The gut microbiota participates in the effect of linaclotide in patients with irritable bowel syndrome with constipation (IBS-C): a multicenter, prospective, pre-post study.

BACKGROUND: Interindividual variation characterizes the relief experienced by constipation-predominant irritable bowel syndrome (IBS-C) patients following linaclotide treatment. Complex bidirectional interactions occur between the gut microbiota and various clinical drugs. To date, no established evidence has elucidated the interactions between the gut microbiota and linaclotide. We aimed to explore the impact of linaclotide on the gut microbiota and identify critical bacterial genera that might participate in linaclotide efficacy. METHODS: IBS-C patients were administered a daily linaclotide dose of 290 µg over six weeks, and their symptoms were then recorded during a four-week posttreatment observational period. Pre- and posttreatment fecal samples were collected for 16S rRNA sequencing to assess alterations in the gut microbiota composition. Additionally, targeted metabolomics analysis was performed for the measurement of short-chain fatty acid (SCFA) concentrations. RESULTS: Approximately 43.3% of patients met the FDA responder endpoint after taking linaclotide for 6 weeks, and 85% of patients reported some relief from abdominal pain and constipation. Linaclotide considerably modified the gut microbiome and SCFA metabolism. Notably, the higher efficacy of linaclotide was associated with enrichment of the Blautia genus, and the abundance of Blautia after linaclotide treatment was higher than that in healthy volunteers. Intriguingly, a positive correlation was found for the Blautia abundance and SCFA concentrations with improvements in clinical symptoms among IBS-C patients. CONCLUSION: The gut microbiota, especially the genus Blautia, may serve as a significant predictive microbe for symptom relief in IBS-C patients receiving linaclotide treatment. TRIAL REGISTRATION: This trial was registered with the Chinese Clinical Trial Registry (Chictr.org.cn, ChiCTR1900027934).

A new artificial intelligence system for both stomach and small bowel capsule endoscopy.

BACKGROUND AND AIMS: Despite the benefits of artificial intelligence (AI) in small bowel (SB) capsule endoscopy (CE) image reading, information on its application in the stomach and SB CE is lacking. METHODS: In this multicenter, retrospective diagnostic study, gastric imaging data were added to the deep learning (DL)-based SmartScan (SS), which has been described previously. A total of 1,069 magnetically controlled gastrointestinal (GI) CE examinations (comprising 2,672,542 gastric images) were used in the training phase for recognizing gastric pathologies, producing a new AI algorithm named SS Plus. 342 fully automated, magnetically controlled CE (FAMCE) examinations were included in the validation phase. The performance of both senior and junior endoscopists with both the SS Plus-Assisted Reading (SSP-AR) and conventional reading (CR) modes was assessed. RESULTS: SS Plus was designed to recognize 5 types of gastric lesions and 17 types of SB lesions. SS Plus reduced the number of CE images required for review to 873.90 (1000) (median, IQR 814.50-1,000) versus 44,322.73 (42,393) (median, IQR 31,722.75-54,971.25) for CR. Furthermore, with SSP-AR, endoscopists took 9.54 min (8.51) (median, IQR 6.05-13.13) to complete the CE video reading. In the 342 CE videos, SS Plus identified 411 gastric and 422 SB lesions, whereas 400 gastric and 368 intestinal lesions were detected with CR. Moreover, junior endoscopists remarkably improved their CE image reading ability with SSP-AR. CONCLUSIONS: Our study shows that the newly upgraded DL-based algorithm SS Plus can detect GI lesions and help improve the diagnostic performance of junior endoscopists in interpreting CE videos.