Antioxidant and Antidiabetic Potential of Ormocarpum cochinchinense (Lour.) Merr. Leaf: An Integrated In vitro and In silico Approach

Diabetes is a prevalent metabolic disorder associated with various complications. Inhibition of α-glucosidase and α-amylase enzymes is an effective strategy for managing non-insulin-dependent diabetes mellitus. This study aimed to investigate the antioxidant and antidiabetic potential of Ormocarpum cochinchinense leaf through in vitro and in silico approaches. The methanol extract exhibited the highest phenolic and flavonoid content over solvent extracts aqueous, acetone, hexane, and chloroform, the same has been correlating with strong antioxidant activity. Furthermore, the methanol extract demonstrated significant inhibitory effects on α-amylase and α-glucosidase enzymes, indicating its potential as an antidiabetic agent. Molecular docking analysis identified compounds, including myo-inositol, with favorable binding energies comparable to the standard drug metformin. The selected compounds displayed strong binding affinity towards α-amylase and α-glucosidase enzymes. Structural dynamics analysis revealed that myo-inositol formed a more stable complex with the enzymes. These findings suggest that O. cochinchinense leaf possesses antioxidant and antidiabetic properties, making it a potential source for developing therapeutic agents.

Genomic sequence analysis of lung infections using artificial intelligence technique

Attributable to the modernization of Artificial Intelligence (AI) procedures in healthcare services, various developments including Support Vector Machine (SVM), and profound learning. For example, Convolutional Neural systems (CNN) have prevalently engaged in a significant job of various classificational investigation in lung malignant growth, and different infections. In this paper, Parallel based SVM (P-SVM) and IoT has been utilized to examine the ideal order of lung infections caused by genomic sequence. The proposed method develops a new methodology to locate the ideal characterization of lung sicknesses and determine its growth in its early stages, to control the growth and prevent lung sickness. Further, in the investigation, the P-SVM calculation has been created for arranging high-dimensional distinctive lung ailment datasets. The data used in the assessment has been fetched from real-time data through cloud and IoT. The acquired outcome demonstrates that the developed P-SVM calculation has 83% higher accuracy and 88% precision in characterization with ideal informational collections when contrasted with other learning methods.

Image analysis and data processing for COVID-19

COVID-19 is a deadly disease caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2). It was first discovered by variations in the respirational and immune systems of a patient who died of a severe acute respiratory syndrome. The first country heavily affected by coronavirus was China. The first case was detected in Wuhan city, China. This virus spreads rapidly from person to person. Based on laboratory tests for coronavirus disease in humans, it is suspected that bats are the natural source of spread of large varieties of virus. The two major viruses, SARS-CoV and Middle East respiratory syndrome coronavirus, originated from the bat; it caused an unexpected disease outbreak in the 21st century throughout the world. Researchers and doctors have investigated COVID in cadavers. The virus was detected in lung, trachea/bronchus, stomach, small intestine, distal convoluted renal tubule, sweat gland, pancreas, adrenal gland, parathyroid, pituitary, cerebrum, and liver. However, it was not noted in bone marrow, heart, aorta, cerebellum, thyroid, testis, esophagus, spleen, lymph node, ovary, muscle, or uterus. This chapter briefly discusses image analysis and data processing used to accelerate COVID-19 detection and support the efforts of researchers and physician to help infected people and break the chain of disease from person to person.

Monitoring and analysis of the recovery rate of Covid-19 positive cases to prevent dangerous stage using IoT and sensors

Purpose

This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease.

Design/methodology/approach

COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world.

Findings

This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate.

Originality/value

From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

Impurity profile study of zaleplon

Zaleplon is a pyrazolopyrimidine derivative and possesses sedative and hypnotic properties. Seven unknown impurities in zaleplon bulk drug at levels below 0.1% were detected by reverse-phase high performance liquid chromatography (HPLC). The starting material, 3-amino-4-cyanopyrazole and an intermediate, N-[3-[3-(dimethylamino)-1-oxo-2-propenyl]-phenyl]-N-ethylacetamide (DOPEA) were also present in the sample at a level below 0.1%. The molecular weights of impurities were determined by LC-MS analysis. These impurities were isolated from crude samples of zaleplon using reverse-phase preparative HPLC. Based on the spectral data the structures of these impurities were characterized as, N-(3-(3-(4-amino-2H-pyrazolo [3,4-d]pyrimidin-6-yl) pyrazolo[1,5-a] pyrimidin-7-yl)phenyl)-N-ethylacetamide (impurity I); N-[3-(3-carboxamidopyrazolo[1,5-a]pyrimidin-7-yl)phenyl]-N-ethylacetamide (impurity II); N-[3-(3-cyanopyrazolo[1,5-a]pyrimidin-7-yl)phenyl]acetamide (impurity III); N-[3-(3-cyanopyrazolo [1,5-a]pyrimidin-7-yl)phenyl]-N-methylacetamide (impurity IV); N-[3-(3-cyanopyrazolo[1,5-a] pyrimidin-5-yl)phenyl]-N-ethylacetamide (impurity V); N-[3-(3-cyanopyrazolo[1,5-a] pyrimidin-7-yl)phenyl]-N-ethylamine (impurity VI); N-[3-(3-cyano-6-[(E)-3-((N-ethyl-N-acetyl)amino)phenyl-3-oxoprop-1-enyl] pyrazolo[1,5-a]pyrimidin-7-yl) phenyl]-N-ethylacetamide (impurity VII). Structural elucidation of all impurities by spectral data ((1)H NMR, (13)C NMR, MS and IR) and formation of these impurities are discussed in detail.