Microfluidic system with integrated electroosmotic pumps, concentration gradient generator and fish cell line (RTgill-W1)—towards water toxicity testing
Bibliographic record
Abstract
This study presents a microfluidic system that incorporates electroosmotic pumps, a concentration gradient generator and a fish cell line (rainbow trout gill) to perform toxicity testing on fish cells seeded in the system. The system consists of three mechanical components: (1) a toxicity testing chip containing a microfluidic gradient generator which creates a linear concentration distribution of toxicant in a cell test chamber, (2) an electroosmotic (EO) pump chip that controls the flow rate and operation of the toxicity chip, and (3) indirect reservoirs that connect the two chips allowing for the toxicant solution to be pumped separately from the electroosmotic pump solution. The flow rate and stability of the EO pumps was measured and tested by monitoring the gradient generator using fluorescence microscopy. Furthermore, a lethality test was performed with this system setup using a rainbow trout gill cell line (RTgill-W1) as the test cells and sodium dodecyl sulfate as a model toxicant. A gradient of sodium dodecyl sulfate, from 0 to 50 microg mL(-1), was applied for 1 hr to the attached cells, and the results were quantified using a Live/Dead cell assay. This work is a preliminary study on the application of EO pumps in a living cell assay, with the potential to use the pumps in portable water quality testing devices with RTgill-W1 cells as the biosensors.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".