4-Amino-N-(5-methyl-isaxazol-3-yl)-benzenesulfonamide was synthesized by reacting the diazonium salt of sulfamethoxazole with 3-amino phenol. Complexes of Ni(II), Pd(II), Au(III), and Pt(IV) were produced and characterized by atomic absorption, elemental microanalysis, infrared spectroscopy, liquid chromatography–mass spectroscopy, thermogravimetric analysis, different scanning calorimetry, and ultraviolet–visible spectroscopy. Furthermore, the conductivity and magnetic properties were quantified. According to the analytical studies, all complexes were found to possess a 1:2 metal–ligand ratio and were non-electrolytes, except the Au(III) complex electrolyte, which had a 1:1 metal:ligand ratio. The biological activities of some of the

In this work, of New Ligand [(E)-5-hydroxy-4-(3-(4-methoxy phenyl) acryl amido) naphthalene -1- sulfonic acid] (ANS) was prepared by reflexing reaction of 4-amino-5-hydroxy naphthalene sulfonic acid with para methoxy cinnamic acid, this produced and described chemical was employed as ligand to prepare tri and di-organotin complexes by condensation reaction with the salts of organotin chloride (phenyl, butyl, and methyl tin chloride). Specialized methods, including elemental analysis, (tin and proton) magnetic resonance, and infrared spectra, were used to identify the complexes. DPPH (2,2-diphenyl-1-picrylhydrazyl) and CUPRAC (Cupric Reducing Antioxidant Capacity) are both commonly used methods for measuring antioxidant capacity in v

Background: The isatin molecule is present in many natural substances, including plants and animals, and is used to prepare compounds with various biological activities. Objectives: To synthesize a new series of isatin derivatives with the expectation that they will have antimicrobial activity. Methods: Thiazole Schiff bases were synthesized from various Mannich bases of isatin to evaluate their antimicrobial properties. Initially, Mannich bases (2a–e) were synthesized by reacting isatin with formaldehyde and different secondary amines. Subsequently, they were treated with 2-aminothiazole to yield the final compounds (3a–e). Spectroscopic characterization was done via FT-IR and 1H-NMR. The antimicrobial screening was conducted o

         Aceclofenac (AC) is an orally active phenyl acetic acid derivative, non-steroidal anti-inflammatory drug with exceptional anti-inflammatory, analgesic and antipyretic properties. It has low aqueous solubility, leading to slow dissolution, low permeability and inadequate bioavailability. The aim of the current study was to prepare and characterize AC-NS-based gel to enhance the dissolution rate and then percutaneous permeability. NS.s were prepared using solvent/antisovent precipitation method at different drug to polymer ratios (1:1, 1:2, and 1:3) using different polymers such as poly vinyl pyrrolidone (PVP-K25), hydroxy propyl methyl cellulose (HPMC-E5) and poloxamer® (388) as stabilizer

Nanocomposites of polymer material based on CdS as filler
material and poly methyl methacrylate (PMMA) as host matrix have
been fabricated by chemical spray pyrolysis method on glass
substrate. CdS particles synthesized by co-precipitation route using
cadimium chloride and thioacetamide as starting materials and
ammonium hydroxide as precipitating agent. The structure is
examined by X-ray diffraction (XRD), the resultant film has
amorphous structure. The optical energy gap is found to be (4.5,
4.06) eV before and after CdS addition, respectively. Electrical
activation energy for CdS/PMMA has two regions with values of
0.079 and 0.433 eV.

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed