In vivo coherent Raman imaging of the melanomagenesis-associated pigment pheomelanin
Bibliographic record
Abstract
Abstract Melanoma is the most deadly form of skin cancer with a yearly global incidence over 232,000 patients. Individuals with fair skin and red hair exhibit the highest risk for developing melanoma, with evidence suggesting the red/blond pigment known as pheomelanin may elevate melanoma risk through both UV radiation-dependent and -independent mechanisms. Although the ability to identify, characterize, and monitor pheomelanin within skin is vital for improving our understanding of the underlying biology of these lesions, no tools exist for real-time, in vivo detection of the pigment. Here we show that the distribution of pheomelanin in cells and tissues can be visually characterized non-destructively and noninvasively in vivo with coherent anti-Stokes Raman scattering (CARS) microscopy, a label-free vibrational imaging technique. We validated our CARS imaging strategy in vitro to in vivo with synthetic pheomelanin, isolated melanocytes, and the Mc1re/e, red-haired mouse model. Nests of pheomelanotic melanocytes were observed in the red-haired animals, but not in the genetically matched Mc1re/e; Tyrc/c (“albino-red-haired”) mice. Importantly, samples from human amelanotic melanomas subjected to CARS imaging exhibited strong pheomelanotic signals. This is the first time, to our knowledge, that pheomelanin has been visualized and spatially localized in melanocytes, skin, and human amelanotic melanomas.
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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 | 0.000 | 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".