Ketoprofen has recently been proven to offer therapeutic potential in preventing cancers such as colorectal and lung tumors, as well as in treating neurological illnesses. The goal of this review is to show the methods that have been used for determining ketoprofen in pharmaceutical formulations. Precision product quality control is crucial to confirm the composition of the drugs in pharmaceutical use. Several analytical techniques, including chromatographic and spectroscopic methods, have been used for determining ketoprofen in different sample forms such as a tablet, capsule, ampoule, gel, and human plasma. The limit of detection of ketoprofen was 0.1 ng/ ml using liquid chromatography with tandem mass spectrometry, while it was 0

Protein arginine methyltransferases (PRMTs) play important roles in transcription, splicing, DNA damage repair, RNA biology, and cellular metabolism. Thus, PRMTs have been attractive targets for various diseases. In this study, we reported the design and synthesis of a potent pan-inhibitor for PRMTs that tethers a thioadenosine and various substituted guanidino groups through a propyl linker. Compound II757 exhibits a half-maximal inhibition concentration (IC50) value of 5 to 555 nM for eight tested PRMTs, with the highest inhibition for PRMT4 (IC50 = 5 nM). The kinetic study demonstrated that II757 competitively binds at the SAM binding site of PRMT1. Notably, II757 is selective for PRMTs over a panel of other methyltransferases, w

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

Background: In the past, an association between Tuberculosis (TB) and Diabetes Mellitus (DM) was widely accepted, today the potential public health and clinical importance of this relationship seems to be largely ignored. The national clinical and policy guidance in the UK on the central of TB, for example, does not consider the relationship with DM.Objectives: To determine the risk of association between diabetes mellitus and pulmonary TB.Methods: A retrospective study conducted in Ibn Zuhr hospital for chest diseases from Jan 2008 – sep 2010 , included in the study 402 patients with TB divided into diabetic & non diabetic, 96 (23.8%) were diabetic while other 306 were TB not diabetic.Results: Risk of TB among DM patients were cle

In this study an experimental work was done to study the possibility of using aluminum rubbish material as a coagulant to remove the colloidal particles from oily wastewater by dissolving this rubbish in sodium hydroxide solution. The experiments were carried out on simulated oily wastewater that was prepared at different oil concentrations and hardness levels (50, 250, 500, and 1000) ppm oil for (2000, 2500, 3000, and 3500) ppm CaCo₃ respectively. The initial turbidity values were (203, 290, 770, and 1306) NTU, while the minimum values of turbidity that have been gained from the experiments in NTU units were (1.67, 1.95, 2.10, and 4.01) at best sodium aluminate dosages in milliliters (12, 20, 24, and 28) for

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci