Today technology using nanoparticle when treatment pathogentic microorganism and we focused on this here. It was found that the species of streptococcus used in present study were sensitive to erythromycin. In present study focusing biofilm formation by Streptococcus spp was evaluated. Species S. mutans was found that highest amount of biofilm compare with the other species. The aim of report effect (SNPs) on ability of biofilm form different species of streptococcus. The anti-biofilm effect of SNPs was in concentration dependent manner. The highest effect of SNP against biofilm formation was found the concentration 160 μg/ml, while the lowest effect was found the lowest used concentration (80 μg/ml) of SNPs. In vivo study revealed that s

Schiff bases of Ceftizoxime sodium were synthesized in an attempt to improve the antimicrobial spectrum of Ceftizoxime. Aminothiazole ring of Ceftizoxime is linked directly through an imino group to different aromatic aldehydes reacted by nucleophilic addition using trimethylamine (TEA), as a catalyst and refluxed in methanol. The antimicrobial activity was evaluated for such Schiff bases using disc diffusion method. Molecular docking was conducted on certain penicillin-binding proteins (PBPs) and carboxypeptidases using 1- click docking software. Schiff bases of Ceftizoxime were prepared with reasonable yields and their chemical structures were confirmed by spectral analysis (FTIR, 1H-NMR) and elemental microanalysis (CHNS). The antibacter

This assay rapidly detects chlorpromazine hydrochloride using its ability to reduce gold ions to form nanoparticles. Its low cost, resilience to interferences and short analysis time could facilitate environmental monitoring and biomedical analysis.

This assay rapidly detects chlorpromazine hydrochloride using its ability to reduce gold ions to form nanoparticles. Its low cost, resilience to interferences and short analysis time could facilitate environmental monitoring and biomedical analysis.

Metal enhanced fluorescence (MEF) is an unequaled phenomenon of metal nanoparticle surface plasmons, when light interacts with the metal nanostructures (silver nanoparticles) which result electromagnetic fields to promote the sensitivity of fluorescence. This work endeavor to study the influence of silver nanoparticles on fluorescence intensity of Fluoreseina dye by employment mixture solution with different mixing ratio. Silver nanoparticles had been manufactured by the chemical reduction method so that Ag NP layer coating had been done by hot rotation liquid method. The optical properties of the prepared samples (mixture solution of Fluoreseina dye solutions and colloidal solution with 5 minutes prepared of Ag NPs) tested by using UV-V

Background: Red onion (Allium cepa L.) peels represent an abundant agricultural byproduct rich in bioactive flavonoids, yet their therapeutic potential against aggressive breast cancer models remains poorly characterized. This study aimed to extract and purify anthocyanins from red onion peels, characterize their active constituents, and evaluate their selective in vitro cytotoxicity against the triple-negative breast cancer (TNBC) cell line CAL-51 relative to non-tumorigenic Normal Human Fibroblast (NHF) cells. Methods: Anthocyanins were extracted with acidified aqueous ethanol, purified using silica gel G60 column chromatography and characterized by reversed-phase high-performance liquid chromatography (RP-HPLC). Cytotoxicity was evaluate

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature