A new Azo‐Schiff base ligand L was prepared by reaction of m‐hydroxy benzoic acid with (Schiff base B) of 3‐[2‐(1H–indol‐3‐yl)‐ethylimino]‐1.5‐dimethyl‐2‐phenyl‐2,3‐dihydro‐1H‐pyrazol‐4‐ylamine. This synthesized ligand was used for complexation with different metal ions like Ni(II), Co(II), Pd(II) and Pt(IV) by using a molar ratio of ligand: metal as 1:1. Resulted compounds were characterized by NMR (¹H and ¹³C), UV–vis spectroscopy, TGA, FT‐IR, MS, elemental analysis, magnetic moment and molar conductivity studies. The activation thermodynamic parameters, such as ΔE*, ΔH^*, ΔS^*, ΔG^*and

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

     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%.

The snthesis and characterization of cobalt(II), nickel(II), copper(II) and zinc(II) complexes of azo ligand 4-[(5-acetyl-2-aminophenyl)- diazenyl]-1,5-dimethyl-2-phenyl-1H-pyrazol-3(2H)-one derived from 4-aminoantipyrine and 4-aminoacetophenone are reported. The nature of the compounds have been studied followed by mole ratio and methods of continuous contrast, Beer′s law followed during a condensation rate (1 × 10-4 – 3 × 10-4 M). The analytical data showed that all the complexes are in 1:2 metal-ligand ratio. An octahedral geometry have been suggested for all the compounds and biological studies of all the complexes were evaluated against different types of antimicrobial strains.

Many pathophysiological processes can affect the pharmacokinetic properties of drugs in people with diabetes. The present study was deigned to evaluate the influence of diabetes mellitus (DM) on the pharmacokinetic parameters of metronidazole administered as single oral dose. Twelve healthy volunteers and twelve diabetic patients were enrolled in the present study. On day 1, a single oral dose of metronidazole 500 mg was administered orally to all participants at 9:00 am after a 10-hour fasting. Over the following 48 hours, blood samples were taken at frequent intervals and serum metronidazole concentrations were measured by a high-performance liquid chromatography method for assessment of pharmacokinetics of metronidazole. The data

Many pathophysiological processes can affect the pharmacokinetic properties of drugs in people with diabetes. The present study was deigned to evaluate the influence of diabetes mellitus (DM) on the pharmacokinetic parameters of metronidazole administered as single oral dose. Twelve healthy volunteers and twelve diabetic patients were enrolled in the present study. On day 1, a single oral dose of metronidazole 500 mg was administered orally to all participants at 9:00 am after a 10-hour fasting. Over the following 48 hours, blood samples were taken at frequent intervals and serum metronidazole concentrations were measured by a high-performance liquid chromatography method for assessment of pharmacokinetics of metronid

Background: Diabetes mellitus type 2 has been known for many years as the most common endocrine metabolic disorder that affect the oral cavity and cause many oral diseases including candidiasis. In this study, the incidence of Candida spp. in the saliva of controlled and uncontrolled diabetic patients were determined and compared with non diabetic group. Material and method: The sample consists of 200 subjects: 100 diabetic patients [57 (28.5%) uncontrolled diabetes, 43 (21.5%) controlled diabetes] and 100 (50%) non diabetic groups. Saliva samples was obtained from the subjects and cultured on selective media using appropriate microbiological method to observe the presence of Candida spp. Results: The results revealed a significant associat

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