Induction of a Humoral Immune Response following an<i>Escherichia coli</i>O157:H7 Infection with an Immunomodulatory Peptidic Fraction Derived from<i>Lactobacillus helveticus</i>-Fermented Milk
Bibliographic record
Abstract
Numerous beneficial effects have been attributed to probiotic lactic acid bacteria (LAB), such as the stimulation of the immune system, the prevention of enteric infections by enteropathogens, and the regression of immunodependent tumors. It has been shown that biologically active metabolites released during fermentation, in particular biopeptides, could act as immunomodulatory agents. However, no studies have been conducted to evaluate the implication of these bioactive peptides in the induction of a protective immune response against enteric infections. The present study aimed to evaluate the possible immunomodulatory and anti-infectious effects of a peptidic fraction released in milk fermented by Lactobacillus helveticus. The immune response in the mucosa-associated lymphoid tissue was monitored following an administration of the potentially bioactive peptidic fraction. The total immunoglobulin A (IgA) immune response was evaluated after an Escherichia coli O157:H7 infection in a BALB/c murine model. Immunohistochemical and enzyme-linked immunosorbent assays revealed an increase in the number of IgA-secreting B lymphocytes in the intestinal lamina propria and an enhanced total secretory and systemic IgA response. Cytokine profiling also revealed stimulation of a Th2 response in mice fed the peptidic fraction, whereas infected controls demonstrated a proinflammatory Th1 response. These results indicate that bioactive peptides released during fermentation by LAB could contribute to the known immunomodulatory effects of probiotic bacteria.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".