Fibroblast cell-based therapy prevents induction of alopecia areata in an experimental model
Bibliographic record
Abstract
Alopecia areata (AA) is an autoimmune hair loss disease with infiltration of proinflammatory cells into hair follicles. Current therapeutic regimens are unsatisfactory mainly because of the potential for side effects and/or limited efficacy. Here we report that cultured, transduced fibroblasts, which express the immunomodulatory molecule indoleamine 2,3-dioxygenase (IDO), can be applied to prevent hair loss in an experimental AA model. A single intraperitoneal (IP) injection of IDO-expressing primary dermal fibroblasts was given to C3H/HeJ mice at the time of AA induction. While 60–70% of mice that received either control fibroblasts or vehicle injections developed extensive AA, none of the IDO-expressing fibroblast-treated mice showed new hair loss up to 20 weeks post injection. IDO cell therapy significantly reduced infiltration of CD4 + and CD8 + T cells into hair follicles and resulted in decreased expression of TNF-α, IFN-γ and IL-17 in the skin. Skin draining lymph nodes of IDO fibroblast-treated mice were significantly smaller, with more CD4 + CD25 + FoxP3 + regulatory T cells and fewer Th17 cells than those of control fibroblast and vehicle-injected mice. These findings indicate that IP injected IDO-expressing dermal fibroblasts can control inflammation and thereby prevent AA hair loss.
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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 | 0.001 |
| 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".