Production of Fungal Pigments: Molecular Processes and Their Applications
Bibliographic record
Abstract
Due to the negative environmental and health effects of synthetic colorants, pigments of natural origins of plants and microbes constitute an abundant source for the food, cosmetic, textile, and pharmaceutical industries. The demands for natural alternatives, which involve natural colorants and natural biological processes for their production, have been growing rapidly in recent decades. Fungi contain some of the most prolific pigment producers, and they excel in bioavailability, yield, cost-effectiveness, and ease of large-scale cell culture as well as downstream processing. In contrast, pigments from plants are often limited by seasonal and geographic factors. Here, we delineate the taxonomy of pigmented fungi and fungal pigments, with a focus on the biosynthesis of four major categories of pigments: carotenoids, melanins, polyketides, and azaphilones. The molecular mechanisms and metabolic bases governing fungal pigment biosynthesis are discussed. Furthermore, we summarize the environmental factors that are known to impact the synthesis of different fungal pigments. Most of the environmental factors that enhance fungal pigment production are related to stresses. Finally, we highlight the challenges facing fungal pigment utilization and future trends of fungal pigment development. This integrated review will facilitate further exploitations of pigmented fungi and fungal pigments for broad applications.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".