Melan-A Positive cells significantly expressed in immune-targeted follicles of Alopecia Areata
Bibliographic record
Abstract
Abstract Background Alopecia areata is characterized by patchy hair loss that can extend to affect all body hair and about 2% of population are affected. Many hypotheses for etiopathogenesis exit but autoimmunity after the hair immune privilege collapse is a widely accepted theory. Objectives The aim of this study is to examine the expression of Melan A within hair follicles of alopecia areata as one of the melanocytic markers, in order to correlate melanocyte existence to the induction of the disease. Methods Cross sectioned hair follicles within alopecia areata specimens were examined by routine H&E and immunehistochemically for the Melan A marker in 16 alopecia areata patients. Results Within the 82 examined alopecia areata follicles, 50 showed positive cellular expression of Melan A. Positive cells ranged from 1 to 20 per follicle. There was a significant increase in count of Melan A positive cell in immune-targeted follicles of alopecia areata with significant positive correlation. Conclusions Melan A is clearly expressed in hair follicles of alopecia areata within bulbar region and the suprabulbar outer root sheath and the expression is significantly correlated to the immune mononuclear infiltrates that accompany the pathogenesis of the disease. This relevant finding may confirm that melanocytes carry the antigenic epitope required to initiate the organ specific autoimmunity in alopecia areata and also explain the regrowth of only white hair follicles in some cases with alopecia and the mysterious Thomas More syndrome.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".