Intestinal microbiome in short bowel syndrome: diagnostic and therapeutic opportunities
Bibliographic record
Abstract
PURPOSE OF REVIEW: The intestinal microbiome plays a strong, complementary role in the development and integrity of the intestinal epithelium. This biology is crucial for intestinal adaptation, particularly after the mucosal insults that lead to short bowel syndrome (SBS). The purpose of this review is to discuss relationships between the intestinal microbiota and the physiology of intestinal adaptation. RECENT FINDINGS: We will address interactions between the intestinal microbiome and nutritional metabolism, factors leading to dysbiosis in SBS, and common compositional differences of the gut microbiome in SBS patients as compared to healthy controls. We will also discuss novel opportunities to expand diagnostic and therapeutic interventions in this population, by using our knowledge of the microbiome to manipulate luminal bacteria and study their resultant metabolites. As microbial therapeutics advance across so many fields of medicine, this review is timely in its advocacy for ongoing research that focuses on the SBS population.Our review will discuss 4 key areas: 1) physiology of the intestinal microbiome in SBS, 2) clinical and therapeutic insults that lead to a state of dysbiosis, 3) currently available evidence on microbiome-based approaches to SBS management, and 4) opportunities and innovations to inspire future research. SUMMARY: The clinical implications of this review are both current, and potential. Understanding how the microbiome impacts intestinal adaptation and host physiology may enhance our understanding of why we experience such clinical variability in SBS patients' outcomes. This review may also expand clinicians' understanding of what 'personalized medicine' can mean for this patient population, and how we may someday consider our nutritional, therapeutic, and prognostic recommendations based on our patients' host, and microbial physiology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".