OBJECTIVE: This study aims to comprehensively explore the diverse mechanisms of cancer drug resistance, assess the evolution of resistance detection methods, and identify strategies for overcoming this challenge. The evolution of resistance detection methods and identification strategies for overcoming the challenge.
METHODS: A comprehensive literature review was conducted to analyze intrinsic and acquired drug resistance mechanisms, including altered drug efflux, reduced uptake, inactivation, target mutations, signaling pathway changes, apoptotic defects, and cellular plasticity. The evolution of mutation detection techniques, encompassing clinical predictions, experimental approaches, and computational methods, was investigated. Strategies to enhance drug efficacy, modify pharmacokinetics, optimizoptimizee binding modes, and explore alternate protein folding states were examined.
RESULTS: The study comprehensively overviews the intricate mechanisms contributing to cancer drug resistance. It outlines the progression of mutation detection methods and underscores the importance of interdisciplinary approaches. Strategies to overcome drug resistance challenges, such as modulating ATP-binding cassette transporters and developing multidrug resistance inhibitors, are discussed. The study underscores the critical need for continued research to enhance cancer treatment efficacy.
CONCLUSION: This study provides valuable insights into the complexity of cancer drug resistance mechanisms, highlights evolving detection methods, and offers potential strategies to enhance treatment outcomes.
MATERIALS AND METHODS: An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied.
RESULTS: Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis.
CONCLUSIONS: Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.