Paneth cell marker CD24 in NOD2 knockout organoids and in inflammatory bowel disease (IBD)
Bibliographic record
Abstract
We read with great interest the article by Shanahan et al 1 describing the roles of environmental conditions, notably co-housing with wild type (WT) littermates, and mouse genetic background in nucleotide-binding oligomerisation domain-containing protein 2 (NOD2)-dependent production of anti-microbial peptides in the mouse intestine. These authors demonstrate that expression, translation and anti-microbial activity of α-defensins are independent of NOD2.1 Robertson et al 2 recently confirmed that housing conditions rather than NOD2 status influenced intestinal microbiota composition. Shanahan et al address the question whether an increase in the number of Paneth cells could compensate for a NOD2-dependent reduction in the level of defensin production in Paneth cells. Using a combination of hematoxylin and eosin staining (to assess crypt numbers), immunohistochemistry (using anti-lyzozyme staining) and flow cytometry (sorting for expression of lyzozyme and lack of expression of CD45—a haematopoietic cell marker) of the entire ilea of WT and NOD2-deficient mice showed NOD2 status did not influence Paneth cell numbers.1 CD24, a heavily glycosylated protein marker of intestinal crypt stem cells and Paneth cells, is upregulated in inflammatory bowel disease (IBD).3 ,4 Sato et al 3 demonstrated that the combination of CD24hi and …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".