Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy.

BACKGROUND: Improved adenoma detection rate (ADR) has been demonstrated with artificial intelligence (AI)-assisted colonoscopy. However, data on the real-world application of AI and its effect on colorectal cancer (CRC) screening outcomes is limited. AIM: To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or symptoms. METHODS: AI software (GI Genius, Medtronic) was implemented into the standard procedure protocol in November 2022. Data was collected on patient demographics, procedure indication, polyp size, location, and pathology. CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up. RESULTS: We evaluated 1008 colonoscopies (278 pre-AI, 255 early post-AI, 285 established post-AI, and 190 late post-AI). The ADR was 38.1% pre-AI, 42.0% early post-AI (P = 0.77), 40.0% established post-AI (P = 0.44), and 39.5% late post-AI (P = 0.77). There were no significant differences in polyp detection rate (PDR, baseline 59.7%), advanced ADR (baseline 16.2%), and non-neoplastic PDR (baseline 30.0%) before and after AI introduction. CONCLUSION: In patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy. Although the potential of AI in colonoscopy is undisputed, current AI technology may not universally elevate screening metrics across all situations and patient populations. Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.

Identification of gallic acid based glycoconjugates as a novel tubulin polymerization inhibitors.

A novel class of gallic acid based glycoconjugates were designed and synthesized as potential anticancer agents. Among all the compounds screened, compound 2a showed potent anticancer activity against breast cancer cells. The latter resulted in tubulin polymerization inhibition and induced G2/M cell cycle arrest, generation of reactive oxygen species, mitochondrial depolarization and subsequent apoptosis in breast cancer cells. In addition, ultraviolet-visible spectroscopy and fluorescence quenching studies of the compound with tubulin confirmed direct interaction of compounds with tubulin. Molecular modeling studies revealed that it binds at the colchicine binding site in tubulin. Further, 2a also exhibited potent in vivo anticancer activity in LA-7 syngeneic rat mammary tumor model. Current data projects its strong candidature to be developed as anticancer agent.