The impact of meals on a probiotic during transit through a model of the human upper gastrointestinal tract
Bibliographic record
Abstract
Commercial literature on various probiotic products suggests that they can be taken before meals, during meals or after meals or even without meals. This has led to serious confusion for the industry and the consumer. The objective of our study was to examine the impact of the time of administration with respect to mealtime and the impact of the buffering capacity of the food on the survival of probiotic microbes during gastrointestinal transit. We used an in vitro Digestive System (IViDiS) model of the upper gastrointestinal tract to examine the survival of a commercial multi-strain probiotic, ProtecFlor®. This product, in a capsule form, contains four different microbes: two lactobacilli (Lactobacillus helveticus R0052 and Lactobacillus rhamnosus R0011), Bifidobacterium longum R0175 and Saccharomyces cerevisiae boulardii. Enumeration during and after transit of the stomach and duodenal models showed that survival of all the bacteria in the product was best when given with a meal or 30 minutes before a meal (cooked oatmeal with milk). Probiotics given 30 minutes after the meal did not survive in high numbers. Survival in milk with 1% milk fat and oatmeal-milk gruel were significantly better than apple juice or spring water. S. boulardii was not affected by time of meal or the buffering capacity of the meal. The protein content of the meal was probably not as important for the survival of the bacteria as the fat content. We conclude that ideally, non-enteric coated bacterial probiotic products should be taken with or just prior to a meal containing some fats.
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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 | 0.000 |
| 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".