Bibliographic record
Abstract
Fungi can produce myriad secondary metabolites, including pigments. Some of these pigments play a positive role in human welfare while others are detrimental. This paper reviews the types and biosynthesis of fungal pigments, their relevance to human health, including their interactions with host immunity, and recent progresses in their structure-activity relationships. Fungal pigments are grouped into carotenoids, melanin, polyketides, and azaphilones, etc. These pigments are phylogenetically broadly distributed. While the biosynthetic pathways for some fungal pigments are known, the majority remain to be elucidated. Understanding the genes and metabolic pathways involved in fungal pigment synthesis is essential to genetically manipulate the production of both the types and quantities of specific pigments. A variety of fungal pigments have shown wide-spectrum biological activities, including promising pharmacophores/lead molecules to be developed into health-promoting drugs to treat cancers, cardiovascular disorders, infectious diseases, Alzheimer's diseases, and so on. In addition, the mechanistic elucidation of the interaction of fungal pigments with the host immune system provides valuable clues for fighting fungal infections. The great potential of fungal pigments have opened the avenues for academia and industries ranging from fundamental biology to pharmaceutical development, shedding light on our endeavors for disease prevention and treatment.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".