This study aimed to study the effect of Ziziphus spina christi Aqueous cold and Alcoholic leaves and fruits extracts on the growth and activities of the following types of bacteria :( Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Streptococcus pyogenes ). The results appeared outweigh the alcoholic extract of leaves and fruits of Sidr that prepared by saxholate extractor by addition of ethanol 95% significant superiority as compared with aqueous extract that prepared by using distilled water as was its influence inhibitor to the growth and effectiveness of bacteria , about the treatment of in-vivo to cause injury to these types of bacteria diagnosed laboratory mice and treated with alcoholic extract of the leaves o

Abstract

The Holy Quran is the greatest motivator for the mind to keep pace with life. The doctrine of faith is a requirement of logic and wisdom, and it cannot be reached by the hand of superstition. The Qur’an destroyed the principle of superstition in the verses of the creation of the universe. The principle of inactivity of the Qur’an is a false accusation that collision with the texts of the Qur’an. The secret of scientific development is to harness the laws of nature, benefit from them, and walk according to its requirements, on which the manifestations of civilization and the Qur’an are based

Objective: This study involved the synthesis of new Schiff bases and 1,3-oxazepine derivatives from the baclofen drug and study the anticancer activities. Methods: Baclofen was initially reacted with aromatic aldehydes to create Schiff base derivatives (Ia–Ib), which were then closed in the next step using anhydrous acids to form oxazepine derivatives (IIa–IId). Results: The title compounds were synthesized successfully and identified using FT-IR, 1H NMR, and 13C NMR spectroscopy. Additionally, compound (IIc)’s (3-(4-chloro-phenyl)-4-[2-(4nitro-phenyl)-4,7-dioxo-4,7-dihydro-[1,3] oxazepin-3-yl]butyric acid) anticancer activity was assessed using MTT assay against FTC-133 (thyroid cancer) compared with WRL-68 (normal cell line). Discus

          New nitrone and selenonitrone compounds were synthesized. The condensation method between N-(2-hydroxyethyl) hydroxylamine and substituted carbonyl compounds such as [benzil, 4, 4́-dichlorobenzil and  2,2́ -dinitrobenzil] afforded a variety of new nitrone compounds while the condensation between N-benzylhydroxylamine and substituted selenocarbonyl compounds such as [di(4-fluorobenzoyl) diselenide and (4-chlorobenzoyl selenonitrile] obtained selenonitrone compounds. The condensation of N-4-chlorophenylhydroxylamine with dibenzoyl diselenide obtained another type of selenonitrone compounds. The structures of the synthesized compounds were assigned based on spectroscopic data (FT-IR,

Since the COVID-19 pandemic alarm was made by the severe acute respiratory syndrome (SARS)-coronavirus (CoV) 2, several institutions and agencies have pursued to clarify the viral virulence and infectivity. The fast propagation of this virus leads to an unprecedented rise in the number of cases worldwide. COVID-19 virus is exceptionally contagious that spreads through droplets, respiratory secretions, and direct contact. The enveloped, single-stranded RNA virus has a specific envelop region called (S) region encoding (S protein) that specifically binds to the host cell receptor. Viral infection requires receptors' participation on the host cell membrane's surface, a  key- step for the viral invasion of susceptible cells.

Rec

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 effectiveness inhibitory to extract alcohol for the leaf and flower to plant sage Salvia pratensis each of Staphylococcus aureus, streptococcus epidermidis, Salmonella typhi, Pseudomonas aeroginosa, Escherichia coli, Aspergillus niger and Candida albicans whom had any inhibition to aqueous extracts of the parts itself species bacterial and fungal. The study also demonstrated that the extract of plant containing compounds chemical such as tannins, Alkaloids, Flavonoieds, and saponins, which owns effectiveness of medical. The MIC, MBC and inhibition zones for crud extract were determinated for microbial agents.