Immunomodulating capacity of kefir
Bibliographic record
Abstract
Kefir is a fermented milk produced by the action of lactic acid bacteria, yeasts and acetic acid bacteria, trapped in a complex matrix of polysaccharides and proteins. Beyond its inherent high nutritional value as a source of proteins and calcium, kefir has a long tradition of being regarded as good for health in countries where it is a staple in the diet. However, published human or animal feeding trials to substantiate this view are not numerous. The aim of this work was to determine the immunomodulating capacity of kefir on the intestinal mucosal immune response in mice and to demonstrate the importance of dose and cell viability on this response. BALB/c mice were fed with commercial kefir ad libitum (diluted 1/10, 1/50, 1/100 or 1/200) or pasteurized kefir (diluted 1/6, 1/10, 1/50, 1/100) for 2, 5 or 7 consecutive days. At the end of each feeding period, the bacterial translocation assay was performed in the liver. Small intestine structure was studied by haematoxilin-eosin staining and light microscopy. The number of IgA+ and IgG+ cells was also determined. For the functional doses chosen, cytokines (IL-2, IL-4, IL-6, IL-10, IL-12, TNF-alpha and IFN-gamma) were determined. Kefir and pasteurized kefir were able to modulate the mucosal immune system in a dose-dependent manner. Kefir was administred 10-times more diluted than pasteurized kefir, but it induced an immunomodulation of similar magnitude, indicating the importance of cell viabilty. The results suggest that a Th1 response was controlled by Th2 cytokines induced by kefir feeding. Pasteurized kefir would induce both Th2 and Th1 responses. This is the first study in vivo regarding the mechanisms involved in the immunomodulating capacity of the oral administration of kefir containing viable or heat-inactivated bacteria at different doses.
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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".