Innate host responses to enteric bacterial pathogens: a balancing act between resistance and tolerance
Bibliographic record
Abstract
Infection by enteric bacterial pathogens activates pathogen recognition receptors, leading to innate responses that promote host defence. While responses that promote host 'resistance' to infection, through the release of antimicrobial mediators, or the recruitment of inflammatory cells aimed at clearing the infection are best known, recent studies have begun to identify additional innate driven responses that instead promote intestinal tissue repair and host survival. Described as infection 'tolerance' responses, we and others have primarily studied these responses in the Citrobacter rodentium infection model. In this review we discuss the impact of innate resistance mechanisms on host defence, and describe how 'tolerance' responses act primarily on the intestinal epithelium, triggering epithelial cell proliferation, repair or promoting barrier function. Resistance and tolerance responses appear to work together, with tolerance repairing the tissue injury caused by resistance driven inflammation. Tolerance responses fit a pattern where innate immunity and inflammation are tightly regulated in the gastrointestinal tract. Moreover, tolerance may have developed due to the successful subversion and avoidance of host resistance by enteric bacterial pathogens. Further studies are needed to clarify the contribution of different pathogen recognition receptors to tolerance and resistance responses against bacterial pathogens, in the gut or in other host tissues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".