Emerging green synthetic routes for thiazole and its derivatives: Current perspectives.

This review article provides an overview of the green synthesis of thiazole derivatives, emphasizing sustainable and environmentally friendly methodologies. Thiazole derivatives possess significant value and find diverse applications across various fields. However, conventional synthesis methods often involve hazardous reagents and generate substantial waste, posing environmental concerns. The green synthesis of thiazole derivatives employs renewable starting materials, nontoxic catalysts, and mild reaction conditions to minimize environmental impact. Innovative techniques such as microwave irradiation, ultrasound synthesis, green solvents, a green catalyst-based approach, and mechanochemistry-mediated synthesis are employed, offering advantages in terms of scalability, cost-effectiveness, and purification simplicity. The resulting thiazole derivatives exhibit comparable or enhanced biological activities, showcasing the feasibility and practicality of green synthesis in drug discovery. This review paper underscores the importance of sustainable approaches in functional molecular synthesis and encourages further research in this domain.

A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis.

Alzheimer's Disease (AD) is a serious neurological illness that causes memory loss gradually by destroying brain cells. This deadly brain illness primarily strikes the elderly, impairing their cognitive and bodily abilities until brain shrinkage occurs. Modern techniques are required for an accurate diagnosis of AD. Machine learning has gained attraction in the medical field as a means of determining a person's risk of developing AD in its early stages. One of the most advanced soft computing neural network-based Deep Learning (DL) methodologies has garnered significant interest among researchers in automating early-stage AD diagnosis. Hence, a comprehensive review is necessary to gain insights into DL techniques for the advancement of more effective methods for diagnosing AD. This review explores multiple biomarkers associated with Alzheimer's Disease (AD) and various DL methodologies, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), The k-nearest-neighbor (k-NN), Deep Boltzmann Machines (DBM), and Deep Belief Networks (DBN), which have been employed for automating the early diagnosis of AD. Moreover, the unique contributions of this review include the classification of ATN biomarkers for Alzheimer's Disease (AD), systemic description of diverse DL algorithms for early AD assessment, along with a discussion of widely utilized online datasets such as ADNI, OASIS, etc. Additionally, this review provides perspectives on future trends derived from critical evaluation of each variant of DL techniques across different modalities, dataset sources, AUC values, and accuracies.

A Review on the Development of Novel Heterocycles as α-Glucosidase Inhibitors for the Treatment of Type-2 Diabetes Mellitus.

One of the most effective therapeutic decencies in the treatment of Type 2 Diabetes Mellitus is the inhibition of α-glucosidase enzyme, which is present at the brush border of the intestine and plays an important role in carbohydrate digestion to form mono-, di-, and polysaccharides. Acarbose, Voglibose, Miglitol, and Erniglitate have been well-known α-glucosidase inhibitors in science since 1990. However, the long synthetic route and side effects of these inhibitors forced the researchers to move their focus to innovate simple and small heterocyclic scaffolds that work as excellent α-glucosidase inhibitors. Moreover, they are also effective against the postprandial hyperglycemic condition in Type 2 Diabetes Mellitus. In this aspect, this review summarizes recent progress in the discovery and development of heterocyclic molecules that have been appraised to show outstanding inhibition of α-glucosidase to yield positive effects against diabetes.