Explore the vast range of titles available.

Long before coronavirus disease of 2019 (COVID-19) was deemed to be a human infection, Clostridioides difficile infection (CDI) has been among the top 5 urgent infectious threats identified by the US Centers for Disease Control and Prevention. DONOR TESTING FOR INFECTIONS AND NOVEL CORONAVIRUS Apart from the general health screening questions and blood tests, several stool tests are recommended for donors including those for enteric pathogens and screening for multidrug-resistant infections (13). Donors who fail this screen should be excluded from donating stool and channeled through the local resources available for COVID-19 screening, testing, and self-isolation if applicable. Because the disease is now a pandemic worldwide, donors who pass this screen should be considered for COVID-19 testing (nasal swab or stool) if adequate numbers of test kits become available. [...]as a vaccine becomes available, vaccination against COVID-19 would become required to qualify to be a stool donor.

The pathogenesis of Clostridioides difficile infection (CDI) was recognized with its link to the use of antimicrobials. Antimicrobials significantly alter gut microbiota structure and composition, which led to the discovery of the association of this gut perturbation with the development of CDI. A number of factors implicated in its pathogenesis, such as advancing age, proton-pump inhibitors, and gastrointestinal diseases, are linked to gut microbiota perturbations. In an effort to better understand CDI, a multitude of studies have tried to ascertain protective and predictive microbial footprints linked with CDI. It has further been realized that CDI in itself can alter the gut microbiome. Its spore-forming capability poses as an impediment in the management of the infection and contributes to its recurrence. Antibiotic therapies used for its management have also been linked to gut microbiota changes, making its treatment a little more challenging. In an effort to exploit and utilize this association, gut microbial restoration therapies, particularly in the form of fecal microbial transplant, are increasingly being put to use and are proving to be beneficial. In this review, we summarize the association of the gut microbiome and microbial perturbation with initial and recurrent CDI.

The species composition of the human gut microbiota is related to overall health, and a healthy gut microbiome is crucial in maintaining colonization resistance against pathogens. Disruption of gut microbiome composition and functionality reduces colonization resistance and has been associated with several gastrointestinal and non-gastrointestinal diseases. One prime example is Clostridioides difficile infection (CDI) and subsequent recurrent infections that occur after the development of systemic antibiotic-related dysbiosis. Standard-of-care antibiotics used for both acute and recurrent infections do not address dysbiosis and often worsen the condition. Moreover, monoclonal antibodies, recommended in conjunction with standard-of-care antibiotics for the prevention of recurrent CDI in patients at high risk of recurrence, reduce recurrences but do not address the underlying dysbiosis. Fecal microbiota transplantation (FMT) is an evolving therapeutic strategy in which microbes are harvested from healthy donor stool and transplanted into the gut of a recipient to restore the gut microbiome. Although effective in the prevention of recurrent CDI, some existing challenges include screening and the standardization of stool acquisition and processing. Recent safety alerts by the US Food and Drug Administration raised concern about the possibility of transmission of multidrug-resistant organisms or severe acute respiratory syndrome coronavirus 2 via FMT. Increased knowledge that microbes are beneficial in restoring the gut microbiome has led to the clinical development of several newer biotherapeutic formulations that are more regulated than FMT, which may allow for improved restoration of the gut microbiome and prevention of CDI recurrence. This review focuses on mechanisms by which gut microbiome restoration could influence colonization resistance against the pathogen C. difficile. Plain language summary The Role of the Gut Microbiome in Clostridioides difficile Infection Introduction: A rich and diverse gut microbiome is key to immune system regulation and colonization resistance against pathogens. A disruption in the gut microbiome composition can make the gut more vulnerable to diseases such as Clostridioides difficile infection (CDI), caused by the bacterium C. difficile. CDI management presents a therapeutic dilemma, as it is usually treated with antibiotics that can treat the infection but also can damage the microbiome. Treatment of CDI using antibiotics can further reduce microbial diversity and deplete beneficial bacteria from the gut leading to a condition called dysbiosis. Antibiotic treatment can be followed by therapies that restore the gut microbiota, boost colonization resistance, and prevent the development of antimicrobial resistance. It is important to evaluate treatment options to determine their safety and effectiveness. Methods: The researchers provided an overview of the mechanisms that the gut microbiome uses to prevent colonization of the gut by pathogens. They subsequently reviewed the efficacy and shortcomings of the following treatments for CDI: - Antibiotics - Monoclonal antibodies - Fecal microbiota transplantation (FMT) Results: Commensal intestinal bacteria prevent colonization of the gut by pathogens using mechanisms such as: - Competition for key nutrients - Production of inhibitory bile acids - Short-chain fatty acid production - Lowering the luminal pH - Production of bacteriocins Antibiotic therapy is recommended as a standard treatment for CDI. However, patients are vulnerable to recurrent CDI after discontinuation of the therapy. Monoclonal antibodies that inactivate C. difficile toxins may be recommended along with antibiotics to prevent recurrent CDI. However, this approach does not restore the microbiome. FMT is one method of microbial restoration, where stool is harvested from a healthy donor and transplanted into a patient’s colon. Although FMT has shown some efficacy in the treatment of recurrent CDI, the procedure is not standardized. Safety concerns have been raised about the possibility of transmission of multidrug-resistant pathogens via FMT. Conclusion: Treatment methods that can efficiently restore the diversity of the gut microbiome are crucial in preventing recurrence of CDI.

The importance of the commensal microbiota that colonizes the skin, gut, and mucosal surfaces of the human body is being increasingly recognized through a rapidly expanding body of science studying the human microbiome. Although, at first glance, these discoveries may seem esoteric, the clinical implications of the microbiome in human health and disease are becoming clear. As such, it will soon be important for practicing clinicians to have an understanding of the basic concepts of the human microbiome and its relation to human health and disease. In this Concise Review, we provide a brief introduction to clinicians of the concepts underlying this burgeoning scientific field and briefly explore specific disease states for which the potential role of the human microbiome is becoming increasingly evident, including Clostridium difficile infection, inflammatory bowel disease, colonization with multidrug-resistant organisms, obesity, allergic diseases, autoimmune diseases, and neuropsychiatric illnesses, and we also discuss current and future roles of microbiome restorative therapies.

Background: Prolonged symptoms after COVID-19 are an important concern due to the large numbers affected by the pandemic. Objectives: To ascertain the frequency of gastrointestinal (GI) manifestations as part of long GI COVID. Design: A systematic review and meta-analysis of studies reporting GI manifestations in long COVID was performed. Data Sources and Methods: Electronic databases (Medline, Scopus, Embase, Cochrane Central Register of Controlled Trials, and Web of Science) were searched till 21 December 2021 to identify studies reporting frequency of GI symptoms in long COVID. We included studies reporting overall GI manifestations or individual GI symptoms as part of long COVID. We excluded pediatric studies and those not providing relevant information. We calculated the pooled frequency of various symptoms in all patients with COVID-19 and also in those with long COVID using the inverse variance approach. All analysis was done using R version 4.1.1 using packages ‘meta’ and ‘metafor’. Results: A total of 50 studies were included. The frequencies of GI symptoms were 0.12 [95% confidence interval (CI), 0.06–0.22, I2 = 99%] and 0.22 (95% CI, 0.10–0.41, I2 = 97%) in patients with COVID-19 and those with long COVID, respectively. The frequencies of abdominal pain, nausea/vomiting, loss of appetite, and loss of taste were 0.14 (95% CI, 0.04–0.38, I2 = 96%), 0.06 (95% CI, 0.03–0.11, I2 = 98%), 0.20 (95% CI, 0.08–0.43, I2 = 98%), and 0.17 (95% CI, 0.10–0.27, I2 = 95%), respectively, after COVID-19. The frequencies of diarrhea, dyspepsia, and irritable bowel syndrome were 0.10 (95% CI, 0.04–0.23, I2 = 98%), 0.20 (95% CI, 0.06–0.50, I2 = 97%), and 0.17 (95% CI, 0.06–0.37, I2 = 96%), respectively. Conclusion: GI symptoms in patients were seen in 12% after COVID-19 and 22% as part of long COVID. Loss of appetite, dyspepsia, irritable bowel syndrome, loss of taste, and abdominal pain were the five most common GI symptoms of long COVID. Significant heterogeneity and small number of studies for some of the analyses are limitations of the systematic review.

Large language models (LLMs) transform healthcare by assisting clinicians with decision-making, research, and patient management. In gastroenterology, LLMs have shown potential in clinical decision support, data extraction, and patient education. However, challenges such as bias, hallucinations, integration with clinical workflows, and regulatory compliance must be addressed for safe and effective implementation. This manuscript presents a structured framework for integrating LLMs into gastroenterology, using Hepatitis C treatment as a real-world application. The framework outlines key steps to ensure accuracy, safety, and clinical relevance while mitigating risks associated with artificial intelligence (AI)-driven healthcare tools. The framework includes defining clinical goals, assembling a multidisciplinary team, data collection and preparation, model selection, fine-tuning, calibration, hallucination mitigation, user interface development, integration with electronic health records, real-world validation, and continuous improvement. Retrieval-augmented generation and fine-tuning approaches are evaluated for optimizing model adaptability. Bias detection, reinforcement learning from human feedback, and structured prompt engineering are incorporated to enhance reliability. Ethical and regulatory considerations, including the Health Insurance Portability and Accountability Act, General Data Protection Regulation, and AI-specific guidelines (DECIDE-AI, SPIRIT-AI, CONSORT-AI), are addressed to ensure responsible AI deployment. LLMs have the potential to enhance decision-making, research efficiency, and patient care in gastroenterology, but responsible deployment requires bias mitigation, transparency, and ongoing validation. Future research should focus on multi-institutional validation and AI-assisted clinical trials to establish LLMs as reliable tools in gastroenterology. Plain language summary How large language models could transform gastroenterology: a framework for future research and care Artificial intelligence (AI) is transforming healthcare by helping doctors make better decisions, analyze research faster, and improve patient care. Large language models (LLMs) are a type of AI that process and generate human-like text, making them useful in gastroenterology. This paper presents a structured framework for safely using LLMs in clinical practice, using Hepatitis C treatment as an example. The framework begins by setting clear goals, such as improving Hepatitis C treatment recommendations or making patient education easier to understand. A team of doctors, AI specialists, and data experts is assembled to ensure the model is medically accurate and practical. Next, relevant medical data from electronic health records (EHRs), clinical guidelines, and research studies is gathered and prepared to improve AI, ensuring it provides useful and fair recommendations. The right AI model is then chosen and improved to specialize in gastroenterology. To make sure the model is reliable and makes correct suggestions, its performance is checked and adjusted before use. A user-friendly interface is created so doctors can access AI-generated recommendations directly in EHRs and decision-support tools, making it easy to integrate into daily practice. Before full use, the AI is tested in real-world settings, where gastroenterologists review its recommendations for safety and accuracy. Once in use, ongoing updates based on doctor feedback help improve its performance. Ethical and legal safeguards, such as protecting patient privacy and ensuring fairness, guide its responsible use. Findings are then shared with the medical community, allowing for further testing and broader adoption. By following this framework, LLMs can help doctors make better decisions, personalize treatments, and improve efficiency, ultimately leading to better patient outcomes in gastroenterology.