Study of the Ability of Bifidobacteria of Human Origin to Prevent and Treat Rotavirus Infection Using Colonic Cell and Mouse Models
Bibliographic record
Abstract
Rotavirus is the leading cause of severe acute gastroenteritis among children worldwide. Despite effective vaccines, inexpensive alternatives such as probiotics are needed. The aim of this study was to assess the ability of probiotic candidate Bifidobacterium thermophilum RBL67 to inhibit rotavirus infection. Bacterial adhesion to intestinal cells and interference with viral attachment were evaluated in vitro. B. thermophilum RBL67 displayed adhesion indexes of 625 ± 84 and 1958 ± 318 on Caco-2 and HT-29 cells respectively and was comparable or superior to four other bifidobacteria, including B. longum ATCC 15707 and B. pseudolongum ATCC 25526 strains. Incubation of B. thermophilum RBL67 for 30 min before (exclusion) and simultaneously (competition) with human rotavirus strain Wa decreased virus attachment by 2.0 ± 0.1 and 1.5 ± 0.1 log10 (by 99.0% and 96.8% respectively). Displacement of virus already present was negligible. In CD-1 suckling mice fed B. thermophilum RBL67 challenged with simian rotavirus SA-11, pre-infection feeding with RBL 67 was more effective than post-infection feeding, reducing the duration of diarrhea, limiting epithelial lesions, reducing viral replication in the intestine, accelerating recovery, and stimulating the humoral specific IgG and IgM response, without inducing any adverse effect. B. thermophilum RBL67 had little effect on intestinal IgA titer. These results suggest that humoral immunoglobulin might provide protection against the virus and that B. thermophilum RBL67 has potential as a probiotic able to inhibit rotavirus infection and ultimately reduce its spread.
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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 | 0.000 | 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".