MyMedR

Displaying all 4 publications

Abstract:

Sort:

Fulltext Privacy-Aware Collaborative Learning for Skin Cancer Prediction

Ain QU, Khan MA, Yaqoob MM, Khattak UF, Sajid Z, Khan MI, et al.

Diagnostics (Basel), 2023 Jul 04;13(13).
PMID: 37443658 DOI: 10.3390/diagnostics13132264

Cancer, including the highly dangerous melanoma, is marked by uncontrolled cell growth and the possibility of spreading to other parts of the body. However, the conventional approach to machine learning relies on centralized training data, posing challenges for data privacy in healthcare systems driven by artificial intelligence. The collection of data from diverse sensors leads to increased computing costs, while privacy restrictions make it challenging to employ traditional machine learning methods. Researchers are currently confronted with the formidable task of developing a skin cancer prediction technique that takes privacy concerns into account while simultaneously improving accuracy. In this work, we aimed to propose a decentralized privacy-aware learning mechanism to accurately predict melanoma skin cancer. In this research we analyzed federated learning from the skin cancer database. The results from the study showed that 92% accuracy was achieved by the proposed method, which was higher than baseline algorithms.
Phytochemical, antioxidant, antipyretic and anti-inflammatory activities of aqueous-methanolic leaf extract of Mangifera indica

Ain QU, Iqbal MO, Khan IA, Bano N, Naeem M, Jamaludin MI, et al.

Am J Transl Res, 2023;15(7):4533-4543.
PMID: 37560231

OBJECTIVE: Plant-based natural antioxidants have a wide variety of biological activities with significant therapeutic value. Mangifera indica has been used traditionally to treat a variety of ailments in animals and human, but little is defined about its biological or pharmacological effects. Therefore, the objective of the present study was to evaluate phytochemical, antioxidant, antipyretic and anti-inflammatory activities of aqueous-methanolic leaf extract of M. indica.
METHODS: To investigate the possible impact of aqueous-methanolic leaf extract of M. indica on oxidative stress, inflammation, and pyrexia, we used a combined in vitro and in vivo series of experiments on laboratory animals.
RESULTS: Results revealed significant antioxidant potential in 2,2-diphenylpicrylhydrazyl (DPPH) and nitric oxide (NO) scavenging assay, while significant but dose dependent antipyretic potential was documented in typhoid-paratyphoid A and B (TAB) vaccine and prostaglandin E (PGE) induced pyrexia models. Significant anti-inflammatory effects were observed in both acute and chronic inflammatory models of arachidonic acid and formalin. Phytochemical screening and high-performance liquid chromatography (HPLC) analysis of M. Indica confirmed the presence of mangiferin, quercetin, and isoquercetin. These phytoconstituents likely play a role in the observed biological activities. Our results show that M. indica has antioxidant, anti-inflammatory, and antipyretic effects, lending credence to its traditional use and advocating for its utilization as a viable contender in treating oxidative stress-associated ailments.
CONCLUSION: It is concluded that Magnifera indica has various properties in the treatment of various diseases.
Comparative molecular docking analysis for analyzing the inhibitory effect of Anakinra and Ustekinumab against IL17F

Nisar H, Attique SA, Javaid A, Ain QU, Butt F, Zaid M, et al.

J Biomol Struct Dyn, 2023;41(22):13302-13313.
PMID: 36715128 DOI: 10.1080/07391102.2023.2173299

Interleukin 17 F is a member of IL-17 cytokine family with a 50% structural homology to IL-17A and plays a significant role either alone or in combination with IL-17A towards inflammation in Rheumatoid arthritis (RA). A growing number of drugs targeting IL-17 pathway are being tested against population specific disease markers. The major objective of this research was to investigate the anti-inflammatory effect of Anakinra (an IL-1 R1 inhibitor) and Ustekinumab (an IL-12 and IL-23 inhibitor) by targeting IL17F. The three dimensional structures of IL17F was taken from PDB while structures of drugs were taken from PubChem database. Docking was performed using MOE and Schrodinger ligand docking software and binding energies, including s-score using London-dG fitness function and glide score using glide internal energy function, between drug and targets were compared. Furthermore, Protein-Drug complex were subjected to 150 ns Molecular Dynamics (MD) Simulations using Schrodinger's Desmond Module. Docking and MD simulation results suggest anakinra as a more potent IL17F inhibitor and forming a more structurally stable complex.Communicated by Ramaswamy H. Sarma.
Fulltext Molecular docking of alpha-enolase to elucidate the promising candidates against Streptococcus pneumoniae infection

Hassan M, Baig AA, Attique SA, Abbas S, Khan F, Zahid S, et al.

Daru, 2021 Jun;29(1):73-84.
PMID: 33537864 DOI: 10.1007/s40199-020-00384-3

PURPOSE: To predict potential inhibitors of alpha-enolase to reduce plasminogen binding of Streptococcus pneumoniae (S. pneumoniae) that may lead as an orally active drug. S. pneumoniae remains dominant in causing invasive diseases. Fibrinolytic pathway is a critical factor of S. pneumoniae to invade and progression of disease in the host body. Besides the low mass on the cell surface, alpha-enolase possesses significant plasminogen binding among all exposed proteins.
METHODS: In-silico based drug designing approach was implemented for evaluating potential inhibitors against alpha-enolase based on their binding affinities, energy score and pharmacokinetics. Lipinski's rule of five (LRo5) and Egan's (Brain Or IntestinaL EstimateD) BOILED-Egg methods were executed to predict the best ligand for biological systems.
RESULTS: Molecular docking analysis revealed, Sodium (1,5-dihydroxy-2-oxopyrrolidin-3-yl)-hydroxy-dioxidophosphanium (SF-2312) as a promising inhibitor that fabricates finest attractive charges and conventional hydrogen bonds with S. pneumoniae alpha-enolase. Moreover, the pharmacokinetics of SF-2312 predict it as a therapeutic inhibitor for clinical trials. Like SF-2312, phosphono-acetohydroxamate (PhAH) also constructed adequate interactions at the active site of alpha-enolase, but it predicted less favourable than SF-2312 based on binding affinity.
CONCLUSION: Briefly, SF-2312 and PhAH ligands could inhibit the role of alpha-enolase to restrain plasminogen binding, invasion and progression of S. pneumoniae. As per our investigation and analysis, SF-2312 is the most potent naturally existing inhibitor of S. pneumoniae alpha-enolase in current time.