Delivery of a mucin domain enriched in cysteine residues strengthens the intestinal mucous barrier
Bibliographic record
Abstract
A weakening of the gut mucous barrier permits an increase in the access of intestinal luminal contents to the epithelial cells, which will trigger the inflammatory response. In inflammatory bowel diseases, there is an inappropriate and ongoing activation of the immune system, possibly because the intestinal mucus is less protective against the endogenous microflora. General strategies aimed at improving the protection of the intestinal epithelium are still missing. We generated a transgenic mouse that secreted a molecule consisting of 12 consecutive copies of a mucin domain into its intestinal mucus, which is believed to modify the mucus layer by establishing reversible interactions. We showed that the mucus gel was more robust and that mucin O-glycosylation was altered. Notably, the gut epithelium of transgenic mice housed a greater abundance of beneficial Lactobacillus spp. These modifications were associated with a reduced susceptibility of transgenic mice to chemically induced colitis. Furthermore, transgenic mice cleared faster Citrobacter rodentium bacteria which were orally given and mice were more protected against bacterial translocation induced by gavage with adherent-invasive Escherichia coli. Our data show that delivering the mucin CYS domain into the gut lumen strengthens the intestinal mucus blanket which is impaired in inflammatory bowel diseases.
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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) | 0.000 | 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".