Assessing bioactivity of environmental water samples filtered using nanomembrane technology and mammalian cell lines
Bibliographic record
Abstract
This project reports on the use of a novel nanomembrane filtering technology to isolate and analyze the bioactivity of microplastic (MP)-containing debris from Lake Ontario water samples. Environmental MPs are a complex mixture of polymers and sorbed chemicals that are persistent and can exhibit a wide range of toxic effects. Since human exposure to MPs is unavoidable, it is necessary to characterize their bioactivity to assess potential health risks. This work seeks to quantify MP presence in the nearshore waters of Lake Ontario and begin to characterize the bioactivity of the filtrate containing MPs. We utilized silicon nitride (SiN) nanomembrane technology to isolate debris sized between 8 and 20 μm from lake water samples collected at various times and locations. MPs were identified with Nile red staining. Cell-based assays were conducted directly on the filtered debris to test for cell viability, aryl hydrocarbon receptor (AhR) activity, and interleukin 6 (IL-6) levels as a measure of proinflammatory response. All samples contained MPs. None of the isolated debris impacted cell viability. However, AhR activity and IL-6 levels varied over time. Additionally, no associations were observed between the amount of plastic and bioactivity. Observed differences in activity are likely due to variations in the physiochemical properties of debris between samples. Our results highlight the need for increased sampling to fully characterize the bioactivity of MPs in human cells and to elucidate the role that sample physiochemical and spatiotemporal properties play in this activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".