The effect of fecal microbial transplant on intestinal microbial composition in short‐bowel neonatal piglets
Bibliographic record
Abstract
BACKGROUND: Short-bowel syndrome (SBS) in neonates is associated with microbial dysbiosis due to intestinal surgery, prolonged hospitalization, enteral nutrition, and repeated antibiotic exposure. Sepsis and liver disease, leading causes of morbidity and mortality in SBS, may relate to such intestinal dysbiosis. We investigated the safety and feasibility of fecal microbial transplant (FMT) to alter intestinal microbial composition in SBS piglets. METHODS: Following a 75% distal small-intestinal resection, piglets were fed parenteral nutrition with an elemental diet and randomized to saline (SAL; n = 12) or FMT (n = 12) treatments delivered by gastric tube on day 2 (d2). The FMT donor was a healthy adult pig. Comparisons were also made to healthy sow-fed littermate controls (SOW; n = 6). Stool samples were collected daily, and tissue samples were collected at baseline and termination. Microbial DNA was extracted from stool and analyzed using 16S ribosomal RNA sequencing. RESULTS: All piglets survived to the end point. On d2-d4, FMT piglets had some differences in microbiota composition compared with SAL, SOW, and donor counterparts. Between base and term, there were transitory changes to alpha and beta diversity in FMT and SAL. CONCLUSION: FMT treatment in postsurgical neonatal piglets with SBS appears safe, with no increase in sepsis and no mortality. In SBS piglets, FMT induced transient changes to the intestinal microbiota. However, these changes did not persist long-term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".