Pathobiont release from dysbiotic gut microbiota biofilms in intestinal inflammatory diseases: a role for iron?
Bibliographic record
Abstract
Gut microbiota interacting with an intact mucosal surface are key to the maintenance of homeostasis and health. This review discusses the current state of knowledge of the biofilm mode of growth of these microbiota communities, and how in turn their disruptions may cause disease. Beyond alterations of relative microbial abundance and diversity, the aim of the review is to focus on the disruptions of the microbiota biofilm structure and function, the dispersion of commensal bacteria, and the mechanisms whereby these dispersed commensals may become pathobionts. Recent findings have linked iron acquisition to the expression of virulence factors in gut commensals that have become pathobionts. Causal studies are emerging, and mechanisms common to enteropathogen-induced disruptions, as well as those reported for Inflammatory Bowel Disease and colo-rectal cancer are used as examples to illustrate the great translational potential of such research. These new observations shed new light on our attempts to develop new therapies that are able to protect and restore gut microbiota homeostasis in the many disease conditions that have been linked to microbiota dysbiosis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".