Probiotic treatment vs empiric oral antibiotics for managing dysbiosis in short bowel syndrome: Impact on the mucosal and stool microbiota, short‐chain fatty acids, and adaptation
Bibliographic record
Abstract
BACKGROUND: Infants and children with short bowel syndrome (SBS) are presumed to be at risk of gut microbial dysbiosis with potential sequelae of bacterial overgrowth that include sepsis, d-lactic acidosis, mucosal inflammation, and malabsorption. In neonatal piglets with SBS, we compared intestinal microbial composition, short-chain fatty acids (SCFAs), and adaptation given probiotic (PRO) treatment (Lactobacillus and Bifidobacterium spp) vs oral metronidazole (MET). METHODS: Following 75% distal small intestinal resection, piglets were allocated to PRO (500 mg twice a day, n = 7), MET (15 mg/kg twice a day, n = 8), and placebo (PLA) (500 mg twice a day, n = 8). After 10 days of parenteral and enteral nutrition, 16S ribosomal RNA gene amplicon sequencing (colon tissue and stool) was undertaken and SCFA analysis (stool and colon effluent) was performed using gas chromatography. RESULTS: In colon, Shannon diversity was higher for PRO compared with MET and PLA (P = 0.002). PRO and PLA increased abundance of Bacteroidetes species (eg, Bacteroides fragilis) compared with MET (P < 0.001). PRO, compared with PLA, increased abundance of Firmicutes species (eg, Lactobacillus fermentum) (P < 0.001). MET increased abundance of Proteobacteria members, predominately Enterobacteriaceae, compared with PRO (P = 0.004). In stool, microbial findings were similar and SCFA (butyrate) concentrations were highest for PRO (P = 0.003) compared with MET. CONCLUSION: In pediatric SBS, the empiric use of oral antibiotics, such as MET, is common for presumed clinical consequences of microbial dysbiosis. In this study of SBS piglets, that approach was associated with decreased microbial diversity and increased abundance of potentially inflammatory Proteobacteria. In contrast, a PRO treatment using Lactobacillus and Bifidobacterium spp increased both diversity and SCFAs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".