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.

Recent Advances in Drug Delivery Systems Targeting Insulin Signalling for the Treatment of Alzheimer's Disease.

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the accumulation of neurofibrillary tangles and amyloid-ß plaques. Recent research has unveiled the pivotal role of insulin signaling dysfunction in the pathogenesis of AD. Insulin, once thought to be unrelated to brain function, has emerged as a crucial factor in neuronal survival, synaptic plasticity, and cognitive processes. Insulin and the downstream insulin signaling molecules are found mainly in the hippocampus and cortex. Some molecules responsible for dysfunction in insulin signaling are GSK-3ß, Akt, PI3K, and IRS. Irregularities in insulin signaling or insulin resistance may arise from changes in the phosphorylation levels of key molecules, which can be influenced by both stimulation and inactivity. This, in turn, is believed to be a crucial factor contributing to the development of AD, which is characterized by oxidative stress, neuroinflammation, and other pathological hallmarks. Furthermore, this route is known to be indirectly influenced by Nrf2, NF-κB, and the caspases. This mini-review delves into the intricate relationship between insulin signaling and AD, exploring how disruptions in this pathway contribute to disease progression. Moreover, we examine recent advances in drug delivery systems designed to target insulin signaling for AD treatment. From oral insulin delivery to innovative nanoparticle approaches and intranasal administration, these strategies hold promise in mitigating the impact of insulin resistance on AD. This review consolidates current knowledge to shed light on the potential of these interventions as targeted therapeutic options for AD.