Investigating the effect of antibiotics on gut microbiota components and subsequent Clostridium difficile infection (LB516)
Bibliographic record
Abstract
C. difficile (Cdif) infections (CDI) are a world‐wide epidemic. Recent small clinical trials have generated excitement for the use of fecal microbial transplants as a therapeutic option; however, the exact components of the microbiota needed for protection against CDI have remained elusive. This study assessed the specific intestinal microbiota components associated with prevention or exacerbation of CDI in a mouse model of disease. C57/Bl6 mice from two different vendors (A or B) were exposed to broad‐spectrum antibiotics before oral gavage with Cdif spores. Mice from A and B were co‐housed for 14 days, followed by exposure to antibiotics and development of CDI. Microbiota diversity and composition were analyzed using Illumina MiSeq Next‐Gen Sequencing of 16S rDNA in fecal samples. Mice from different vendors had distinct microbiota profiles both before and after antibiotic exposure. Mice from A developed severe CDI as evidenced by weight loss, histological damage, increased MPO levels and inflammatory cytokines, including KC and IL‐1β. Mice from B were resistant to CDI with minimal histological and cytokine changes from baseline. While similar at naïve levels, vendor B retained a high abundance of Bacteroidetes phyla after antibiotics. Following cohousing, mice from vendor A had less severe CDI and mice from vendor B had more severe CDI. Our data are the first to identify specific patterns in the intestinal microbiota that confer susceptibility to CDI. Furthermore, the bacterial populations that may be critical for preventing or reducing the severity of CDI were revealed. This study may lead to more targeted bacteriotherapy for the treatment and prevention of CDI. Grant Funding Source : Supported by Canadian Institute of Health Research and Alberta Innovates ‐ Health Solutions
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".