Beta-lactamase was purified from local isolate Klebsiella pneumonia by several steps included precipitation with ammonium sulphate at 20-40% saturation, DEAE- ion exchange chromatography and gel filtration on Sephacryl S-200 column. The obtained purification fold and recovery were 32.66; 47.04% respectively. The characterization of the purified beta-lactamase showed that the molecular weight was about 4000 daltons as determined by gel filtration.Purified enzyme had an optimal pH of 7 for activity and an optimal stability between pH 6.5-7.5, results shows that the optimal temperature appear to be 35 ? C .During storage the enzyme retained 72% at -20 ? C and retained 25% of the activity at the same period at 4 ? C.

Olfactory impairment and abnormal frontal EEG oscillations are recognized as early markers of Alzheimer’s disease (AD). Using a publicly available olfactory EEG dataset of 35 subjects spanning normal cognition, amnestic mild cognitive impairment (aMCI), and AD, each with MMSE scores and demographics, stimulus-locked epochs from four electrodes (Fp1, Fz, Cz, Pz) were processed with wavelet-based time–frequency analysis. Band-limited power ratios (delta, theta, alpha, beta) were computed as log-transformed post-odor/baseline values and aggregated to subject-level features. Statistical analyses revealed graded attenuation of odor-evoked frontal (Fp1) band-power ratios across groups, with significant differences in several band–od

Nowadays, there is increased interest in the biosynthesis of microbial melanin related to their numerous biological functions and applications in many fields, especially in medical fields, including immune-modulating, antimicrobial antibiotic, antiviral antivenin, anticancer, antitumor activity, and anti-biofilm activity. Pyomelanin is a hydrophobic macromolecule that is typically dark brown or black in color, formed by the oxidative polymerization of phenolic or indolic compounds. Pyomelanin is reported to be safe for consumption, thus providing a crucial strategy for biocontrol of biofilm. Furthermore, natural pyomelanin is known as a potent antioxidant, photoprotective, and free radical scavenging. Objective: This study focuses on the

Medicinal plants are used to treat various diseases although little is known about their toxicity. Coriandrum sativum is one of the most commonly plants that is used to treat several physiological disorders. Thus, this study was conducted to evaluate the effect of aqueous extract of C. sativum on the structure and function of liver in male albino mice. Thirty male mice were randomly divided into three groups: Group 1 untreated (control), Group 2 and 3 were administrated orally with the aqueous extract of the plant at dose 125 and 250 mg/kg. b. w. For 30 days. The effect of the extract on liver weights, biochemical parameters as well as histological study were assessed. There were no significant difference (P>0.05) observed in relative organ

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class