Probiotic Supplementation in a Clostridium difficile-Infected Gastrointestinal Model Is Associated with Restoring Metabolic Function of Microbiota
Bibliographic record
Abstract
Clostridium (C.) difficile-infection (CDI), a nosocomial gastrointestinal disorder, is of growing concern due to its rapid rise in recent years. Antibiotic therapy of CDI is associated with disrupted metabolic function and altered gut microbiota. The use of probiotics as an adjunct is being studied extensively due to their potential to modulate metabolic functions and the gut microbiota. In the present study, we assessed the ability of several single strain probiotics and a probiotic mixture to change the metabolic functions of normal and C. difficile-infected fecal samples. The production of short-chain fatty acids (SCFAs), hydrogen sulfide (H2S), and ammonia was measured, and changes in microbial composition were assessed by 16S rRNA gene amplicon sequencing. The C. difficile-infection in fecal samples resulted in a significant decrease (p < 0.05) in SCFA and H2S production, with a lower microbial alpha diversity. All probiotic treatments were associated with significantly increased (p < 0.05) levels of SCFAs and restored H2S levels. Probiotics showed no effect on microbial composition of either normal or C. difficile-infected fecal samples. These findings indicate that probiotics may be useful to improve the metabolic dysregulation associated with C. difficile infection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".