No abstract

Background: Toll like receptors (TLRs) are a group of transmembrane receptors belonging to the broad class pattern recognition receptors (PRR), involved in the recognition of Pathogen Associated Molecular Patterns (PAMPs) and thereby inducing an immune response. Apart from these exogenous PAMPs, numerous endogenous PAMPs are also ligands for various TLRs thereby activating the TLR dependent immune response, subsequently leading to the onset of an inflammatory response. Prolonged activation of TLR by these endogenous PAMPs leads to chronic inflammatory insults to the body and which in turn alters the proliferative patterns of the cells, which ultimately leads to the development of cancer. Objectives: The present review aims to provide a detailed outline of the differential roles of various TLRs in cancer and the possible use of them as a therapeutic target. Methods: Data were collected from PubMed/Sciencedirect/Web of Science database and sorted; the latest literature on TLRs was incorporated in the review. Results: Among the different TLRs, few are reported to be anti-neoplastic, which controls the cell growth and multiplication in response to the endogenous signals. On the contrary, numerous studies have reported the pro-carcinogenic potentials of TLRs. Hence, TLRs has emerged as a potential target for the prevention and treatment of various types of cancers. Several molecules such as monoclonal antibodies, small molecule inhibitors and natural products have shown promising anticancer potential by effectively modulating the TLR signalling. Conclusion: Toll like receptors play vital roles in the process of carcinogenesis, hence TLR targeting is a promising approach for cancer prevention.

No abstract

This chapter examines how Python can assist in predicting type 2 diabetes using insulin DNA sequences, given the substantial problem that biologists face in objectively evaluating diverse biological characteristics of DNA sequences. The chapter highlights Python's various libraries, such as NumPy, Pandas, and Scikit-learn, for data handling, analysis, and machine learning, as well as visualization tools, such as Matplotlib and Seaborn, to help researchers understand the relationship between different DNA sequences and type 2 diabetes. Additionally, Python's ease of integration with other bioinformatics tools, like BLAST, EMBOSS, and ClustalW, can help identify DNA markers that could aid in predicting type 2 diabetes. In addition, the initiative tries to identify unique gene variants of insulin protein that contribute to diabetes prognosis and investigates the risk factors connected with the discovered gene variants. In conclusion, Python's versatility and functionality make it a valuable tool for researchers studying insulin DNA sequences and type 2 diabetes prediction.