Increased expression of PD‐L1 and PD‐L2 in dermal fibroblasts from alopecia areata mice
Bibliographic record
Abstract
Alopecia areata (AA) is a common autoimmune disorder affecting millions of people worldwide, which manifests as a sudden, non‐scarring hair loss. The expression of a pro‐inflammatory cytokine, interferon‐gamma (INF‐γ), has been well established to be involved in the development of AA. As IFN‐γ and other cytokines are also known to up‐regulate programmed cell death ligand 1 and 2 (PD‐L1 and PD‐L2), which both negatively control immune responses, we asked whether or not a high number of infiltrated T cells, seen in AA lesions, can modulate the expression of PD‐L1 and PD‐L2 in skin cells. From a series of experiments, we showed that a significantly higher number of PD‐L1 or PD‐L2 positive cells affect the skin in AA mice, compared to the skin of non‐AA mice. The number of PD‐L1 positive cells was well correlated with the number of infiltrated T cells, especially CD8+ T cells. We also found that the expression of PD‐L1 and PD‐L2 was co‐localized with type 1 pro‐collagen, CD90 and vimentin, which are biomarkers for dermal fibroblasts. Further studies revealed that releasable factors from activated, but not inactivated, lymphocytes significantly increase the expressions of both PD‐L1 and PD‐L2 in cultured dermal fibroblasts. In conclusion, our findings suggest that the expression of PD‐L1 and PD‐L2 in dermal fibroblasts is up‐regulated by activated T cells in AA‐affected skin, and as such, these regulatory molecules may not exert a negative control of the immune activation seen in AA lesions.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".