The immune-enhancing effects of dietary fibres and prebiotics
Bibliographic record
Abstract
The gastrointestinal tract is subjected to enormous and continual foreign antigenic stimuli from food and microbes. This organ must integrate complex interactions among diet, external pathogens, and local immunological and non-immunological processes. It is critical that protective immune responses are made to potential pathogens, while hypersensitivity reactions to dietary antigens are minimised. There is increasing evidence that fermentable dietary fibres and the newly described prebiotics can modulate various properties of the immune system, including those of the gut-associated lymphoid tissues (GALT). This paper reviews evidence for the immune-enhancing effects of dietary fibres. Changes in the intestinal microflora that occur with the consumption of prebiotic fibres may potentially mediate immune changes via: the direct contact of lactic acid bacteria or bacterial products (cell wall or cytoplasmic components) with immune cells in the intestine; the production of short-chain fatty acids from fibre fermentation; or by changes in mucin production. Although further work is needed to better define the changes, mechanisms for immunomodulation, and the ultimate impact on immune health, there is convincing preliminary data to suggest that the consumption of prebiotics can modulate immune parameters in GALT, secondary lymphoid tissues and peripheral circulation. Future protocols on the physiological impact of consuming prebiotics should be designed to include assessments of the gut microflora, gut physiology and the function and composition of the various regions of GALT.
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) | 0.000 | 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".