A new class of biologically active nanocomposites and modified polymers based on poly (vinyl alcohol) (PVA) with some organic compounds [II, IV, V and VI] were synthesized using silver nanoparticles (Ag-NPs). All compounds were synthesized using nucleophilic substitution interactions and characterized by FTIR, DSC and TGA. The biological activity of the modified polymers was evaluated against: gram (+) (staphylococcus aureus) and gram (-): (Es cherichia coli bacteria). Antimicrobial films are developed based on modified poly (vinyl alcohol) MPVA and Ag-NPs nanoparticles. The nanocomposites and modified polymers showed better antibacterial activities against Escherichia coli (Gram negative) than against Staphyloc

Amino acids are the basic building block for peptides and proteins. They are raw materials for generating hormones, purines, pyrimidines and vitamins. Amino acids also provide the body with energy through their carbon structures. The study analyzed the amino acid in the kidneys of the albino mice embryo at 17 and 19 gestation days, using a high-performance liquid chromatography device (HPLC). Samples were obtained after removing them from the embryo and placing them in an ice bath to prevent cell lysis and acid loss. The study found 18 amino acids in the kidneys of the albino mice embryo. They are Asparagine (Asn), Glutamine (Glu), Serine (Ser), Glycine (Gly), Threonine (Thr), Histidine (His), Cysteine (Cys), Alanine (Ala), Proline

Antibacterial substances belong to a group of compounds that attack dangerous microorganisms. Therefore, killing bacteria or reducing their metabolic activity will lessen their adverse effects on a biological system. They originated from either synthetic materials, microbes, or mold. Many of these medications treat the gram-negative bacteria from the critical precedence group, such as pseudomonas, carbapenem-resistant acinetobacter, and enterobacterales. This study aims to investigate the simultaneous analysis of specific antibacterial spectrophotometrically. The WHO maintains this list of priority infections with antibiotic resistance. Drug combinations in single dosage forms are becoming increasingly popular in the pharmaceutical industry

Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the

Abstract

  The logistic regression model is one of the nonlinear models that aims at obtaining highly efficient capabilities, It also the researcher an idea of the effect of the explanatory variable on the binary response variable.                                                                                  &nb

At present, numerous novel chemical compounds face challenges related to their limited solubility in aqueous environments. These compounds are classified under the Biopharmaceutical Classification System (BCS) as either class II or class IV substances. Different carriers were used to increase their solubility. Candesartan cilexetil (CC) is one of the most widely used antihypertensive drugs, which belongs to class II drugs. The aim of this research was to enhance the solubility and dissolution rate of CC through a complexation approach involving β-cyclodextrin and its derivatives, specifically hydroxypropyl beta cyclodextrin (HP-β-CD), methyl beta cyclodextrin (M-β-CD), and sulfonyl ether beta-cyclodextrin (SBE-β-CD), serving as