Bibliographic record
Abstract
In Crohn's disease, the mucus layer appears to be defective in terms of low defensin levels and lack of antibacterial activity. These deficiencies actually explain the Montreal phenotypes and the stable localization of disease in the terminal ileum with low α-defensins from Paneth cells and/or low β-defensins in colonic disease, respectively. Conversely, in ulcerative colitis (UC) the defensin production is normal or even induced, but the mucus layer is thinner and patchy, more in the liquid form and also chemically altered so that antibacterial peptides are not retained and lost into the luminal bacterial bulk. Therefore, both barrier problems allow slow bacterial attachment and invasion, ultimately triggering the massive response of adaptive immunity and tissue destruction. Therefore, leakiness should refer to the antibacterial barrier and not to the general barrier against small molecules, such as mannitol or lactulose, which are not antigenic. The most promising approach in UC seems to be the use of probiotics or the natural compound lecithin as a stabilizer of mucus structure to enhance the barrier. While a phase II study has yielded positive results, the results of the ongoing phase III study are eagerly awaited. It is quite possible that the protective effect of smoking in UC is related to mucus production in the colon also, but this is not an option. Another alternative would be to shift cell differentiation in the colon towards goblet cell; the relevant differentiation factors are known. In Crohn's disease, the direct oral application of defensins might be effective if release and binding to the mucus are achieved. In the experimental colitis model, this works quite well. In conclusion, in a situation where enthusiasm about so-called biologics is declining due to loss of response over time, searching for the primary defects in inflammatory bowel disease and treating them may well be worthwhile, although it is unlikely to provide rapid relief.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".