Excess alcohol consumption is associated with numerous metabolic changes and homeostasis disturbances of many macro- and micro-elements in the body. It has been associated with multiple pathologies at all levels. In the digestive apparatus, alcohol has generally been related to its toxic effects upon the liver. Alpha-1 antitrypsin (α-1 AT or AAT) plays an important role in controlling inflammation, coagulation and repair mechanisms in the body and most α-1 AT in the body is produced by the liver; from the other hand, Intrinsic factor (IF), which is a glycoprotein produced by the parietal cells of the stomach is necessary for the absorption of vitamin B12 in the small intestine. This study was designed to assess serum levels of α-1 AT and

Prednisolone (SAID) was conjugated with ibuprofen (NSAID) through an amino acid (glycine) as a spacer arm to synthesize the following compound:

Prednisolone – glycine – ibuprofen.

    The method employed consists of converting the carboxylic acid function of (R,S) – ibuprofen – glycine to the highly reactive acid chloride and subsequent reaction with the C₂₁ hydroxyl group of prednisolone. This reactive intermediate was found to react as well with the C₁₇ tertiary hydroxyl group of the steroid to form three compounds and eight diastereomers. These results were confirmed by T.L.C, and the desired compound was separated by column chromatograph

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of variou

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th