Microfluidic devices for embryonic and larval zebrafish studies
Bibliographic record
Abstract
Zebrafish or Danio rerio is an established model organism for studying the genetic, neuronal and behavioral bases of diseases and for toxicology and drug screening. The embryonic and larval stages of zebrafish have been used extensively in fundamental and applied research due to advantages offered such as body transparency, small size, low cost of cultivation and high genetic homology with humans. However, the manual experimental methods used for handling and investigating this organism are limited due to their low throughput, labor intensiveness and inaccuracy in delivering external stimuli to the zebrafish while quantifying various neuronal and behavioral responses. Microfluidic and lab-on-a-chip devices have emerged as ideal technologies to overcome these challenges. In this review paper, the current microfluidic approaches for investigation of behavior and neurobiology of zebrafish at embryonic and larval stages will be reviewed. Our focus will be to provide an overview of the microfluidic methods used to manipulate (deliver and orient), immobilize and expose or inject zebrafish embryos or larvae, followed by quantification of their responses in terms of neuron activities and movement. We will also provide our opinion in terms of the direction that the field of zebrafish microfluidics is heading toward in the area of biomedical engineering.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".