Elimination of Endocrine Disrupting Chemicals using White Rot Fungi and their Lignin Modifying Enzymes: A Review
Bibliographic record
Abstract
Abstract The ability of white rot fungi (WRF) and their lignin modifying enzymes (LMEs), i.e. laccase and lignin‐ and manganese‐dependent peroxidase, to treat endocrine disrupting chemicals (EDCs) is extensively reviewed in this paper. These chemicals cause adverse health effects by mimicking endogenous hormones in receiving organisms. The alkylphenolic EDCs nonylphenol, bisphenol A and triclosan, the phthalic acid esters dibutylphthalate, diethylphthalate and di‐(2‐ethylhexyl)phthalate, the natural estrogens estrone, 17β‐estradiol, estriol and 17α‐ethynylestradiol and the phytoestrogens genistein and β‐sitosterol have been shown to be eliminated by several fungi and LMEs. WRF have manifested a highly efficient removal of EDCs in aqueous media and soil matrices using both LME and non LME‐systems. The ligninolytic system of WRF could also be used for the elimination of several EDCs and the associated hormone‐mimicking activity. The transformation of EDCs by LMEs and WRF is supported by emerging knowledge on the physiology and biochemistry of these organisms and the biocatalytic properties of their enzymes. Due to field reaction conditions, which drastically differ from laboratory conditions, further efforts will have to be directed towards developing robust and reliable biotechnological processes for the treatment of EDC‐contaminated environmental matrices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".