Girdhar Khandelwal

: In recent years, the use of natural compounds derived from plants for the treatment of skin cancer has gained significant attention due to their potential therapeutic effects and minimal side effects. This review focuses on the innovative approach of utilizing biocomponents sourced from plants in combination with backpropagation neural networks (BPNN) for the screening and analysis of skin cancer treatments. The integration of plant-derived compounds and AI-driven algorithms holds promise for enhancing the precision and effectiveness of skin cancer therapies. The review begins by highlighting the escalating global burden of skin cancer and the limitations of conventional treatment approaches. With the rise in concerns about the adverse effects of synthetic drugs, researchers have turned their attention towards exploring the therapeutic potential of plant-derived biocomponents. These natural compounds are known for their rich bioactive constituents that exhibit anti-cancer properties, making them suitable candidates for skin cancer treatment. One of the key challenges in harnessing the potential of plant-derived compounds is the need for accurate screening and analysis of their effects. This is where backpropagation neural networks, a type of artificial neural network, comes into play. These networks can process complex data and recognize intricate patterns, enabling them to predict the efficacy of various biocomponents in combating skin cancer. The review delves into the functioning of BPNN and its applications in drug discovery and treatment evaluation. Furthermore, the review explores several case studies that demonstrate the successful integration of plant-derived compounds with BPNN in the context of skin cancer treatment. These studies provide evidence of how this synergistic approach can lead to improved treatment outcomes by minimizing adverse effects and maximizing therapeutic benefits. The methodology section discusses the steps involved in training the neural network using relevant datasets and optimizing its performance for accurate predictions. While the integration of plant-derived compounds and BPNN shows great promise, the review also addresses the existing challenges and limitations. These include the need for comprehensive and standardized datasets, potential biases in training data, and the complexity of neural network architectures. The regulatory considerations surrounding plant-based therapies are also discussed, highlighting the importance of rigorous testing and validation.

Convolvulus prostratus, also known as sankhapushpi, is a very adaptable plant frequently used as a nervine tonic, nootropic, and rejuvenator in cases of insanity and epilepsy and has a broad spectrum of beneficial properties. Several medicinal qualities, including anxiety, neuroprotective, antioxidant, analgesic, immunomodulatory, antibacterial, antidiabetic, and cardioprotective effects, have been related to this herb in ancient literature. Numerous bioactive phytoconstituents, including kaempferol(flavonoid), ceryl alcohol, scopolamine(phenolics), and convolamine (alkaloid), are associated with the therapeutic capabilities of this herb. The Whole herb or its juice is credited with alterative, antiphlogistic, and nervine tonic action. Additionally, the involvement of C. prostratus extracts in neurodegenerative was thoroughly established. Although this significant herb has comprehensive preclinical studies, thorough clinical research and mechanistic mode-of-action studies still need to be conducted. With a detailed analysis of the research gaps, the current review aims to highlight the phytonutrient constituents, beneficial characteristics, and therapeutic properties of C. prostratus. The creation of CNS phytopharmaceuticals, derived from C. prostratus, is hoped to flourish due to the scientific upgrade on the ethnomedicinal characteristics of this herb.