Designing microfluidic devices for behavioral screening of multiple zebrafish larvae
Bibliographic record
Abstract
BACKGROUND: Microfluidic devices are being used for phenotypic screening of zebrafish larvae in fundamental and pre-clinical research. A challenge for the broad use of these microfluidic devices is their low throughput, especially in behavioral assays. Previously, we introduced the tail locomotion of a semi-mobile zebrafish larva evoked on-demand with electric signal in a microfluidic device. Here, we report the lessons learned for increasing the number of specimens from one to four larvae in this device. METHODS AND RESULTS: Multiple parameters including loading and testing time per fish and loading and orientation efficiencies were refined to optimize the performance of modified designs. Flow and electric field simulations within the final device provided insight into the flow behavior and functionality of traps when compared to previous single-larva devices. Outcomes led to a new design which decreased the testing time per larva by ≈60%. Further, loading and orientation efficiencies increased by more than 80%. Critical behavioral parameters such as response duration and tail beat frequency were similar in both single and quadruple-fish devices. CONCLUSION: The developed microfluidic device has significant advantages for greater throughput and efficiency when behavioral phenotyping is required in various applications, including chemical testing in toxicology and gene screening.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".