The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence.

Cervical cancer (CC) continues to be a major worldwide health concern, profoundly impacting the lives of countless females worldwide. In low- and middle-income countries (LMICs), where CC prevalence is high, innovative, and cost-effective approaches for prevention, diagnosis, and treatment are vital. These approaches must ensure high response rates with minimal side effects to improve outcomes. The study aims to compile the latest developments in the field of CC, providing insights into the promising future of CC management along with the research gaps and challenges. Integrating biotechnology and artificial intelligence (AI) holds immense potential to revolutionize CC care, from MobileODT screening to precision medicine and innovative therapies. AI enhances healthcare accuracy and improves patient outcomes, especially in CC screening, where its use has increased over the years, showing promising results. Also, combining newly developed strategies with conventional treatment options presents an optimal approach to address the limitations associated with conventional methods. However, further clinical studies are essential for practically implementing these advancements in society. By leveraging these cutting-edge technologies and approaches, there is a substantial opportunity to reduce the global burden of this preventable malignancy, ultimately improving the lives of women in LMICs and beyond.

Defining new biomarkers for overcoming therapeutical resistance in cervical cancer using lncRNA.

Despite improvements in cervical cancer diagnosis and treatment, the prognosis for cervical cancer patients remains dismal due to the development of drug resistance, metastasis, and invasion resulting leading to treatment failure. Long non-coding RNAs (lncRNAs), a class of RNA transcripts have been reported in mediating carcinogenesis as well as drug, and radio-resistance in tumor cells. These lncRNAs regulate various cancer hallmarks and contribute to the development of therapeutic resistance. They regulates multiple signalling pathways, recruits polycomb group, function as miRNA sponge and scaffolds. Additionally, lncRNAs can act as oncogenes or tumor suppressors in cervical cancer. This comprehensive review outlines the biogenesis of lncRNA and its role in cancer development. It delves into the mechanisms through which various lncRNAs mediate chemoresistance and radioresistance in cervical cancer. By shedding into the light of mechanism, this review will also aids researchers in understanding lncRNAs as biomarkers and latest advancements in clinically targeting them with the help of Artificial Intelligence for overcoming chemoresistance and radioresistance, thereby improving cervical cancer treatment.

A Review on Deep Learning-driven Drug Discovery: Strategies, Tools and Applications.

It takes an average of 10-15 years to uncover and develop a new drug, and the process is incredibly time-consuming, expensive, difficult, and ineffective. In recent years the dramatic changes in the field of artificial intelligence (AI) have helped to overcome the challenges in the drug discovery pipeline. Artificial intelligence (AI) has taken root in various pharmaceutical sectors, from lead compound identification to clinical trials. Deep learning (DL) is a component of artificial intelligence (AI) that has excelled in many fields of Artificial intelligence (AI) research over the past decades. Its numerous applications in the realms of science and technology, especially in biomedicine and bioinformatics, are witnessed deep learning (DL) applications significantly accelerate drug discovery and pharmaceutical research in recent years, and their usefulness has exceeded expectations and shown good promise in tackling a range of issues with drug discovery. Deep learning (DL) holds great potential for drug development since it allows for sophisticated image interpretation, molecular structure and function prediction, and the automated creation of novel chemical entities with specific features. In the process of drug discovery, deep learning (DL) can be incorporated at all stages like identification of targets, prognostic biomarkers, drug designing and development, synergism and antagonism prediction, etc. This review summarizes various approaches of deep learning (DL) in drug discovery like deep generative models for drug discovery, deep learning (DL) tools for drug discovery, synergy prediction, and precision medicine.