Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient’s age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.

OBJECTIVES: Although vaccination has started, coronavirus disease 2019 (COVID-19) poses a continuing threat to public health. Therefore, in addition to vaccination, the use of supplements to support the immune system may be important. The purpose of this study was to synthesize evidence on the possible effect of low serum vitamin D levels (25[OH]D<20 ng/mL or 50 nmol/L) on COVID-19 infection and outcomes.METHODS: We searched Google Scholar, PubMed, Scopus, Web of Science, and ScienceDirect without any language restrictions for articles published between January 1 and December 15, 2020. We performed 3 meta-analyses (called vitamin D and COVID-19 infection meta-analysis [D-CIMA], vitamin D and COVID-19 severity meta-analysis [D-CSMA], and vitamin D and COV ID-19 mortality meta-analysis [D-CMMA] for COVID-19 infection, severity, and mortality, respectively) to combine odds ratio values according to laboratory measurement units for vitamin D and the measured serum 25(OH)D level.RESULTS: Twenty-one eligible studies were found to be relevant to the relationship between vitamin D and COVID-19 infection/outcomes (n=205,869). The D-CIMA meta-analysis showed that individuals with low serum vitamin D levels were 1.64 times (95% confidence interval [CI], 1.32 to 2.04; p<0.001) more likely to contract COVID-19. The D-CSMA meta-analysis showed that people with serum 25(OH)D levels below 20 ng/mL or 50 nmol/L were 2.42 times (95% CI, 1.13 to 5.18; p=0.022) more likely to have severe COVID-19. The D-CMMA meta-analysis showed that low vitamin D levels had no effect on COVID-19 mortality (OR, 1.64; 95% CI, 0.53 to 5.06, p=0.390).CONCLUSIONS: According to our results, vitamin D deficiency may increase the risk of COVID-19 infection and the likelihood of severe disease. Therefore, we recommend vitamin D supplementation to prevent COVID-19 and its negative outcomes.

Gene expression data typically are large, complex, and highly noisy. Their dimension is high with several thousand genes (i.e., features) but with only a limited number of observations (i.e., samples). Although the classical principal component analysis (PCA) method is widely used as a first standard step in dimension reduction and in supervised and unsupervised classification, it suffers from several shortcomings in the case of data sets involving undersized samples, since the sample covariance matrix degenerates and becomes singular. In this paper we address these limitations within the context of probabilistic PCA (PPCA) by introducing and developing a new and novel approach using maximum entropy covariance matrix and its hybridized smoothed covariance estimators. To reduce the dimensionality of the data and to choose the number of probabilistic PCs (PPCs) to be retained, we further introduce and develop celebrated Akaike’s information criterion (AIC), consistent Akaike’s information criterion (CAIC), and the information theoretic measure of complexity (ICOMP) criterion of Bozdogan. Six publicly available undersized benchmark data sets were analyzed to show the utility, flexibility, and versatility of our approach with hybridized smoothed covariance matrix estimators, which do not degenerate to perform the PPCA to reduce the dimension and to carry out supervised classification of cancer groups in high dimensions.

Objectives: Vaccination to the population is the most effective way to get rid of the COVID-19 pandemic, which currently has no effective treatment. For the high vaccine acceptance rate, it is important to determine the vaccine willingness of individuals and the barriers to vaccination. We aimed to determine the acceptability of the COVID-19 vaccine and the effect of attitudes, perception and beliefs on vaccination willingness among medical students. Methods: In February 2020, a cross-sectional survey was conducted online among medical student. A total of 734 medical students out of 980 medical students answered the questionnaire forms (response rate: 75.4%). The online questionnaire collected socio-demographic characteristics, attitudes, risk perception and beliefs about the COVID-19 pandemic and vaccine. Vaccine hesitancy, rejection and acceptance were determined by self-report of medical students. Results: Total of 739 medical students included study. Students declared that 60.1% (n = 444) of them to be willing to vaccinated, 14.2% (n = 105) refused and 25.7% (n = 190) were not sure. Covid-19 willingness to be vaccinated was higher in female (p = 0.005), clinical students (p < 0.001), those who ever tested for COVID-19 (p = 0.002), those who had covid-19 infection history in the family (p = 0.043), those who had COVID-19 and COVID-19 vaccine education (p = 0.001). Positive attitudes skor and higher knowledge scores were related with highly willingness to be vaccinated. High beliefs score (positive beliefs) was found to be associated with a higher willingness to be vaccinated (p < 0.001). Conclusions: Although the majority of the study population willingness to be vaccinated, one-third of medical students are hesitant about vaccination. Education, positive attitude and beliefs are related to higher willingness to vaccination.The COVID-19 pandemic immunization programs should be designed to remove barriers to negative vaccine beliefs and perception.

Spectral data often contains a large number of variables that are highly correlated. Although Partial Least Squares (PLS) regression is specifically designed to handle issues arising from limited sample sizes, its effectiveness may still diminish in extremely small datasets, making it challenging to construct a calibration model with high predictive performance. This study introduces a new framework, the Genetic Algorithm and Hybrid Regression Model (GAHRM), designed specifically for variable selection and regression in high‐dimensional, low‐sample‐size spectral datasets. GAHRM integrates Hybrid Regression, which constructs regression models using a covariance structure that is first stabilized through Thomaz Stabilization and then regularized, with Genetic Algorithm (GA), an efficient optimization technique for selecting the best subset of variables among a vast model space. Unlike traditional approaches that rely on exhaustive search for model selection criteria, GAHRM leverages GA to navigate the exponentially large search space, enabling computationally feasible and robust model construction. The effectiveness of GAHRM was validated on the benchmark “Gasoline” dataset, where it demonstrated superior performance compared to PLS in terms of prediction accuracy and model selection efficiency. These results highlight GAHRM as a powerful alternative for wavelength selection and calibration modeling in challenging data scenarios.

Riociguat is a drug that improves hemodynamic parameters and increases the exercise capacity of patients with chronic thromboembolic pulmonary hypertension (CTEPH). This meta‐analysis evaluated cohort studies that measured changes in parameters such as the 6‐minute walk distance (6MWD), mean pulmonary arterial pressure (mPAP), pulmonary vascular resistance (PVR), cardiac output (CO), and pulmonary arterial wedge pressure (PAWP). The study utilized cohort studies with a paired samples group design to measure the changes in these parameters. We searched for articles containing the keywords “Riociguat” and “Chronic thromboembolic pulmonary hypertension” in their titles in PubMed, Web of Science, Scopus, and ScienceDirect databases until May 2024. We conducted five meta‐analyses to combine the mean difference values. We identified nine studies that examine the effects of Riociguat on patients. Analyzing data from 565 patients revealed that Riociguat increases the distance walked during the 6MWD test by an average of 35.86 m. After analyzing data from 717 patients, it was found that Riociguat reduces mPAP by an average of 9.23 mm Hg. Analyzing data from 586 patients, it was found that Riociguat reduces PVR by an average of 220.11 . Moreover, analyzing data from 643 patients showed that Riociguat increases CO by an average of 0.49 L/min. Finally, after analyzing data from 645 patients, it was concluded that Riociguat treatment did not have a statistically significant effect on PAWP. Our findings indicate that Riociguat improved certain hemodynamic parameters and exercise capacity in CTEPH patients.