Molecular Modelling of Optical Biosensor Phosphorene-Thioguanine for Optimal Drug Delivery in Leukemia Treatment.

Thioguanine is an anti-cancer drug used for the treatment of leukemia. However, thioguanine has weak aqueous solubility and low biocompatibility, which limits its performance in the treatment of cancer. In the present work, these inadequacies were targeted using density functional theory-based simulations. Three stable configurations were obtained for the adsorption of thioguanine molecules on the phosphorene surface, with adsorption energies in the range of -76.99 to -38.69 kJ/mol, indicating physisorption of the drug on the phosphorene surface. The calculated bandgap energies of the individual and combined geometries of phosphorene and thioguanine were 0.97 eV, 2.81 eV and 0.91 eV, respectively. Owing to the physisorption of the drug molecule on the phosphorene surface, the bandgap energy of the material had a direct impact on optical conductivity, which was significantly altered. All parameters that determine the potential ability for drug delivery were calculated, such as the dipole moment, chemical hardness, chemical softness, chemical potential, and electrophilicity index. The higher dipole moment (1.74 D) of the phosphorene-thioguanine complex reflects its higher biodegradability, with no adverse physiological effects.

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ 2 Statistical Model and Gaussian Naive Bayes.

Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ 2 statistical model is used to rank the commonly used 13 HF features. Based on the χ 2 test score, an optimal subset of features is searched using forward best-first search strategy. In the second stage, Gaussian Naive Bayes (GNB) classifier is used as a predictive model. The performance of the newly proposed method (χ 2-GNB) is evaluated by using an online heart disease database of 297 subjects. Experimental results show that our proposed method could achieve a prediction accuracy of 93.33%. The developed method (i.e., χ 2-GNB) improves the HF prediction performance of GNB model by 3.33%. Moreover, the newly proposed method also shows better performance than the available methods in literature that achieved accuracies in the range of 57.85-92.22%.

Correction of failure in linear antenna arrays with greedy sparseness constrained optimization technique.

This paper proposes the correction of faulty sensors using a synthesis of the greedy sparse constrained optimization GSCO) technique. The failure of sensors can damage the radiation power pattern in terms of sidelobes and nulls. The synthesis problem can recover the wanted power pattern with reduced number of sensors into the background of greedy algorithm and solved with orthogonal matching pursuit (OMP) technique. Numerical simulation examples of linear arrays are offered to demonstrate the effectiveness of getting the wanted power pattern with a reduced number of antenna sensors which is compared with the available techniques in terms of sidelobes level and number of nulls.