Layers of mutualism with commensal bacteria protect us from intestinal inflammation
Bibliographic record
Abstract
Mammals are highly adapted to their very high densities of non-pathogenic bacteria which inhabit the lower small intestine and colon. This is achieved by a series of functional and cellular layers that normally avoid the inflammatory consequences of immune activation by bacterial molecules. In this review we set out the layers of mucosal immune defence to show the many ways in which mutualism with the commensal flora can break down to trigger chronic intestinal inflammation in animal models and inflammatory bowel disease (IBD) in humans. The immune system of the intestine is phylogenetically ancient, and its development appears to have preceded most other lymphoid structures, including the thymus.1 The intestinal mucosal immune system is also of immense size. Approximately 70% of all lymphocytes in the body are within the mucosal immune system and most of the antibody produced in healthy individuals is IgA, which is secreted across mucous membranes. Intestinal immunity must be finally balanced between the capacity for mounting protective immune responses to infectious agents and yet tolerate the enormous load of antigens and immunostimulatory molecules that constitute the commensal intestinal bacteria. Commensal bacteria are present in vast numbers.2 The colon is colonised with approximately 1012 organisms/g intestinal contents, making us walking culture media because the bacteria outnumber our own cells! The benefits of commensal bacteria are well known: they help us digest cellulose and salvage energy, synthesise vitamin K, and fill up the microbiological niche that might otherwise be exploited by less benign microbes. Of course, commensals can become pathogens in the right setting, such as debilitated or immunocompromised patients, but on the whole it is impressive how well we coexist with our microbial passengers. Our bodies have adapted in many ways to achieve this harmonious mutualism with our commensals. The evidence for this comes …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".