Active and allosteric site binding MM-QM studies of Methylidene tetracyclo derivative in PCSK9 protein intended to make a safe antilipidemic agent.

Interaction of low-density lipoprotein receptors with proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a vital part in causing atherosclerosis. It is the hidden precursor of clinical myocardial infarction (MI), stroke, CVD and estimates 60% of deaths worldwide. The current need is to design small molecules to prevent the interaction between PCSK9 and LDL receptors. This study aims to evaluate the interaction between Methylidene tetracyclo derivative and PCSK9 protein through conceptual studies and compare the same with the interaction of the standard atorvastatin. Also, a comparative study was performed to analyze the interaction of molecules inside the active and allosteric sites of PCSK9. The RCSB downloaded pdb file 7S5H and the above said ligands were optimized to the level of local minima energy and configured inside the active and allosteric sites. The stability of non-bonded interactions of the complexes were analyzed using Desmond MD simulation studies. The results of docking showed that the Methylidene tetracyclo molecule possesses a two-fold higher affinity of -10.894 kcal/mol in the active site and -10.884 kcal/mol in the allosteric site. The Phe379 amino acid enabled the Methylidene tetracyclo molecule to orient inside the active site. Nine H-bonds with 6 amino acids of allosteric site increased the binding affinity compared to Atorvastatin. The MD simulation studies confirmed the stability of the nonbonded interaction of Methylidene tetracyclo molecule throughout 100 ns. This confirms that the Methylidene tetracyclo molecule will be the better hit as well as the lead molecule to modulate the behavior of PCSK9 protein.Communicated by Ramaswamy H. Sarma.

Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices.

Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision loss and degeneration, early detection and treatment are essential. For the purpose of detecting and classifying diabetic retinopathy on fundus retina images, numerous artificial intelligence-based algorithms have been put forth by the scientific community. Due to its real-time relevance to everyone's lives, smart healthcare is attracting a lot of interest. With the convergence of IoT, this attention has increased. The leading cause of blindness among persons in their working years is diabetic eye disease. Millions of people live in the most populous Asian nations, including China and India, and the number of diabetics among them is on the rise. To provide medical screening and diagnosis for this rising population of diabetes patients, skilled clinicians faced significant challenges. Our objective is to use deep learning techniques to automatically detect blind spots in eyes and determine how serious they may be. We suggest an enhanced convolutional neural network (ECNN) utilizing a genetic algorithm in this paper. The ECNN technique's accuracy results are compared to those of existing approaches like the K-nearest neighbor approach, convolutional neural network, and support vector machine with the genetic algorithm.

Genetic Algorithm-Based Human Mental Stress Detection and Alerting in Internet of Things.

Healthcare is one of the emerging application fields in the Internet of Things (IoT). Stress is a heightened psycho-physiological condition of the human that occurs in response to major objects or events. Stress factors are environmental elements that lead to stress. A person's emotional well-being can be negatively impacted by long-term exposure to several stresses affecting at the same time, which can cause chronic health issues. To avoid strain problems, it is vital to recognize them in their early stages, which can only be done through regular stress monitoring. Wearable gadgets offer constant and real information collecting, which aids in experiencing an increase. An investigation of stress discovery using detecting devices and deep learning-based is implemented in this work. This proposed work investigates stress detection techniques that are utilized with detecting hardware, for example, electroencephalography (EEG), photoplethysmography (PPG), and the Galvanic skin reaction (GSR) as well as in various conditions including traveling and learning. A genetic algorithm is utilized to separate the features, and the ECNN-LSTM is utilized to classify the given information by utilizing the DEAP dataset. Before that, preprocessing strategies are proposed for eliminating artifacts in the signal. Then, the stress that is beyond the threshold value is reached the emergency/alert state; in that case, an expert who predicts the mental stress sends the report to the patient/doctor through the Internet. Finally, the performance is evaluated and compared with the traditional approaches in terms of accuracy, f1-score, precision, and recall.