Impact of Pharmaceutical Compounds in the Bioremediation of Municipal Biosolids by the White-Rot-Fungi Trametes hirsuta
Bibliographic record
Abstract
The potential of microorganisms for the treatment of municipal biosolids is continuously growing. The present studies evaluated the potency of Trametes hirsuta for the reduction in biosolid mass, production of extracellular enzymes, and removal of pharmaceutical compounds (PhACs) in biosolid slurry in the presence and absence of spiked PhACs [5 non-steroidal anti-inflammatories (NSAIs) and 2 psychoactive compounds (PACs)]. Toxicity after 35 days of fungal treatment was also assessed. Results showed that the growth of T. hirsuta is limited above 25% and wholly inhibited above 50% of biosolids in the slurry. At 12% of biosolid concentration, biosolid mass was reduced by 90%, NSAIs were entirely removed, but PACs' removal was only ~20%. Increasing biosolid content to 25% did not markedly affect biosolid reduction but significantly enhanced the removal of PACs (>50%). Results also showed that both PhACs and biosolids induced the production of oxidative enzymes. In 12% biosolids in the slurry, the oxidative potential measured by the ABTS assay (OABTS) reached 5,000 mM of OABTS in the presence of PhACs, and 2,500 mM of OABTS without PhACs, as compared to 1,200 mM of OABTS in control culture. Finally, we report that white rot fungi (WRF) treatment significantly decreased the toxicity of the biosolids.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".