Adherence to the nordic diet is associated with anxiety, stress, and depression in recovered COVID-19 patients, a case-control study.

BACKGROUND: Follow-up of COVID-19 recovered patients to discover important adverse effects on other organs is required. The psychological health of COVID-19 patients may be affected after recovery. AIM: We aimed to evaluate the association between adherence to the Nordic diet (ND) and psychological symptoms caused by COVID-19 after recovery. METHOD: Dietary data on 246 qualified adults (123 cases and 123 controls). The dietary intake in this case-control study was calculated by a reliable and valid food frequency questionnaire (FFQ). Depression Anxiety Stress Scale (DASS), Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and Short-Form Health Survey (SF-36) were used to analyze participant's anxiety, stress, depression, sleep quality, insomnia, and quality of life of participants. RESULTS: There was a significant inverse relationship between total anxiety, stress, and depression scores and the intake of whole grains (P < 0.05). Furthermore, there was a significant inverse association between depression and fruit intake (P < 0.05). A significant negative correlation was found between insomnia and sleep quality and the intake of root vegetables (P < 0.05). In the multinomial-regression model, a significant association between the Nordic diet and anxiety, stress, and depression was found only in the case group (OR = 0.719, 95% CI 0.563-0.918, p-value = 0.008; OR = 0.755, 95% CI 0.609-0.934, P-value = 0.010, and, OR = 0.759, 95% CI 0.602-0.956, P-value = 0.019 respectively). CONCLUSION: Adherence to the Nordic diet might reduce anxiety, stress, and depression in recovered COVID-19 patients.

Using meta-learning to recommend an appropriate time-series forecasting model.

BACKGROUND: There are various forecasting algorithms available for univariate time series, ranging from simple to sophisticated and computational. In practice, selecting the most appropriate algorithm can be difficult, because there are too many algorithms. Although expert knowledge is required to make an informed decision, sometimes it is not feasible due to the lack of such resources as time, money, and manpower. METHODS: In this study, we used coronavirus disease 2019 (COVID-19) data, including the absolute numbers of confirmed, death and recovered cases per day in 187 countries from February 20, 2020, to May 25, 2021. Two popular forecasting models, including Auto-Regressive Integrated Moving Average (ARIMA) and exponential smoothing state-space model with Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS) were used to forecast the data. Moreover, the data were evaluated by the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) criteria to label time series. The various characteristics of each time series based on the univariate time series structure were extracted as meta-features. After that, three machine-learning classification algorithms, including support vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used as meta-learners to recommend an appropriate forecasting model. RESULTS: The finding of the study showed that the DT model had a better performance in the classification of time series. The accuracy of DT in the training and testing phases was 87.50% and 82.50%, respectively. The sensitivity of the DT algorithm in the training phase was 86.58% and its specificity was 88.46%. Moreover, the sensitivity and specificity of the DT algorithm in the testing phase were 73.33% and 88%, respectively. CONCLUSION: In general, the meta-learning approach was able to predict the appropriate forecasting model (ARIMA and TBATS) based on some time series features. Considering some characteristics of the desired COVID-19 time series, the ARIMA or TBATS forecasting model might be recommended to forecast the death, confirmed, and recovered trend cases of COVID-19 by the DT model.

A large population-based study on the prevalence of electrocardiographic abnormalities: A result of Mashhad stroke and heart atherosclerotic disorder cohort study.

BACKGROUND: Twelve-lead electrocardiogram (ECG) is a common and inexpensive tool for the diagnostic workup of patients with suspected cardiovascular disease, both in clinical and epidemiological settings. The present study was designed to evaluate ECG abnormalities in Mashhad population. METHODS: ECGs were taken as part of MASHAD cohort study (phase1) and were coded according to the Minnesota coding criteria. Data were analyzed using SPSS. RESULTS: Total 9035 ECGs were available for final analysis including 3615 (40.0%) male and 5420 (60.0%) female. Among ECG abnormalities precordial Q wave, major T-wave abnormalities, inferior Q wave, sinus bradycardia, and left axis deviation were the most prevalent abnormalities. The frequency of precordial and inferior Q wave, inferior QS pattern, major and minor ST abnormalities, major and minor T abnormalities, Wolff-Parkinson-White and Brugada pattern, sinus bradycardia, sinus tachycardia, left axis deviation, ST elevation, and tall T wave were significantly different between two genders. Moreover, the frequency of Q wave in precordial and aVL leads, QS pattern in precordial and inferior leads, major and minor T-wave abnormalities, Wolff-Parkinson-White, atrial fibrillation, sinus bradycardia, left axis deviation, and ST elevation were significantly different in different age groups. A comparison of the heart rate, P-wave duration, and QRS duration between men and women indicated that there was a significant difference. CONCLUSIONS: Our finding indicated that the prevalence ECG abnormalities are different between men and women and also it varied in different age groups.

Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique.

Previous studies have proposed that heat shock proteins 27 (HSP27) and its anti-HSP27 antibody titers may play a crucial role in several diseases including cardiovascular disease. However, available studies has been used simple analytical methods. This study aimed to determine the factors that associate serum anti-HSP27 antibody titers using ensemble machine learning methods and to demonstrate the magnitude and direction of the predictors using PFI and SHAP methods. The study employed Python 3 to apply various machine learning models, including LightGBM, CatBoost, XGBoost, AdaBoost, SVR, MLP, and MLR. The best models were selected using model evaluation metrics during the K-Fold cross-validation strategy. The LightGBM model (with RMSE: 0.1900 ± 0.0124; MAE: 0.1471 ± 0.0044; MAPE: 0.8027 ± 0.064 as the mean ± sd) and the SHAP method revealed that several factors, including pro-oxidant-antioxidant balance (PAB), physical activity level (PAL), platelet distribution width, mid-upper arm circumference, systolic blood pressure, age, red cell distribution width, waist-to-hip ratio, neutrophils to lymphocytes ratio, platelet count, serum glucose, serum cholesterol, red blood cells were associated with anti-HSP27, respectively. The study found that PAB and PAL were strongly associated with serum anti-HSP27 antibody titers, indicating a direct and indirect relationship, respectively. These findings can help improve our understanding of the factors that determine anti-HSP27 antibody titers and their potential role in disease development.

Investigating the association of acute kidney injury (AKI) with COVID-19 mortality using data-mining scheme.

COVID-19 has caused significant challenges in kidney research and disease management. Data mining techniques such as logistic regression (LR) and decision tree (DT) were used to model data. All analyses were performed using SPSS 25 and Python 3. The incidence of acute kidney injury (AKI) was 14.1% and the overall mortality risk was 13% among COVID-19 patients. The mortality was associated with, AKI, age, marital status, smoking status, heart failure, chronic obstructive pulmonary disease, malignancy, and SPO2 level using LR. The accuracy, sensitivity, specificity, and area under the curve of the DT (and LR) classifier were 70% (85%), 73% (75%), 78% (79%), and 77% (81%), respectively. Based on the DT model, the variable most significantly associated with COVID-19 mortality was AKI followed by age, high WBC count, BMI, and lymphocyte count. It was concluded that the incidence of AKI was high, and AKI was identified as one of the important factors that played an effective role in mortality due to COVID-19.

Clustering of the causes of death in Northeast Iran: a mixed growth modeling.

BACKGROUND: Processing and analyzing data related to the causes of mortality can help to clarify and monitor the health status, determine priorities, needs, deficiencies, and developments in the health sector in research and implementation areas. In some cases, the statistical population consists of invisible sub-communities, each with a pattern of different trends over time. In such cases, Latent Growth Mixture Models (LGMM) can be used. This article clusters the causes of individual deaths between 2015 and 2019 in Northeast Iran based on LGMM. METHOD: This ecological longitudinal study examined all five-year mortality in Northeast Iran from 2015 to 2019. Causes of mortality were extracted from the national death registration system based on the ICD-10 classification. Individuals' causes of death were categorized based on LGMM, and similar patterns were placed in one category. RESULTS: Out of the total 146,100 deaths, ischemic heart disease (21,328), malignant neoplasms (17,613), cerebrovascular diseases (11,924), and hypertension (10,671) were the four leading causes of death. According to statistical indicators, the model with three classes was the best-fit model, which also had an appropriate interpretation. In the first class, which was also the largest class, the pattern of changes in mortality due to diseases was constant (n = 98, 87.50%). Second-class diseases had a slightly upward trend (n = 10, 8.92%), and third-class diseases had a completely upward trend (n = 4, 3.57%). CONCLUSIONS: Identifying the rising trends of diseases leading to death using LGMM can be a suitable tool for the prevention and management of diseases by managers and health policy. Some chronic diseases are increasing up to 2019, which can serve as a warning for health policymakers in society.

Predictive factors of breast cancer mammography screening among Iranian women.

Introduction: Understanding the factors that influence women's cancer screening behavior is crucial in reducing cancer mortality through early detection. Therefore, the objective of this study was to examine the status of mammography and related factors among women who presented to the health centers of Khorasan Razavi province, Iran. Methods: For this study, a sample of 251,011 women who visited healthcare centers affiliated with Mashhad University of Medical Sciences was selected. The study examined several variables, including sociodemographic information, current smoking, nutrition status, and physical exercise. All analyses were performed using Python programming language and SPSS software. Furthermore, to handle imbalanced data, we used SMOTE balancing method that is an oversampling method and produce synthetic samples from the minority class. Results: The factors of age, education, being employed, having children, family history of cancer, physical activity, smoking status, and diet were all predictors of mammography screening. Moreover, findings showed that age and family history of breast cancer were most important variables to predict mammography status, respectively. Conclusions: By examining various variables such as dietary habits, exercise, smoking, and demographic properties, it sheds light on the relationships between these factors and mammography screening. This provides valuable insights into the associations between breast cancer screening behavior and preventive lifestyle behaviors. By targeting both preventive lifestyle choices and breast cancer screening behaviors, interventions can effectively promote positive changes in behavior and ultimately reduce the incidence and impact of breast cancer.

The visceral adiposity index and lipid accumulation product as predictors of cardiovascular events in normal weight subjects.

INTRODUCTION: Visceral adipose tissue (VAT) has an important role in the incidence of cardiovascular disease (CVD) than obesity by itself. The visceral adiposity index (VAI) and lipid accumulation product (LAP) are surrogate indices for measuring VAT. The aimed of this study was to investigate the association of these markers with cardiovascular events among populations with different BMI category in Mashhad, northeast of Iran. METHOD: The present study comprised a prospective cohort of 9685 men and women (35-65 years) who were recruited from MASHAD study. BMI category was defined as normal weight (BMI <25), over weight (25 ≤ BMI<30) and obese (BMI≥30). Demographic, laboratory evaluations, anthropometric and metabolic parameters were performed. Logistic and Cox regression analyses were used to determine the association and risk of cardiovascular events with VAT and LAP. RESULTS: The mean VAI and LAP in CVD patients were significantly higher than in healthy ones in all 3 groups. In terms of CVD event prediction, VAI and LAP had significant association with the incidence of CVD in the second (RR (95% CI): 2.132 (1.047-4.342) and 2.701 (1.397-5.222), respectively) and third tertiles (RR (95% CI): 2.541 (1.163-5.556) and 2.720 (1.159-6.386), respectively) in the normal group, but this association was only found in the third tertiles (RR (95% CI): 2.448 (1.205-4.971) and 2.376 (1.086-5.199), respectively) in the overweight group. The result couldn't find this association for the obese group. CONCLUSION: In this study, we found that there was a significant association between LAP and VAI and cardiovascular events in normal weight and over-weight groups; however, no significant relationship was found in the obese group.

Factors Associated With Trabecular Bone Score and Bone Mineral Density; A Machine Learning Approach.

INTRODUCTION: Bone indexes including trabecular bone score (TBS) and bone mineral density (BMD) have been shown to be associated with wide spectrum of variables including physical activity, vitamin D, liver enzymes, biochemical measurements, mental and sleep disorders, and quality of life. Here we aimed to determine the most important factors related to TBS and BMD in SUVINA dataset. METHODS: Data were extracted from the Survey of Ultraviolet Intake by Nutritional Approach (SUVINA study) including all 306 subjects entered this survey. All the available parameters in the SUVINA database were included the analysis. XGBoost modeler software was used to define the most important features associated with bone indexes including TBS and BMD in various sites. RESULTS: Applying XGBoost modeling for 4 bone indexes indicated that this algorithm could identify the most important variables in relation to bone indexes with an accuracy of 92%, 93%, 90% and 90% respectively for TBS T-score, lumbar Z-score, neck of femur Z-score and Radius Z-score. Serum vitamin D, pro-oxidant-oxidant balance (PAB) and physical activity level (PAL) were the most important factors related to bone indices in different sites of the body. CONCLUSIONS: Our findings indicated that XGBoost could identify the most important variables with an accuracy of >90% for TBS and BMD. The most important features associated with bone indexes were serum vitamin D, PAB and PAL.

Cesarean delivery in Iran: a population-based analysis using the Robson classification system.

BACKGROUND: The rise of Cesarean Sections (CS) is a global concern. In Iran, the rate of CS increased from 40.7% in 2005 to 53% in 2014. This figure is even higher in the private sector. OBJECTIVE: To analyze the CS rates in the last 2 years using the Robson Classification System in Iran. METHODS: A retrospective analysis of all in-hospital electronically recorded deliveries in Iran was conducted using the Robson classification. Comparisons were made in terms of the type of hospital, CS rate, and obstetric population, and contributions of each group to the overall cesarean deliveries were reported. RESULTS: Two million three hundred twenty-two thousand five hundred women gave birth, 53.6% delivered through CS. Robson group 5 was the largest contributing group to the overall number of cesarean deliveries (47.1%) at a CS rate of 98.4%. Group 2 and 1 ranked the second and third largest contributing groups to overall CSs (20.6 and 10.8%, respectively). The latter groups had CS rates much higher than the WHO recommendation of 67.2 and 33.1%, respectively. "Fetal Distress" and "Undefined Indications" were the most common reasons for cesarean deliveries at CS rates of 13.6 and 13.4%, respectively. There was a significant variation in CS rate among the three types of hospitals for Robson groups 1, 2, 3, 4, and 10. CONCLUSION: The study revealed significant variations in CS rate by hospital peer-group, especially for the private maternity units, suggesting the need for further attention and audit of the Robson groups that significantly influence the overall CS rate. The study results will help policymakers identify effective strategies to reduce the CS rate in Iran, providing appropriate benchmarking to compare obstetric care with other countries that have better maternal and perinatal outcomes.

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods.

BACKGROUND: The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence. METHODS: In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020. RESULTS: Our findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2484 new confirmed and 114 new death cases of COVID-19. CONCLUSION: According to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.