The Probiotic Mixture VSL#3 Accelerates Gastric Ulcer Healing by Stimulating Vascular Endothelial Growth Factor
Bibliographic record
Abstract
Studies assessing the effect and mechanism of probiotics on diseases of the upper gastrointestinal tract (GI) including gastric ulcers are limited despite extensive work and promising results of this therapeutic option for other GI diseases. In this study, we investigated the mechanisms by which the probiotic mixture VSL#3 (a mixture of eight probiotic bacteria including Lactobacilli, Bifidobacteria and Streptococcus species) heals acetic acid induced gastric ulcer in rats. VSL#3 was administered orally at low (6 × 10(9) bacteria) or high (1.2 × 10(10) bacteria) dosages from day 3 after ulcer induction for 14 consecutive days. VSL#3 treatments significantly enhanced gastric ulcer healing in a dose-dependent manner. To assess the mechanism(s) whereby VSL#3 exerted its protective effects, we quantified the gene expression of several pro-inflammatory cytokines, protein and expression of stomach mucin-Muc5ac, regulatory cytokine-IL-10, COX-2 and various growth factors. Of all the components examined, only expression and protein production of VEGF was increased 332-fold on day 7 in the ulcerated tissues of animals treated with VSL#3. Predictably, animals treated with VEGF neutralizing antibody significantly delayed gastric ulcer healing in VSL#3 treated animals. This is the first report to demonstrate high efficacy of the probiotic mixture VSL#3 in enhancing gastric ulcer healing. Probiotic efficacy was effective at higher concentrations of VSL#3 by specifically increasing the expression and production of angiogenesis promoting growth factors, primarily VEGF.
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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 | 0.000 |
| 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 | 0.001 |
| 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".