Dietary biogenic selenium nanoparticles improve growth and immune-antioxidant indices without inducing inflammatory responses in Nile tilapia.

The present study evaluated the use of green-synthesized selenium nanoparticles (SeNPs), using the microalgae Pediastrum boryanum as a diet additive in aquaculture to improve the growth performance, health, and immune response of Nile tilapia. Nile tilapia were fed different concentrations of green SeNPs (79.26 nm) as follows: 0, 0.

Exploring the role of topological descriptors to predict physicochemical properties of anti-HIV drugs by using supervised machine learning algorithms.

In order to explore the role of topological indices for predicting physio-chemical properties of anti-HIV drugs, this research uses python program-based algorithms to compute topological indices as well as machine learning algorithms. Degree-based topological indices are calculated using Python algorithm, providing important information about the structural behavior of drugs that are essential to their anti-HIV effectiveness. Furthermore, machine learning algorithms analyze the physio-chemical properties that correspond to anti-HIV activities, making use of their ability to identify complex trends in large, convoluted datasets.

Dietary Pediastrum boryanum microalgal extract improves growth, enhances immunity, and regulates immune-related genes in Nile tilapia.

Background: Identifying alternative sustainable feed sources with high nutritional values is crucial for the future of environmentally and socially responsible aquaculture. In this regard, microalgae have been proven to have positive effects on fish health, which overwhelmed our interest in this study.

Methods: Pediastrum boryanum (P.

A python based algorithmic approach to optimize sulfonamide drugs via mathematical modeling.

This article explores the structural properties of eleven distinct chemical graphs that represent sulfonamide drugs using topological indices by developing python algorithm. To find significant relationships between the topological characteristics of these networks and the characteristics of the associated sulfonamide drugs. We use quantitative structure-property relationship (QSPR) approaches.

Mathematical modeling and topological graph description of dominating David derived networks based on edge partitions.

Chemical graph theory is a well-established discipline within chemistry that employs discrete mathematics to represent the physical and biological characteristics of chemical substances. In the realm of chemical compounds, graph theory-based topological indices are commonly employed to depict their geometric structure. The main aim of this paper is to investigate the degree-based topological indices of dominating David derived networks (DDDN) and assess their effectiveness.