General Background: Breast cancer is the most prevalent cancer affecting women, with increasing incidence worldwide. Specific Background: Recent research has focused on the role of epigenetic changes in DNA damage, repair mechanisms, and the potential therapeutic effects of probiotics. Probiotics have shown promise in promoting tissue regeneration and DNA repair. Knowledge Gap: However, the precise impact of probiotics on DNA repair in cancer cells, specifically breast cancer cells, remains underexplored. Aims: This study aimed to evaluate the effects of probiotics on DNA damage repair in AMJ13 Iraqi breast cancer cells and assess the cytotoxic effects of probiotics on these cells. Results: Using the comet assay, we found significant increases in DNA damage repair in AMJ13 cells treated with Lactobacillus plantarum (T1) and a combination of eight probiotic strains (T2). Exposure to T1 for 48 hours resulted in significant increases in tail DNA (P≤0.001), head DNA (P≤0.001), and tail moment (P<0.001), while T2 showed similar significant increases at 72 hours (P<0.05). Image analysis further supported the DNA repair potential of probiotics, as indicated by a small tail curve for treated cells. Novelty: This study provides novel insights into the therapeutic potential of probiotics in breast cancer treatment by demonstrating their capacity to enhance DNA repair mechanisms in cancer cells. Implications: The findings suggest that probiotic therapy may be a promising adjunct treatment in breast cancer, offering a new avenue for cancer management through the enhancement of DNA repair and reduction of DNA damage. Highlights: Probiotics significantly repaired DNA damage in breast cancer cells. T1 and T2 enhanced DNA repair within 48-72 hours. Probiotics offer potential as breast cancer adjunct therapy. Keywords: Breast cancer, probiotics, DNA repair, AMJ13 cells, cytotoxicity
Study of the development of an activated carbon nanotube catalyst for alkaline fuel cell technology. Through the prepared carbon nanotubes catalyst by an electrochemical deposition technique. Different analytical approaches such as X-ray diffraction (XRD) to determine the structural properties and Scanning Electron Microscope (SEM), were used to characterize, Mesh stainless steel catalyst substrate had an envelope structure and a large surface area. Voltages were also obtained at 1.83 V and current at 3.2 A of alkaline fuel cell. In addition, study the characterization of the electrochemical parameters.
We studied at the morphology, structural setup, and optical characteristics of thin cadmium (CdSe) films a thickness of 250 nm that were created by thermal evaporation over glass, The films exhibited a hexagonal shape were crystalline, and tended to form grains in the (111) crystallographic direction, according to the X-ray diffraction examinations. These characteristics were established using the investigation's findings. Through the use of thin films of CdSe doped with Ag at a concentration of 1.5%, the crystal structure orientations for pure CdSe (25.32, 41.84) and CdSe:Ag (25.39, 41.01) that were both pure as well as those that were doped with silver were both determined. The band gap of the optical spectrum decreased by 1.93–
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The environmental conditions are important factors, because they affect both the efficiency of a photovoltaic module and the energy load. This research was carried out experimentally and modeling was done in MATLAB –Simulink by monitoring the variation in power output of the system with environmental conditions such as solar radiation, ambient temperature, wind speed, and humidity of Baghdad city. From the results, the ambient temperatures are inversely proportional to humidity and the output power performance of the system, while the wind speed is directly proportional with the output power performance of the system.
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... Show MoreIn this paper had been studied the characterization of the nanocatalyst (NiO) Mesh electrodes. For fuel cell. The catalyst is prepared and also the electrodes The structural were studied through the analysis of X-ray diffraction of the prepared nanocatalyst for determining the yielding phase and atomic force microscope to identify the roughness of prepared catalyst surface, Use has been nanocatalyst led to optimization of cell voltage, current densities & power for a fuel cell.
This study presents a mathematical model describing the interaction of gut bacteria in the participation of probiotics and antibiotics, assuming that some good bacteria become harmful through mutations due to antibiotic exposure. The qualitative analysis exposes twelve equilibrium points, such as a good-bacteria equilibrium, a bad-bacteria equilibrium, and a coexisting endemic equilibrium in which both bacteria exist while being exposed to antibiotics. The theory of the Sotomayor theorem is applied to study the local bifurcation around all possible equilibrium points. It’s noticed that the transcritical and saddle-node bifurcation could occur near some of the system’s equilibrium points, while pitchfork bifurcation cannot be accrued at
... Show MoreBackground: Dental caries is a localized, progressive destructive, largely irreversible microbial based disease of multifactorial nature; these factors include (host, microbes and food) they influence differently on the initiation and progression of dental caries. The aims of the study: was to evaluate the effect of smoking on salivary flow rate, secretory immunoglobulin (SIgA) level and viable count of mutans streptococci (M.S) bacteria in oral cavity and their relation to dental caries experience. Material and method: The samples were collected from 80 male students ranging in ages from 18-22 years old. Where they divided in to two groups, 40 non-smokers (control group) and 40 smokers (study group). Unstimulated salivary samples were c
... Show MoreOne of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p
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