aim of this research was to select the critical feature of the relationship between demographic, biochemical, and hematological characteristics, in patients with and without COVID-19 infection. METHOD: A total of 13,170 participants in the age range of 35-65 years were recruited. Decision Tree (DT), Logistic Regression (LR), and Bootstrap Forest (BF) techniques were fitted into data. Three models were considered in this study, in model I, the biochemical features, in model II, the hematological features, and in model II, both biochemical and homological features were studied. RESULTS: In Model I, the BF, DT, and LR algorithms identified creatine phosphokinase (CPK), blood urea nitrogen (BUN), fasting blood glucose (FBG), total bilirubin, body mass index (BMI), sex, and age, as important predictors for COVID-19. In Model II, our BF, DT, and LR algorithms identified BMI, sex, mean platelet volume (MPV), and age as important predictors. In Model III, our BF, DT, and LR algorithms identified CPK, BMI, MPV, BUN, FBG, sex, creatinine (Cr), age, and total bilirubin as important predictors. CONCLUSION: The proposed BF, DT, and LR models appear to be able to predict and classify infected and non-infected people based on CPK, BUN, BMI, MPV, FBG, Sex, Cr, and Age which had a high association with COVID-19.

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications.

Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.

Platelet distribution widths and white blood cell are associated with cardiovascular diseases: data mining approaches.

OBJECTIVE: To investigate the association between cardiovascular diseases (CVDs) and haematologic factors in a cohort of Iranian adults. METHOD: For a total population of 9,704 aged 35 to 65, a prospective study was designed. Haematologic factors and demographic characteristics (such as gender, age, and smoking status) were completed for all participants. The association between haematologic factors and CVDs was assessed through logistic regression (LR) analysis, decision tree (DT), and bootstrap forest (BF). RESULTS: Almost all of the included factors were significantly associated with CVD (p<.001). Among the included factors, were: age, white blood cell (WBC), and platelet distribution width (PDW) had the strongest correlation with the development of CVD. For unit OR interpretation, WBC has been represented as the most remarkable risk factor for CVD (OR: 1.22 (CI 95% (1.18, 1.27))). Also, age is associated with an increase in the odds of CVD + occurrence (OR: 1.12 (CI 95% (1.11, 1.13))). Moreover, males are times more likely to develop CVD than females (OR: 1.39 (CI 95% (1.22, 1.58))). In DT model, age is the best classifier factor in CVD development, followed by WBC and PDW. Furthermore, based on the BF algorithm, the most crucial factors correlated with CVD are age, WBC, PDW, sex, and smoking status. CONCLUSION: The obtained result from LR, DT, and BF models confirmed that age, WBC, and PDW are the most crucial factors for the development of CVD.

Serum uric acid to high-density lipoprotein ratio as a novel indicator of inflammation is correlated with the presence and severity of metabolic syndrome: A large-scale study.

INTRODUCTION: We investigated the association of serum uric acid to high-density lipoprotein ratio (UHR) with the presence and severity of metabolic syndrome (MetS) among MASHAD cohort participants. METHODS: In this cross-sectional study, according to International Diabetes Federation criteria, the cohort participants were divided into MetS (+) and MetS (-) groups. MetS (+) were classified into Group 1 (those with 3 MetS criteria), Group 2 (those with 4 MetS criteria) and Group 3 (those with 5 MetS criteria). UHR was compared among the groups. RESULTS: Data related to 9637 subjects including 3824 MetS (+) and 5813 MetS (-) were analysed. The mean UHR was significantly higher (p < .001) in the MetS (+) group compared with the MetS (-) group. UHR increased as the MetS severity increased (p < .001). ROC analysis revealed that UHR greater than 9.5% has 89.07% sensitivity and 77.03% specificity in differentiating MetS (-) from MetS (+) subjects. CONCLUSION: Among MASHAD cohort study participants, a significant association between UHR and MetS was found. Furthermore, there is an increase in UHR as the severity of MetS increases. Registration number of MASHAD cohort study: 85134.

Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2.

BACKGROUND AND OBJECTIVE: Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease. MATERIAL AND METHOD: This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35-65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection. RESULT: Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection. CONCLUSION: There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection.

Development of Data Mining Algorithms for Identifying the Best Anthropometric Predictors for Cardiovascular Disease: MASHAD Cohort Study.

INTRODUCTION: Many studies have been published to assess the best anthropometric measurements associated with cardiovascular diseases (CVDs), but controversies still exist. AIM: Investigating the association between CVDs and anthropometric measurements among Iranian adults. METHODS: For a total population of 9354 aged 35 to 65, a prospective study was designed. Anthropometric measurements including ABSI (A Body Shape Index), Body Adiposity Index (BAI), Body Mass Index (BMI), Waist to Height Ratio (WHtR), Body Round Index (BRI), HC (Hip Circumference), Demispan, Mid-arm circumference (MAC), Waist-to-hip (WH) and Waist Circumference (WC) were completed. The association between these parameters and CVDs were assessed through logistic regression (LR) and decision tree (DT) models. RESULTS: During the 6-year follow-up, 4596 individuals (49%) developed CVDs. According to the LR, age, BAI, BMI, Demispan, and BRI, in male and age, WC, BMI, and BAI in female had a significant association with CVDs (p-value < 0.03). Age and BRI for male and age and BMI for female represent the most appropriate estimates for CVDs (OR: 1.07, (95% CI: 1.06, 1.08), 1.36 (1.22, 1.51), 1.14 (1.13, 1.15), and 1.05 (1.02, 1.07), respectively). In the DT for male, those with BRI ≥ 3.87, age ≥ 46 years, and BMI ≥ 35.97 had the highest risk to develop CVDs (90%). Also, in the DT for female, those with age ≥ 54 years and WC ≥ 84 had the highest risk to develop CVDs (71%). CONCLUSION: BRI and age in male and age and BMI in female had the greatest association with CVDs. Also, BRI and BMI was the strongest indices for this prediction.

A neurodynamic approach for nonsmooth optimal power consumption of intelligent and connected vehicles.

This paper investigates a class of power consumption minimization and equalization for intelligent and connected vehicles cooperative system. Accordingly, a distributed optimization problem model related to power consumption and data rate of intelligent and connected vehicles is presented, where the power consumption cost function of each intelligent and connected vehicle may be nonsmooth, and the corresponding control variable is subject to the constraints generated by data acquisition, compression coding, transmission and reception. We propose a distributed subgradient-based neurodynamic approach with projection operator to achieve the optimal power consumption of intelligent and connected vehicles. By differential inclusion and nonsmooth analysis, it is confirmed that the state solution of neurodynamic system converges to the optimal solution of the distributed optimization problem. With the help of the algorithm, all intelligent and connected vehicles asymptotically reach a consensus on an optimal power consumption. Simulation results show that the proposed neurodynamic approach is capable of effectively solving the problem of power consumption optimal control for intelligent and connected vehicles cooperative system.

Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis.

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.

A subgradient-based neurodynamic algorithm to constrained nonsmooth nonconvex interval-valued optimization.

In this paper, a subgradient-based neurodynamic algorithm is presented to solve the nonsmooth nonconvex interval-valued optimization problem with both partial order and linear equality constraints, where the interval-valued objective function is nonconvex, and interval-valued partial order constraint functions are convex. The designed neurodynamic system is constructed by a differential inclusion with upper semicontinuous right-hand side, whose calculation load is reduced by relieving penalty parameters estimation and complex matrix inversion. Based on nonsmooth analysis and the extension theorem of the solution of differential inclusion, it is obtained that the global existence and boundedness of state solution of neurodynamic system, as well as the asymptotic convergence of state solution to the feasible region and the set of LU-critical points of interval-valued nonconvex optimization problem. Several numerical experiments and the applications to emergency supplies distribution and nondeterministic fractional continuous static games are solved to illustrate the applicability of the proposed neurodynamic algorithm.

Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements.

BACKGROUND: The aim of this study was to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM) using machine learning approaches. METHODS: A prospective study was designed for a total population of 9354 (43% men and 57% women) aged 35-65. Anthropometric measurements include weight, height, demispan, Hip Circumference (HC), Mid-arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), Waist-to-height Ratio (WHtR), and Waist-to-hip Ratio (WHR) were completed for all participants. The association was assessed using logistic regression (LR) and decision tree (DT) analysis. Receiver operating characteristic (ROC) curve was performed to evaluate the DT's accuracy, sensitivity, and specificity using R software. RESULTS: Traditionally, 1461 women and 875 men with T2DM (T2DM group). According to the LR, in males, WC and BIA (p-value < 0.001) and in females, demispan and WC (p-value < 0.001) had the highest correlation with T2DM development risk. The DT indicated that WC has the most crucial effect on T2DM development risk, followed by HC, and BAI. CONCLUSIONS: Our results showed that in both men and women, WC was the most important anthropometric factor to predict T2DM.

A pilot study of the effects of crocin on high-density lipoprotein cholesterol uptake capacity in patients with metabolic syndrome: A randomized clinical trial.

A randomized clinical trial high-density lipoprotein (HDL) cholesterol uptake capacity (CUC) is reduced in patients with metabolic syndrome (MetS). We have assessed the effect of crocin supplementation on HDL CUC in patients with MetS. Forty-four subjects with MetS were randomly allocated to one of two groups: one group received placebo and the other group received crocin at a dose of 30 mg (two tablets of 15 mg per day) for 8 weeks. Serum biochemical parameters were measured using an AutoAnalyzer BT3000 (BioTechnica). The modified CUC method is a cell free, simple, and high-throughput assay that used to evaluate HDL CUC of serum samples. The decision tree analysis was undertaken using JMP Pro (SAS) version 13. The mean age of the crocin and placebo groups were 38.97 ± 13.33 and 43.46 ± 12.77 years, respectively. There was a significant increase in serum HDL CUC in the crocin group compared to that of the placebo group in patients with MetS (p-value< 0.05). The decision tree analysis showed that serum HDL functionality was more important variable than HDL-C level in predicting patients with hypertension at baseline (p-value < 0.05). Crocin administration (30 mg for a period of 8 weeks) was found to improve serum HDL CUC in patients with MetS. TRIAL REGISTRATION: IRCT2013080514279N1.

Serum HDL cholesterol uptake capacity in subjects from the MASHAD cohort study: Its value in determining the risk of cardiovascular endpoints.

BACKGROUND: The efficiency of high-density lipoprotein (HDL) to efflux cholesterol contributes to the reverse cholesterol transport (RCT) pathway as one of HDL's proposed functions and depends on the ability of HDL to uptake cholesterol. We aimed to investigate cholesterol uptake capacity (CUC) by a newly developed assay in samples from the MASHAD (Mashhad Stroke and Heart Atherosclerotic Disorders) cohort study. METHOD: The study population comprised 153 individuals developed CVD diagnosed by a specialist cardiologist, over 6 years of follow-up, and 350 subjects without CVD. We used a modified CUC method to evaluate the functionality of HDL in serum samples. RESULT: The CUC assay was highly reproducible with values for inter- and intra-assay variation of 13.07 and 6.65, respectively. The mean serum CUC was significantly lower in the CVD group compared to control (p = 0.01). Although, there were no significant differences in serum HDL-C between the groups and there was no significantly association with risk of progressive CVD. Multivariate logistic regression analysis showed that there was a significantly negative association between CUC and risk of CVD after adjustment for confounding parameters (OR = 0.57, 95% CI = 0.38-0.87, p = 0.009). The CUC was also inversely and independently associated with the risk of CVD event using Cox proportional hazards models analysis (HR = 0.62; 95% CI = 0.41-0.94, p = 0.02). We determined the optimum cutoff value of 1.7 a.u for CUC in the population. Furthermore, the CUC value was important in determining the CVD risk stratification derived from data mining analysis. CONCLUSIONS: Reduced HDL functionality, as measured by CUC, appears to predict CVD in population sample from north-eastern Iran.

Recurrent Neural Network Model: A New Strategy to Solve Fuzzy Matrix Games.

This paper aims to investigate the fuzzy constrained matrix game (MG) problems using the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first in attempting to find a solution for fuzzy game problems using RNN models. For this purpose, a fuzzy game problem is reformulated into a weighting problem. Then, the Karush-Kuhn-Tucker (KKT) optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Moreover, the Lyapunov stability and the global convergence of the RNN model are also confirmed. Finally, three illustrative examples are presented to demonstrate the effectiveness of this approach. The obtained results are compared with the results obtained by the previous approaches for solving fuzzy constrained MG.

A Projection Neural Network for Identifying Copy Number Variants.

The identification of copy number variations (CNVs) helps the diagnosis of many diseases. One major hurdle in the path of CNVs discovery is that the boundaries of normal and aberrant regions cannot be distinguished from the raw data, since various types of noise contaminate them. To tackle this challenge, the total variation regularization is mostly used in the optimization problems to approximate the noise-free data from corrupted observations. The minimization using such regularization is challenging to deal with since it is non-differentiable. In this paper, we propose a projection neural network to solve the non-smooth problem. The proposed neural network has a simple one-layer structure and is theoretically assured to have the global exponential convergence to the solution of the total variation-regularized problem. The experiments on several real and simulated datasets illustrate the reasonable performance of the proposed neural network and show that its performance is comparable with those of more sophisticated algorithms.