Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.

The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for the analysis of large datasets that include genetic, imaging, and clinical data. Moreover, multi-modal data integration, which includes information from brain imaging (MRI, PET scans), genetic profiles, and clinical evaluations, is made easier by computational intelligence. A thorough knowledge of the course of the illness is made possible by this consolidative method, which also facilitates the creation of predictive models for early medical evaluation and outcome prediction. Furthermore, there has been a great deal of promise shown by the use of artificial intelligence to neuroimaging analysis. Sophisticated image processing methods combined with machine learning algorithms make it possible to identify functional and structural anomalies in the brain, which often act as early indicators of neurodegenerative diseases. This chapter examines how computational intelligence plays a critical role in improving the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's, etc. To sum up, computational intelligence provides a revolutionary approach for improving the identification of neurodegenerative illnesses. In the battle against these difficult disorders, embracing and improving these computational techniques will surely pave the path for more individualized therapy and more therapies that are successful.

A rational approach for the design and synthesis of 1-acetyl-3,5-diaryl-4,5-dihydro(1H)pyrazoles as a new class of potential non-purine xanthine oxidase inhibitors.

Xanthine oxidase is a complex molybdoflavoprotein that catalyses the hydroxylation of xanthine to uric acid. Fifty three analogues of 1-acetyl-3,5-diaryl-4,5-dihydro(1H)pyrazoles were rationally designed and synthesized and evaluated for in vitro xanthine oxidase inhibitory activity for the first time. Some notions about structure activity relationships are presented. Six compounds 41, 42, 44, 46, 55 and 59 were found to be most active against XO with IC(50) ranging from 5.3 µM to 15.2 µM. The compound 59 emerged as the most potent XO inhibitor (IC(50)=5.3 µM). Some of the important interactions of 59 with the amino acid residues of active site of XO have been figured out by molecular modeling.