Phenotypic chemical and mutant screening of zebrafish larvae using an on-demand response to electric stimulation
Bibliographic record
Abstract
Behavioral responses of zebrafish larvae to environmental cues are important functional readouts that should be evoked on-demand and studied phenotypically in behavioral, genetical and developmental investigations. Very recently, it was shown that zebrafish larvae execute a voluntary and oriented movement toward the positive electrode of an electric field along a microchannel. Phenotypic characterization of this response was not feasible due to larva's rapid movement along the channel. To overcome this challenge, a microfluidic device was introduced to partially immobilize the larva's head while leaving its mid-body and tail unrestrained in a chamber to image motor behaviors in response to electric stimulation, hence achieving quantitative phenotyping of the electrically evoked movement in zebrafish larvae. The effect of electric current on the tail-beat frequency and response duration of 5-7 days postfertilization zebrafish larvae was studied. Investigations were also performed on zebrafish exposed to neurotoxin 6-hydroxydopamine and larvae carrying a pannexin1a (panx1a) gene knockout, as a proof of principle applications to demonstrate on-demand movement behavior screening in chemical and mutant assays. We demonstrated for the first time that 6-hydroxydopamine leads to electric response impairment, levodopa treatment rescues the response and panx1a is involved in the electrically evoked movement of zebrafish larvae. We envision that our technique is broadly applicable as a screening tool to quantitatively examine zebrafish larvae's movements in response to physical and chemical stimulations in investigations of Parkinson's and other neurodegenerative diseases, and as a tool to combine recent advances in genome engineering of model organisms to uncover the biology of electric response.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".