Objective: The present work was undertaken to investigate the impact of sub inhibitory concentration of gentamicin on hla gene expression in methicillin resistant Staphylococcus aureus isolates. Methods: The bacterial isolates used in this study represent 33 MRSA strains, previously isolated form patients visiting several hospitals in Baghdad. Gentamicin, vancomycin, and oxacillin MIC were determined using broth dilution method. Microtiter plate method was adopted to investigate the biofilm forming capacity. Alpha hemolysin was detected by culturing MRSA isolates on rabbit blood agar. Furthermore, hla gene was detected in MRSA isolates using conventional PCR technique; while, qRT-PCR method was performed to assay the hla expression in plank

Congenital adrenal hyperplasia is a group of autosomal recessive disorders. The most frequent one is 21-hydroxylase deficiency. Analyzing CYP21A2 gene mutations was so far not reported in Iraq. This work aims to analyze the spectrum and frequency of CYP21A2 mutations among Iraqi CAH patients. Sixty-two children were recruited from the Pediatric Endocrine Consultation Clinic, Children Welfare Teaching Hospital, Baghdad, Iraq, from September 2014 till June 2015. Their ages ranged between one day and 15 years. They presented with salt wasting, simple virilization, or pseudoprecocious puberty. Cytogenetic study was performed for cases with ambiguous genitalia. Molecular analysis of

     In this research, silver nanoparticles (AgNPs) were manufactured using aqueous extract of mushroom Pleurotus ostreatus. Anticancer potential of AgNPs was investigated versus human breast cancer cell line (MCF-7). Cytotoxic response was assessed by MTT assay. AgNPs showed inhibition effect at the following concentrations 12.5, 25, 50, 100 and 200 µg/ml versus MCF-7 cell line, and all treatments had a positive result. The MCF-7 cells were inhibited up to 85.14 % at the concentration 200 μg/ml of AgNPs which reduced cells viability to 14.86%, while 12.5 μg/ml of AgNPs caused 24.23% cells inhibition with reduction of cells viability to 75.77%.

Background: The transcriptional control of various cell types, especially in the development or functioning of immune system cells involved in either promoting or inhibiting the immune response against cancer, is significantly influenced by DNA or RNA methylation. Multifaceted interconnections exist between immunological or cancer cell populations in the tumor's microenvironment (TME). TME alters the fluctuating DNA (as well as RNA) methylation sequences in these immunological cells to change their development into pro- or anti-cancer cell categories (such as T cells, which are regulatory, for instance). Objective: This review highlights the impact of DNA and RNA methylation on myeloid and lymphoid cells, unraveling their intricate

Amygdalin (d-Mandelonitrile 6-O-β-d-glucosido-β-d-glucoside) and its semi synthetic product is Laetrile ( also called vitamin B17): a natural cyanogenic glycoside occurring in the seeds of some edible plants, such as bitter almonds and peaches. Early in the 19th century, Amygdalin was first isolated in 1830 by two French chemists, Robiquet and Boutron-Charlard, as active components in various fruit pits and raw nuts. However, the systematized study of vitamin B17 started when chemist Bohn (1802) discovered that a hydrocyanic acid is released during distillation of the water from bitter almonds. The various pharmacological effects of Laetrile include antiatherogenic, activity in renal fibrosis, pulmonary fibrosis, immune regulation, ant

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

The Arabic Grammar between Originality and Sufficiency

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