Probiotics in Clostridium difficile infection: reviewing the need for a multistrain probiotic
Bibliographic record
Abstract
In the past two years an enormous amount of molecular, genetic, metabolomic and mechanistic data on the host-bacterium interaction, a healthy gut microbiota and a possible role for probiotics in Clostridium difficile infection (CDI) has been accumulated. Also, new hypervirulent strains of C. difficile have emerged. Yet, clinical trials in CDI have been less promising than in antibiotic associated diarrhoea in general, with more meta-analysis than primary papers on CDI-clinical-trials. The fact that C. difficile is a spore former, producing at least three different toxins has not yet been incorporated in the rational design of probiotics for (recurrent) CDI. Here we postulate that the plethora of effects of C. difficile and the vast amount of data on the role of commensal gut residents and probiotics point towards a multistrain mixture of probiotics to reduce CDI, but also to limit (nosocomial) transmission and/or endogenous reinfection. On the basis of a retrospective chart review of a series of ten CDI patients where recurrence was expected, all patients on adjunctive probiotic therapy with multistrain cocktail (Ecologic®AAD/OMNiBiOTiC® 10) showed complete clinical resolution. This result, and recent success in faecal transplants in CDI treatment, are supportive for the rational design of multistrain probiotics for CDI.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".