On the horizon: trophic peptide growth factors as therapy for neonatal short bowel syndrome
Bibliographic record
Abstract
INTRODUCTION: Short bowel syndrome (SBS) occurs more commonly in human neonates than in adults. There are currently no approved therapeutic agents aimed directly at stimulating intestinal adaptation in this population. AREAS COVERED: A brief review of SBS and intestinal adaptation is first presented. We then present candidate peptide growth factors that are suggested to augment intestinal adaptation in SBS, with a particular focus on glucagon-like peptide-2, as well as insulin-like growth factor-1 and epidermal growth factor. The normal physiology of these peptides and our understanding of their roles in intestinal adaptation are discussed. We further consider the roles of these peptides in the ontogeny of the gastrointestinal tract and we present the limited preclinical data on the effects of administering these peptides in neonatal SBS. EXPERT OPINION: The clinical translation of trophic peptide therapies in neonatal SBS will require several challenges to be overcome. The optimal dose, timing and route of administration for the likely peptide, or combination of peptides, to be administered will be paramount. Despite their cost to patient care, trophic peptides have shown promise in preclinical models of neonatal SBS and may be especially beneficial for neonates that lack remnant ileum and suffer from irreversible intestinal failure.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".