Reproducibility and Expression of Skin Biomarkers in Sun-Damaged Skin and Actinic Keratoses
Bibliographic record
Abstract
OBJECTIVES: To explore p53 and proliferating cell nuclear antigen (PCNA) expression and polyamine content as biomarkers in skin cancer chemoprevention trials, we evaluated their expression in early stages of UV-induced squamous cell tumorigenesis. METHODS: Biopsies were collected from three groups: 78 subjects with sun damage on forearms, 33 with actinic keratosis (AK) on forearms, and 32 with previous squamous cell carcinoma. Participants with sun damage were randomized to sunscreen or no sunscreen. RESULTS: We found significant differences in p53 and polyamines in forearms from the sun-damaged group (11.5 +/- 1.2% for p53, 65.5 +/- 1.9 nmol/g for putrescine, and 187.7 +/- 3.3 nmol/g for spermidine) compared with the group with sun damage plus AK (20.9 +/- 2.3% for p53, P = 0.0001; 81.7 +/- 3.9 nmol/g for putrescine, P = 0.0001; 209.4 +/- 8.2 nmol/g for spermidine, P < 0.06). PCNA was not different. When lesion histology was considered, there was a stepwise significant increase in p53 in biopsies without characteristics of AK compared with early AK (P = 0.02) and AK (P = 0.0006) and a similar pattern for PCNA with the only significant difference between early AK and AK. There was a stepwise increase in putrescine and spermidine in normal, sun-damaged forearm, forearm from subjects with AK, and the AK lesion itself (P < 0.0001). No significant differences in p53 or polyamines were seen in 3-month biopsies or, as a result of sunscreen use, although PCNA in the sun-damaged group not using sunscreen decreased significantly. CONCLUSIONS: p53 expression and polyamines in skin were elevated in early stages of skin tumorigenesis and were not affected by sunscreen, adding validity to their use as biomarkers in skin cancer chemoprevention trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".