In vitro evaluation of nutrients that selectively confer a competitive advantage to lactobacilli
Bibliographic record
Abstract
An assay was developed that tested the ability of Lactobacillus acidophilus to outcompete a challenge of Escherichia coli in a mixed culture containing different test nutrients. Using this assay, addition of fructo-oligosaccharide to the media allowed L. acidophilus to outcompete a challenge of E. coli, whereas in a mixed culture without the prebiotic the trend was reversed. Growth curves generated for E. coli in a single culture showed that fructo-oligosaccharide did not affect growth, indicating that the carbohydrate was not toxic to E. coli. This indicates that fructo-oligosaccharides may increase the ability of beneficial microbes to outcompete a pathogenic challenge. These results were confirmed using a skin simulant model that incorporates growth of the organisms at an air-surface interface to mimic the vulvar environment. It is possible to use a co-culture assay as an in vitro screening tool to define nutrients that confer a competitive advantage to beneficial flora specific to the female urogenital tract.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".