Background: Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. Methods: We gathered ten research abstracts from five high impact factor medical journals (n=50) and asked ChatGPT to generate research abstracts based on their titles and journals. We evaluated the abstracts using an artificial intelligence (AI) output detector, plagiarism detector, and had blinded human reviewers try to distinguish whether abstracts were original or generated. Results: All ChatGPT-generated abstracts were written clearly but only 8% correctly followed the specific journal's formatting requirements. Most generated abstracts were detected using the AI output detector, with scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% [12.73, 99.98] compared with very low probability of AI-generated output in the original abstracts of 0.02% [0.02, 0.09]. The AUROC of the AI output detector was 0.94. Generated abstracts scored very high on originality using the plagiarism detector (100% [100, 100] originality). Generated abstracts had a similar patient cohort size as original abstracts, though the exact numbers were fabricated. When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, but that the generated abstracts were vaguer and had a formulaic feel to the writing. Conclusion: ChatGPT writes believable scientific abstracts, though with completely generated data. These are original without any plagiarism detected but are often identifiable using an AI output detector and skeptical human reviewers. Abstract evaluation for journals and medical conferences must adapt policy and practice to maintain rigorous scientific standards; we suggest inclusion of AI output detectors in the editorial process and clear disclosure if these technologies are used. The boundaries of ethical and acceptable use of large language models to help scientific writing remain to be determined.

Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. We gathered fifth research abstracts from five high-impact factor medical journals and asked ChatGPT to generate research abstracts based on their titles and journals. Most generated abstracts were detected using an AI output detector, ‘GPT-2 Output Detector’, with % ‘fake’ scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% ‘fake’ [12.73%, 99.98%] compared with median 0.02% [IQR 0.02%, 0.09%] for the original abstracts. The AUROC of the AI output detector was 0.94. Generated abstracts scored lower than original abstracts when run through a plagiarism detector website and iThenticate (higher scores meaning more matching text found). When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, though abstracts they suspected were generated were vaguer and more formulaic. ChatGPT writes believable scientific abstracts, though with completely generated data. Depending on publisher-specific guidelines, AI output detectors may serve as an editorial tool to help maintain scientific standards. The boundaries of ethical and acceptable use of large language models to help scientific writing are still being discussed, and different journals and conferences are adopting varying policies.

Staphylococcal peptidoglycan is characterized by pentaglycine crossbridges that are crosslinked between adjacent wall peptides by Penicillin-Binding Proteins (PBPs) to confer robustness and flexibility. In Staphylococcus aureus, pentaglycine crossbridges are synthesized by three proteins: FemX adds the first glycine and the homodimers FemA and FemB sequentially add two Gly-Gly dipeptides. Occasionally, serine residues are also incorporated into the crossbridges by enzymes that have heretofore not been identified. Here, we show that the FemA/FemB homologues FmhA and FmhC pair with FemA and FemB to incorporate Gly-Ser dipeptides into crossbridges and to confer resistance to lysostaphin, a secreted bacteriocin that cleaves the pentaglycine-crossbridge. FmhA incorporates serine residues at positions 3 and 5 of the crossbridge. In contrast, FmhC incorporates a single serine at position 5. Serine incorporation also lowers resistance toward oxacillin, an antibiotic that targets PBPs, in both methicillin-sensitive and methicillin-resistant strains of S. aureus.  FmhC is encoded by a gene immediately adjacent to lytN which specifies a hydrolase that cleaves the bond between the fifth glycine of crossbridges and the alanine of the adjacent stem peptide. In this manner, LytN facilitates the separation of daughter cells. Cell wall damage induced upon lytN overexpression can be alleviated by overexpression of fmhC.  Together, these observations suggest that FmhA and FmhC generate peptidoglycan crossbridges with unique serine patterns that provide protection from endogenous murein hydrolases governing cell division and from bacteriocins produced by microbial competitors.

BACKGROUND: Patients with ulcerative colitis often develop medically refractory colonic inflammation or colorectal neoplasia, and approximately 10% to 15% of patients require surgery. The most common surgical procedure is a restorative proctocolectomy with IPAA. Even if the preoperative diagnosis is ulcerative colitis, approximately 10% of patients can develop inflammatory pouch conditions resembling a Crohn’s disease phenotype. OBJECTIVE: This study aimed to review the diagnostic approach, prognosis, and management of IPAA with Crohn's disease–like features. DATA SOURCES: The data sources include search in electronic databases. STUDY SELECTION: This narrative review included studies focusing on pouches with Crohn's disease–like features. MAIN OUTCOME MEASURES: The main topics in this review included the pathogenesis, risk factors, diagnosis, phenotypes, prognosis, and medications of pouches with Crohn's disease–like features. RESULTS: A diagnostic approach for the pouch conditions resembling a Crohn's disease phenotype should be based on history–taking to evaluate its risk factors and endoscopic assessment of the pouch. Prior disease history and pathology, location of pouch complications, and timing of complications offer clues for the differential diagnosis of this phenotype. We advocate for the more descriptive term “pouch with Crohn's disease–like features” and reserve the term “Crohn's disease of the pouch” for patients who undergo IPAA and have a precolectomy diagnosis of Crohn's disease or whose colectomy pathology revealed Crohn's disease. Medications, which are often used for traditional Crohn's disease, show efficacy in pouches with Crohn's disease–like features as well. The poor prognosis associated with pouches with Crohn's disease–like features, particularly the fistulizing phenotype, underscores the importance of proactive monitoring and therapeutic intervention. LIMITATIONS: The limitations include no explicit criteria for article selection. CONCLUSIONS: This review suggests future research should seek to understand the natural history and meaningful shorter and longer term therapeutic targets for these types of pouch phenotypes. Long-term follow-up and prospective preoperative and postoperative interventional trials of treatments and prevention strategies are needed.

INTRODUCTION Fecal calprotectin (Fcal) is a non-invasive, inexpensive biomarker of disease activity. However, patient compliance with this test is variable and incompletely described. We assessed compliance rates with Fcal tests and identified factors associated with non-compliance. METHODS A retrospective chart review of patients with IBD who had a Fcal test ordered through our center between August 2021 and December 2021 was conducted. Demographic, clinical, disease, and test-related information were recorded. Patients with incomplete Fcal orders were sent a survey to better understand their reasons for non- compliance. Simple statistical analysis, multivariable logistic regression, and Bayesian Factor Analysis were performed. RESULTS Of 303 patients, 165 (54.4%) had an order for Fcal. Of the Fcal tests ordered, 55 (33.3%) were not completed. Remission of IBD, no prior Fcal completion, and tests ordered at a distant site were all associated with test non-completion. A multivariable logistic regression revealed that history of a prior completed Fcal test is associated with subsequent test completion (OR = 8.3, 95% CI 1.9-35.5, p = 0.004). Patients who did not complete the test described the pandemic and third-party testing center issues as the most common reasons for non-compliance. CONCLUSIONS In this single center experience with Fcal testing in patients with IBD, we identified that a history of incomplete Fcal testing and distant location of the lab are significantly associated with non- completion of the test. We provide practical guidance for future utilization and compliance, including the impact of home-based testing.