Background: Breast cancer has become one of the most predominant health risks among women and its mass death rate has continued to escalate world over. New data indicate that breast cancer may be modification by lifestyle factors, especially diet. Lactobacillus and Lactococcus type of probiotics have been identified to cure or promote health by regulation of the immune mechanism. Such bacteria are frequently found in healthy breast tissue and they could possibly help prevent breast cancer. Combining with a well established probiotic, Bifidobacterium longum, Lactobacillus acidophilus has proved to have immunomodulatory and anti-inflammatory effects, therefore, it should be a prospective probiotic strain to maintain breast health. Aim

Background: Calcium hydroxide and calcium-silicate materials used as direct pulp capping materials. The aims of this in vitro study is to compare among these materials in, the calcium ion release and pH change in soaking water after immersion of materials’ specimens in deionized water. Also Solubility and water sorption of materials’ specimens measured after soaking time. Calcium-silicate materials used were Biodentine, TheraCal and MTA Plus. Materials and methods: Four materials used in this study; Urbical lining (as control group), Biodentine, TheraCal and MTA Plus. Ten discs fabricated from each tested material, by using plastic moulds of 9 mm diameter and 1 mm thickness. Each specimen was immersed in 10 ml of d

Pseudomonas aeruginosa is a common and major opportunistic human pathogen, its causes many and dangersinfectious diseases due to death in some timesex: cystic fibrosis , wounds inflammation , burns inflammation , urinary tract infection , other many infections otitis external , Endocarditis , nosocomial infection and also causes other blood infections (Bacteremia). thereforebecomes founding fast and exact identification of P. aeruginosafrom samples culture very important.However, identification of this species may be problematic due to the marked phenotypic variabilitydemonstrated by samples isolates and the presence of other closely related species. To facilitate species identification, we used 16S ribosomal DNA(rRNA) sequence data

A new adsorbent was developed by integrating algae biomass (AG) into a chitosan (CN) matrix, followed by structural enhancement via crosslinking with pyromellitic dianhydride (PMDA) through a hydrothermal synthesis approach. This process resulted in the formation of a robust AG@CN-PMDA composite with improved physicochemical characteristics suitable for advanced adsorption applications. The AG@CN-PMDA composite was evaluated for its efficiency in removal of the cationic dye methyl violet 2B (MV 2B) from aqueous solution. The adsorption process was refined through the Box-Behnken design (RSM-BBD), evaluating three essential parameters: adsorbent dosage (A: 0.02–0.1 g/100 mL), pH (B: 4–10), and time (C: 5–20 min). The ideal conditions f

Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

Beyond the immediate content of speech, the voice can provide rich information about a speaker's demographics, including age and gender. Estimating a speaker's age and gender offers a wide range of applications, spanning from voice forensic analysis to personalized advertising, healthcare monitoring, and human-computer interaction. However, pinpointing precise age remains intricate due to age ambiguity. Specifically, utterances from individuals at adjacent ages are frequently indistinguishable. Addressing this, we propose a novel, end-to-end approach that deploys Mozilla's Common Voice dataset to transform raw audio into high-quality feature representations using Wav2Vec2.0 embeddings. These are then channeled into our self-attentio