γδ T cells can play a role in provoking an inflammatory attack on the cornea
Bibliographic record
Abstract
Abstract Ocular immune privilege results from a complex set of mechanisms that together prevent immunogenic inflammation, but still allow the eye to deal with potentially infectious agents in a localized way that does not compromise vision. Failure of immune privilege can unleash a damaging autoimmune attack on the eye. γδ T cells have been found to be important for these processes in a number of different systems. In mice of the C57BL/10 background (B10), we have previously shown that γδ. T cells help prevent the spontaneous development of keratitis, an inflammation of the cornea that B10.TCRδ −/− mice are highly prone to develop, particularly the females. Paradoxically, B10 mice that can produce γδ T cells but not αβ T cells also develop keratitis at a high rate. We recently found that Vγ4+ cells from the spleens of keratitic B10.TCRβ−/− mice, but not Vγ1+ cells, can adoptively transfer the disease to normally keratitis-resistant B10.TCRβ−/−δ−/− hosts. Immunofluorescence staining of corneal whole mounts from B10.TCRβ−/− mice revealed that Vγ4+ cells infiltrate the keratitic corneas, and show a strong bias to secrete IL-17. In contrast, Vγ1+ cells were more rare in the keratitic corneas and did not produce IL-17. The majority of the γδ T cells in keratitic corneas were Vγ1-negative, Vγ4-negative, and Vγ7-negative, however, and did not produce IL-17. We hypothesize that although some γδ T cell subsets protect against autoimmune attack on the cornea, certain Vγ4+ γδ T cells instead promote keratitis by infiltrating the corneas and secreting IL-17, which attracts and mobilizes neutrophils, resulting in tissue damage.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".