On Lactococcus lactis UL719 competitivity and nisin (Nisaplin®) capacity to inhibit Clostridium difficile in a model of human colon
Bibliographic record
Abstract
Clostridium difficile is the most frequently identified enteric pathogen in patients with nosocomially acquired, antibiotic-associated diarrhea and pseudomembranous colitis. Although metronidazole and vancomycin were effective, an increasing number of treatment failures and recurrence of C. difficile infection are being reported. Use of probiotics, particularly metabolically active lactic acid bacteria, was recently proposed as an alternative for the medical community. The aim of this study was to assess a probiotic candidate, nisin Z-producer Lactococcus lactis UL719, competitivity and nisin (Nisaplin(®)) capacity to inhibit C. difficile in a model of human colon. Bacterial populations was enumerated by qPCR coupled to PMA treatment. L. lactis UL719 was able to survive and proliferate under simulated human colon, did not alter microbiota composition, but failed to inhibit C. difficile. While a single dose of 19 μmol/L (5× the MIC) was not sufficient to inhibit C. difficile, nisin at 76 μmol/L (20×the MIC) was effective at killing the pathogen. Nisin (at 76 μmol/L) caused some temporary changes in the microbiota with Gram-positive bacteria being the mostly affected. These results highlight the capacity of L. lactis UL719 to survive under simulated human colon and the efficacy of nisin as an alternative in the treatment of C. difficile infections.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".