Overexpression of p27 <sup>KIP1</sup> in Seborrheic Keratosis
Bibliographic record
Abstract
BACKGROUND: The pathogenesis of seborrheic keratosis (SK) is not well understood. SKs are slow growing, but the details of cell cycle control in these lesions are not known. We hypothesized that cyclin-dependent kinase inhibitors would be strongly expressed in SKs and that the proliferation rate would be low. OBJECTIVES: To quantify the expression of Ki67, p16(INK4a), p21(WAF1), and p27(KIP1 )in SK. METHODS: We assessed acanthotic SKs (n=10) and irritated SKs (n=10) for Ki67, p16(INK4a), p21(WAF1), and p27(KIP1 )expression using immunohistochemistry. RESULTS: For nonirritated acanthotic pattern SKs, the Ki67 index was 3.4% (range 0.6-6.5%), confirming a low proliferation rate. The p16(INK4A) index was 6.0% (range 0-16%), and the p21(WAF1) index was 4.8% (range 0-25%). p27(KIP1) was strongly and diffusely expressed in all SKs, with a labeling index of 78% (range 75-85%). The labeling indices were similar in irritated SK lesions with a slightly increased proliferation rate and corresponding decrease in p27(KIP1) expression. CONCLUSIONS: We conclude that in SKs, strong expression of the cyclin-dependent kinase inhibitor p27(KIP1) appears to be a major mechanism controlling keratinocyte proliferation.
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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) | 0.001 | 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".