Glia, which was formerly considered to exist just to connect neurons, now plays a key function in a wide range of physiological events, including formation of memory, learning, neuroplasticity, synaptic plasticity, energy consumption, and homeostasis of ions. Glial cells regulate the brain's immune responses and confers nutritional and structural aid to neurons, making them an important player in a broad range of neurological disorders. Alzheimer's, ALS, Parkinson's, frontotemporal dementia (FTD), and epilepsy are a few of the neurodegenerative diseases that have been linked to microglia and astroglia cells, in particular. Synapse growth is aided by glial cell activity, and this activity has an effect on neuronal signalling. Each glial malfunction in diverse neurodegenerative diseases is distinct, and we will discuss its significance in the progression of the illness, as well as its potential for future treatment.
We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals' use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver's operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.
Over 80% of genetic studies in the Parkinson's disease (PD) field have been conducted on individuals of European descent. There is a social and scientific imperative to understand the genetic basis of PD across global populations for therapeutic development and deployment. PD etiology is impacted by genetic and environmental factors that are variable by ancestry and region, emphasising the need for worldwide programs to gather large numbers of patients to identify novel candidate genes and risk loci involved in disease. Only a handful of documented genetic assessments have investigated families with PD in AfrAbia, which comprises the member nations of the Arab League and the African Union, with very limited cohort and case-control studies reported. This review article summarises prior research on PD genetics in AfrAbia, highlighting gaps and challenges. We discuss the etiological risk spectrum in the context of historical interactions, highlighting allele frequencies, penetrance, and the clinical manifestations of known genetic variants in the AfrAbian PD patient community.