In a Neonatal Piglet Model of Intestinal Failure, Administration of Antibiotics and Lack of Enteral Nutrition Have a Greater Impact on Intestinal Microflora Than Surgical Resection Alone
Bibliographic record
Abstract
BACKGROUND: Data are limited on how short bowel syndrome (SBS) affects the healthy developing intestinal microbiome, with even less assessing different SBS anatomical surgical models. This study was conducted to describe the "ileal" and "colonic" microflora in 2 surgical models of SBS. MATERIALS AND METHODS: Neonatal piglets (2-5 days old) underwent intestinal resection, leaving the ileum (JI anatomy, n = 6) or removing the ileum and ileocecal valve (JC anatomy, n = 5), or sham surgery (sham; n = 4). JI, JC, and sham piglets commenced parenteral nutrition on day 0 and received ampicillin and trimethoprim-sulfadoxine on days 0-4 for prevention of line sepsis. At day 7, ileal and colonic digesta were collected, and they were also collected from age-matched sow-fed piglets (n = 6). DNA extraction, sequencing, and annotation followed standard procedures. RESULTS: Colonic and ileal bacterial genus diversity and relative bacterial abundance were greater ( P < .05) in sow-fed compared with JI, JC, and sham piglets; however, minor differences were observed in either location between sham, JI, and JC piglets and within the surgical model. In the colon, sow-fed piglets had higher ( P < .05) abundance of Lactobacillus (26%) and tended to have lower ( P = .06) abundance of Enterococcus (<.1%) than JI, JC, or sham piglets, in which Lactobacillus and Enterococcus abundance averaged <.1% and 9%, respectively. CONCLUSIONS: Intestinal resection reduces bacterial diversity in the large bowel, and the difference is associated with the presence/absence of the ileum and ileocecal valve. The lack of enteral nutrition and antibiotic administration (ie, sow-fed vs surgery) had a greater influence on the observed shift in diversity and relative abundance than intestinal resection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".