Spike glycoprotein (Sgp) is liable for binding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the host receptors. Since Sgp is the main target for vaccine and drug designing, elucidating its mutation pattern could help in this regard. This study is aimed at investigating the correspondence of specific residues to the SgpSARS-CoV-2 functionality by explorative interpretation of sequence alignments. Centrality analysis of the Sgp dissects the importance of these residues in the interaction network of the RBD-ACE2 (receptor-binding domain) complex and furin cleavage site. Correspondence of RBD to threonine500 and asparagine501 and furin cleavage site to glutamine675, glutamine677, threonine678, and alanine684 was observed; all residues are exactly located at the interaction interfaces. The harmonious location of residues dictates the RBD binding property and the flexibility, hydrophobicity, and accessibility of the furin cleavage site. These species-specific residues can be assumed as real targets of evolution, while other substitutions tend to support them. Moreover, all these residues are parts of experimentally identified epitopes. Therefore, their substitution may affect vaccine efficacy. Higher rate of RBD maintenance than furin cleavage site was predicted. The accumulation of substitutions reinforces the probability of the multi-host circulation of the virus and emphasizes the enduring evolutionary events.

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Missing data is one of the most important causes in reduction of classification accuracy. Many real datasets suffer from missing values, especially in medical sciences. Imputation is a common way to deal with incomplete datasets. There are various imputation methods that can be applied, and the choice of the best method depends on the dataset conditions such as sample size, missing percent, and missing mechanism. Therefore, the better solution is to classify incomplete datasets without imputation and without any loss of information. The structure of the “Bayesian additive regression trees” (BART) model is improved with the “Missingness Incorporated in Attributes” approach to solve its inefficiency in handling the missingness problem. Implementation of MIA-within-BART is named “BART.m”. As the abilities of BART.m are not investigated in classification of incomplete datasets, this simulation-based study aimed to provide such resource. The results indicate that BART.m can be used even for datasets with 90 missing present and more importantly, it diagnoses the irrelevant variables and removes them by its own. BART.m outperforms common models for classification with incomplete data, according to accuracy and computational time. Based on the revealed properties, it can be said that BART.m is a high accuracy model in classification of incomplete datasets which avoids any assumptions and preprocess steps.

To investigate the longitudinal association of different phenotypes of diabetes and obesity with the incidence of cardiovascular disease (CVD), CVD- and all-cause mortality. A total of 5432 adults, aged ≥ 35 years and free of CVD were included in this cohort study. Diabesity phenotypes were defined in six categories based on the presence of diabetes (normal (NG), prediabetes and diabetes) and obesity (obese, non-obese). Fasting blood sugar, 2-h post prandial glucose, or using anti-diabetic medicines were used to define diabetes, and body mass index and waist circumference were used to define obesity. Cox proportional hazards models were used to estimate hazard ratios (HRs) for incident CVD, CVD- and all-cause mortality across these categories. After a median follow-up of 11.25 years, 819 CVD cases, 181 CVD deaths and 488 all-cause deaths occurred. In multivariable-adjusted models and irrespective of obesity definition, the phenotypes of normal glucose-obese, prediabetes-obese and pre-diabetes-non obese were not associated with CVD incidence in comparison with NG-non obese phenotype, however, the phenotypes of diabesity, either defined by general or abdominal obesity, were associated with increased risk of incident CVD events (HR = 1.42, 95% CI 1.01, 1.99, and HR = 1.46, 95% CI 1.07, 1.98, respectively). These findings were sex-specific and only in men with a phenotype of abdominal obesity-diabetes, a positive link was observed for CVD incidence (HR = 1.60, 95% CI 1.01, 2.52). No significant association was found between diabesity and death from CVD or all causes. Diabesity is a predictor of CVD and stroke incidence, but not CVD or all-cause mortality, among Iranians. This association is more pronounced amongst men than women.

Background Cardiovascular diseases (CVD) are the predominant cause of early death worldwide. Identification of people with a high risk of being affected by CVD is consequential in CVD prevention. This study adopts Machine Learning (ML) and statistical techniques to develop classification models for predicting the future occurrence of CVD events in a large sample of Iranians. Methods We used multiple prediction models and ML techniques with different abilities to analyze the large dataset of 5432 healthy people at the beginning of entrance into the Isfahan Cohort Study (ICS) (1990–2017). Bayesian additive regression trees enhanced with “missingness incorporated in attributes” (BARTm) was run on the dataset with 515 variables (336 variables without and the remaining with up to 90% missing values). In the other used classification algorithms, variables with more than 10% missing values were excluded, and MissForest imputes the missing values of the remaining 49 variables. We used Recursive Feature Elimination (RFE) to select the most contributing variables. Random oversampling technique, recommended cut-point by precision-recall curve, and relevant evaluation metrics were used for handling unbalancing in the binary response variable. Results This study revealed that age, systolic blood pressure, fasting blood sugar, two-hour postprandial glucose, diabetes mellitus, history of heart disease, history of high blood pressure, and history of diabetes are the most contributing factors for predicting CVD incidence in the future. The main differences between the results of classification algorithms are due to the trade-off between sensitivity and specificity. Quadratic Discriminant Analysis (QDA) algorithm presents the highest accuracy (75.50 ± 0.08) but the minimum sensitivity (49.84 ± 0.25); In contrast, decision trees provide the lowest accuracy (51.95 ± 0.69) but the top sensitivity (82.52 ± 1.22). BARTm.90% resulted in 69.48 ± 0.28 accuracy and 54.00 ± 1.66 sensitivity without any preprocessing step. Conclusions This study confirmed that building a prediction model for CVD in each region is valuable for screening and primary prevention strategies in that specific region. Also, results showed that using conventional statistical models alongside ML algorithms makes it possible to take advantage of both techniques. Generally, QDA can accurately predict the future occurrence of CVD events with a fast (inference speed) and stable (confidence values) procedure. The combined ML and statistical algorithm of BARTm provide a flexible approach without any need for technical knowledge about assumptions and preprocessing steps of the prediction procedure.