Differential accumulation and function of proinflammatory 6-sulfo LacNAc dendritic cells in lymph node and colon of Crohn’s versus ulcerative colitis patients
Bibliographic record
Abstract
Human Slan DCs have been studied in patients with psoriasis, rheumatoid arthritis, cancer, and autoimmune diseases. In this study, we investigated the frequency, phenotype, and function of Slan DCs in blood, colon, as well as mLNs of patients with IBD. We first show that the frequency of circulating CD14(dull)Slan DCs was reduced in CD patients refractory to immunosuppressive drugs or TNF-α blockers relative to untreated CD, UC, and healthy subjects. In blood of CD patients, Slan DCs expressed CD172a, as detected by CD47 fusion protein binding, when compared with its lack of expression in control subjects. Next, we demonstrate that CD172a(+)Slan DCs that produced IL-1β and TNF-α accumulated in mLNs and colons of CD patients. The CD172a(+)Slan DCs up-regulated their expression of CD14 in CD tissues and the proinflammatory cytokines were produced in CD14(bright)CD172a(+)Slan DCs. By contrast, no difference was noted in the frequency of Slan DCs between inflamed, noninflamed colonic mucosa of UC patients and control, non-IBD donors. Finally, the percentage of cytokine-producing Slan DCs also augmented in response to TLR2 and NOD2 in in vitro stimulation in PBMCs of CD, but not UC, patients. In conclusion, we propose that proinflammatory CD14(bright)CD172a(+)Slan DCs are a distinguishing feature between CD and UC, as these cells accumulate uniquely in mLNs and colonic mucosa of CD patients. Thus, Slan DCs may contribute to CD immunopathogenesis.
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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 | 0.001 | 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) | 0.002 | 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".