Pharmaceutical removal from wastewater by introducing cytochrome P450s into microalgae
Bibliographic record
Abstract
Pharmaceuticals are of increasing environmental concern as they emerge and accumulate in surface- and groundwater systems around the world, endangering the overall health of aquatic ecosystems. Municipal wastewater discharge is a significant vector for pharmaceuticals and their metabolites to enter surface waters as humans incompletely absorb prescription drugs and excrete up to 50% into wastewater, which are subsequently incompletely removed during wastewater treatment. Microalgae present a promising target for improving wastewater treatment due to their ability to remove some pollutants efficiently. However, their inherent metabolic pathways limit their capacity to degrade more recalcitrant organic compounds such as pharmaceuticals. The human liver employs enzymes to break down and absorb drugs, and these enzymes are extensively researched during drug development, meaning the cytochrome P450 enzymes responsible for metabolizing each approved drug are well studied. Thus, unlocking or increasing cytochrome P450 expression in endogenous wastewater microalgae could be a cost-effective strategy to reduce pharmaceutical loads in effluents. Here, we discuss the challenges and opportunities associated with introducing cytochrome P450 enzymes into microalgae. We anticipate that cytochrome P450-engineered microalgae can serve as a new drug removal method and a sustainable solution that can upgrade wastewater treatment facilities to function as "mega livers".
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".