Synthesis, characterization and optimization of <i>in vitro</i> properties of NIR-fluorescent cyclic α-MSH peptides for melanoma imaging
Bibliographic record
Abstract
Melanoma are malignant tumors derived from melanocytes being responsible for the majority of skin cancer deaths with an increasing rate of incidence. The Melanocortin-1 receptor (MC1R) has been recognized as a molecular target for melanoma detection. Here, we report on the development and optimization of molecular probes which are based on novel conjugates of near-infrared (NIR) fluorescent indocyanine dyes and an MC1R-targeting peptide intended for optical fluorescence imaging enabling an early, specific, accurate and sensitive diagnosis of malignant melanomas. The introduction of anionic groups into the aromatic ring of the indolenine substructure of the conjugated dyes has shown to result in a strong fluorescence in aqueous solution and a concomitant increase of binding affinities of the peptide conjugates to the target receptor. The length and flexibility of the PEG chain introduced as a linker, as well as the nature of its attachment to the dye also affect the binding affinities, albeit to a lower extent. The conjugates have been successfully applied in the MC1R-specific staining of B16F10 melanoma cells, both in cell cultures and in microtome sections of solid tumors.
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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".