Spontaneously occurring melanoma in animals and their relevance to human melanoma
Bibliographic record
Abstract
In contrast to other cancer types, melanoma incidence has been increasing over the last 50 years, and while it still represents less than 5% of all cutaneous malignancies, melanoma accounts for the majority of skin cancer deaths, due to its propensity to metastasise. Whilst melanoma most commonly affects the skin, it can also arise in mucosal surfaces, the eye, and the brain. For new therapies to be developed, a better understanding of the genetic landscape, signalling pathways, and tumour-microenvironmental interactions is needed. This is where animal models are of critical importance. The mouse is the foremost used model of human melanoma. Arguably this is due to its plethora of benefits as a laboratory animal; however, it is important to note that unlike humans, melanocytes are not present at the dermal-epidermal junction in mice and mice do not develop melanoma without genetic manipulation. In contrast, there are numerous reports of animals that spontaneously develop melanoma, ranging from sharks and parrots to hippos and monkeys. In addition, several domesticated and laboratory-bred animals spontaneously develop melanoma or UV-induced melanoma, specifically, fish, opossums, pigs, horses, cats, and dogs. In this review, we look at spontaneously occurring animal 'models' of melanoma and discuss their relevance to the different types of melanoma found in humans. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland..
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".