Enhanced Inducible Costimulator Ligand (ICOS-L) Expression on Dendritic Cells in Interleukin-10 Deficiency and Its Impact on T-Cell Subsets in Respiratory Tract Infection
Bibliographic record
Abstract
An association between inducible costimulator ligand (ICOS-L) expression and interleukin (IL)-10 production by dendritic cells (DCs) has been commonly found in infectious disease. DCs with higher ICOS-L expression and IL-10 production are reportedly more efficient in inducing regulatory T cells (Tregs). Here we use the Chlamydia muridarum (Cm) lung infection model in IL-10 knockout (KO) mice to test the relationship between IL-10 production and ICOS-L expression by DCs. We examined ICOS-L expression, the development of T-cell subsets, including Treg, Th17 and Th1 cell, in the background of IL-10 deficiency and its relationship with ICOS-L/ICOS signaling after infection. Surprisingly, we found that the IL-10 KO mice exhibited significantly higher ICOS-L expression by DCs. Moreover, IL-10 KO mice showed lower Tregs but higher Th17 and Th1 responses, but only the Th17 response depended on ICOS signaling. Consistently, most of the Th17 cells were ICOS⁺, whereas most of the Th1 cells were ICOS⁻ in the infected mice. Furthermore, neutralization of IL-17 in IL-10 KO mice significantly exacerbated lung infection. The data suggest that ICOS-L expression on DC may be negatively regulated by IL-10 and that ICOS-L expression on DC in the presence or absence of IL-10 costimulation may promote Treg or Th17 response, without significant impact on Th1.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".