Current Protocols: Alopecia Areata Mouse Models for Drug Efficacy and Mechanism Studies
Bibliographic record
Abstract
Alopecia areata is the second most common form of hair loss in humans after androgenetic alopecia. Although a variety of animal models for alopecia areata have been described, currently the C3H/HeJ mouse model is the most commonly used and accepted. Spontaneous hair loss occurs in 15%-25% of older mice in which the lesions wax and wane, similar to the human disease, with alopecia being more common and severe in female mice. Full-thickness skin grafts from mice with spontaneous alopecia areata to young, normal-haired, histocompatible mice provide a highly reproducible model with progressive lesions that makes it useful for drug efficacy and mechanism-based studies. As alopecia areata is a cell-mediated autoimmune disease, transfer of cultured lymph node cells from affected mice to unaffected, histocompatible recipients also promotes disease development and provides an alternative, nonsurgical protocol. Protocols are presented to produce these models such that they can be used to study alopecia areata and to develop novel drug therapies. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Full-thickness skin grafts to reproducibly induce alopecia areata in C3H/HeJ mice Basic Protocol 2: Adoptive transfer of cultured lymphoid cells provides a nonsurgical method to induce alopecia areata in C3H/HeJ mice.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.106 | 0.056 |
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".