Dynamic Cytotoxic Response to Microcystins Using Microelectronic Sensor Arrays
Bibliographic record
Abstract
Microcystins are bioactive metabolites produced by cyanobacteria in water. These cyclic heptapeptides have caused public health concern worldwide. By interfering with cellular phosphorylation and signaling, microcystins can cause acute and chronic liver diseases. Therefore, the World Health Organization (WHO) has set the provisional drinking water guideline value at 1.0 microg/L for microcystin-LR (free plus cell-bound). Microcystins do not readily cross cell membranes in in vitro cell-based assays, except for those using freshly isolated hepatocytes. However, the sensitivity of in vitro cell-based assays is not adequate for testing samples at low environmental concentrations. Hence, there is a need to develop a sensitive and stable cytotoxicity assay for use in environmental studies. On the basis of the observation that microcystin-LR can be transported by the liver-specific members of the organic anion transporting polypeptides (OATPs), we investigated the potential of using an OATP1B3-expressing cell line in a cytotoxicity assay for microcystins. Using a novel cell electronic sensing system (RT-CES), we were able to monitor the real-time, dynamic cytotoxic response to microcystins at microgram per liter concentrations. We demonstrated that the cytotoxicity of the most common microcystins, -LR, -YR, -RR, -LF, and -LW, was mediated by OATP1B3 transporters. Microcystin-LF is the most potent toxin among the five congeners. In conclusion, we have established a highly automated, real-time, sensitive, and stable assay for measuring microcystin cytoxicity.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".