Melanins and melanogenesis: from pigment cells to human health and technological applications
Bibliographic record
Abstract
During the past decade, melanins and melanogenesis have attracted growing interest for a broad range of biomedical and technological applications. The burst of polydopamine-based multifunctional coatings in materials science is just one example, and the list may be expanded to include melanin thin films for organic electronics and bioelectronics, drug delivery systems, functional nanoparticles and biointerfaces, sunscreens, environmental remediation devices. Despite considerable advances, applied research on melanins and melanogenesis is still far from being mature. A closer intersectoral interaction between research centers is essential to raise the interests and increase the awareness of the biomedical, biomaterials science and hi-tech sectors of the manifold opportunities offered by pigment cells and related metabolic pathways. Starting from a survey of biological roles and functions, the present review aims at providing an interdisciplinary perspective of melanin pigments and related pathway with a view to showing how it is possible to translate current knowledge about physical and chemical properties and control mechanisms into new bioinspired solutions for biomedical, dermocosmetic, and technological applications.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".