Fungal melanin detection by the use of copper sulfide-silver
Bibliographic record
Abstract
Silver-staining procedures were investigated for their effectiveness in identifying cell wall-based fungal melanins in live and fixed plastic embedded samples, particularly 1,8-dihydroxynaphthalene (DHN) based polyketide melanins. We developed a simple and reliable melanin-staining technique based on a silver accumulation method originally published for histological demonstration of heavy metal sulfides in mammalian tissues. Copper is bound to fungal melanin followed by formation of the copper sulfide at melanin sites in fungal cell walls, which then are amplified into vivid black stains using a silver enhancement step. The method demonstrates patterns of melanization in a range of fungal hyphae and is suitable for light and electron microscopy. Albino mutant fungi and normally nonmelanized fungi do not stain with the sulfide-silver technique. Mammalian melanocytes also were labeled by the technique, indicating its universality as a melanin probe.
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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".