Ahmed M. Naglah

Bio: Ahmed M. Naglah is an academic researcher from King Saud University. The author has contributed to research in topics: Chemistry & Schiff base. The author has an hindex of 12, co-authored 113 publications receiving 571 citations. Previous affiliations of Ahmed M. Naglah include University of Louisville & Nile University.

Antibacterial Evaluation, In Silico Characters and Molecular Docking of Schiff Bases Derived from 5-aminopyrazoles.

Abstract: A series of Schiff bases 14-25 were designed and synthesized for evaluation of their antibacterial properties against multi-drug resistant bacteria (MDRB). The antibacterial activities of Schiff bases 14-25 showed that most of the synthesized compounds displayed a significant antibacterial activity. Assessment of in silico ADMET properties (absorption, distribution, metabolism, excretion and toxicity) of Schiff bases illustrates that all derivatives showed agreement to the Lipinski's rule of five. Further enzymatic assay aided by molecular docking study demonstrated that compound 18 is a potent inhibitor of staphylococcus aureus DNA gyrase and dihydrofolate reductase kinases. This study could be valuable in the discovery of new potent antimicrobial agents.

Discovery of New Schiff Bases Tethered Pyrazole Moiety: Design, Synthesis, Biological Evaluation, and Molecular Docking Study as Dual Targeting DHFR/DNA Gyrase Inhibitors with Immunomodulatory Activity.

Abstract: A series of Bis-pyrazole Schiff bases (6a-d and 7a-d) and mono-pyrazole Schiff bases (8a-d and 9a-d) were designed and synthesized through the reaction of 5-aminopyrazoles 1a-d with aldehydes 2-5 using mild reaction condition with a good yield percentage. The chemical structure of newly formed Schiff bases tethered pyrazole core was confirmed based on spectral and experimental data. All the newly formed pyrazole Schiff bases were evaluated against eight pathogens (Gram-positive, Gram-negative, and fungi). The result exhibited that, most of them have good and broad activities. Among those, only six Schiff bases (6b, 7b, 7c, 8a, 8d, and 9b) displayed MIC values (0.97-62.5 µg/mL) compared to Tetracycline (15.62-62.5 µg/mL) and Amphotericin B (15.62-31.25 µg/mL), MBC values (1.94-87.5 µg/mL) and selectivity to tumor cell than normal cells. Immunomodulatory activities showed that the promising Schiff bases increase the immunomodulator effect of defense cell and the Schiff base 8a is the highest one by (Intra. killing activity = 136.5 ± 0.3%) having a pyrazole moiety as well as amide function (O=C-NH2) and piperidinyl core. Furthermore, the most potent one exhibited broad activity depending on both MIC and MBC values. Moreover, to study the mechanism of these pyrazole Schiff bases, two active Schiff bases 8a and 9b from six derivatives were introduced to study the enzyme assay as dihydrofolate reductase (DHFR) on E. coli organism and DNA gyrase with two different organisms, S. aureus and B. subtilis, to determine the inhibitory activities with lower values in the case of DNA gyrase (8a and 9b) or nearly as DHFR compound 9b, while pyrazole 8a showed excellent inhibitory against all enzyme assay. The molecular docking study against dihydrofolate reductase and DNA gyrase were performed to study the binding between active site in the pocket with the two Schiff bases (8a and 9b) that exhibited good binding affinity with different bond types as H-bonding, aren-aren, and arene-cation interaction as well as study the physicochemical and pharmacokinetic properties of the two active Schiff bases 8a and 9b.

Deep Learning Based Method for Computer Aided Diagnosis of Diabetic Retinopathy

Abstract: Diabetic retinopathy (DR) is a retinal disease caused by the high blood sugar levels that may damage and block the blood vessels feeding the retina. In the early stages of DR, the disease is asymptomatic; however, as the disease advances, a possible sudden loss of vision and blindness may occur. Therefore, an early diagnosis and staging of the disease is required to possibly slow down the progression of the disease and improve control of the symptoms. In response to the previous challenge, we introduce a computer aided diagnosis tool based on convolutional neural networks (CNN) to classify fundus images into one of the five stages of DR. The proposed CNN consists of a preprocessing stage, five stage convolutional, rectified linear and pooling layers followed by three fully connected layers. Transfer learning was adopted to minimize overfitting by training the model on a larger dataset of 3.2 million images (i.e. ImageNet) prior to the use of the model on the APTOS 2019 Kaggle DR dataset. The proposed approach has achieved a testing accuracy of 77% and a quadratic weighted kappa score of 78%, offering a promising solution for a successful early diagnose and staging of DR in an automated fashion.

Harrison's Principles of Internal Medicine

Abstract: The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many