Engineered Fungal Biofilms in a Novel Biosensor for the Real-time Monitoring of Estrogenic Endocrine Disrupting Chemicals
Bibliographic record
Abstract
The presence of emerging contaminants such as endocrine-disrupting chemicals (EDCs) in water resources is a global concern, with reported persistent and rising concentrations in surface- and groundwaters. Endocrine-disrupting chemicals (EDCs) are known to be bioactive and interfere with the vertebrate endocrine system which regulates hormone synthesis, transport and degradation. Although analytical chemistry can detect for single chemical contaminants, it does not address the possible biological activity of the chemical cocktail of hazardous chemicals potentially present in environmental samples. Effect-based methods (EBMs) address this shortcoming by accounting for chemical-chemical interactions and assessing acute or chronic exposure risks to ecosystem and human health. Both analytical chemistry and EBM techniques, however, require an advanced skill level to conduct with other added financial costs. Deployable automated biosensors functioning as EBMs could provide a solution to EDC screening, bridging the resource and human capacity gaps. Within this study, we developed a fluorescent yeast estrogen screen (fYES) strain, Saccharomyces cerevisiae SC-ERCIT, to respond in a dose-dependent manner to the presence of estrogenic EDCs. A low-cost, deployable biosensor device was subsequently designed to allow for inline exposure to and monitoring of environmental samples. By exploiting the biofilm growth mode of this organism, the designed reactor sensor system was applied to detect the presence of estrogenic EDCs under continuous flow conditions, allowing real-time and online monitoring of water source quality. The sensitivity, specificity, response time and limits of detection (LoD) and quantification (LoQ) of the constructed yeast strain showed promise under static and continuous flow conditions for future in situ applications. This will include assessing not only the quality of groundwater directly as part of Managed Aquifer Recharge (MAR), but also the sources like treated wastewater that could possibly feed MAR resources.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".