METHODS: The psoralen derivatives were produced through the condensation of seven different types of amine groups consisting of electron withdrawing groups and electron donating groups.
RESULTS: All the synthesised compounds were obtained with moderate to high yields. Structural characterization using ATR-FTIR, 1H NMR, 13C NMR, and HRMS has confirmed their structure. Moreover, in silico evaluation of the psoralen derivatives against the AChE enzyme was performed, and acetylcholinesterase inhibitory activity of psoralen derivatives was also conducted.
CONCLUSION: Results from molecular docking show the potential of compound 12e as AChE inhibitors due to its highest binding energy value. It was further supported by the anti-acetylcholinesterase activity of compound 12e, which has 91.69% inhibition, comparable to galantamine (94.12%). Furthermore, 100 ns run molecular dynamics (MD) simulation was used to refine docking results.
INTRODUCTION: Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.
RESULTS: The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.
CONCLUSION: In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.