Microfluidics for Investigation of Electric-Induced Behaviors of Zebrafish Larvae
Bibliographic record
Abstract
Zebrafish has emerged as a model organism for studying the genetic, neuronal and behavioral bases of diseases and for drug screening. Being a vertebrate, they are phylogenetically closer to humans than invertebrates, possess complex organs and the overall organization of their brain shows structural similarities with human. They are small at larval stages, optically transparent and easy to culture. In addition, zebrafish models of human diseases and genetic mutants are widely available. These characteristics make this vertebrate model an ideal organism for neurodegeneration study and drug screening from the molecule to whole organism level. Despite these attractive features, the conventional zebrafish screening methods used for movement-based behavioral tests are mostly time-consuming, uncontrollable, qualitative, low-throughput and inaccurate. Zebrafish larvae behavioral response to various stimulations including optical and chemical stimuli, have been already investigated. However, zebrafish sensory-motor responses to electrical signals, a controllable stimulus which its potential in inducing locomotion response was proven in research done before, have not been broadly studied. Examples of research questions remaining to be answered are if zebrafish electric induced response is sensitive to different electric current intensities, voltage drops, multiple electrical stimulation, and the electric field direction. The involvement of different pathways and genes in this response and its potential for utilization in disease studies and chemical screening, and drug discovery can also be investigated. This research aims to enhance our understanding of zebrafish electric-induced response via presenting novel microfluidic devices that address the challenges associated with monitoring the behavioral activities of zebrafish larvae in response to various electrical signals. In Objective 1 of the thesis, we designed a microfluidic device to deliver electrical stimuli to the awake and partially immobilized zebrafish larvae, screen and study their phenotypic behavioral responses and analyze the outputs. Behavioral response was characterized in terms of response duration and tail beat frequency. A multi-phenotypic microfluidic device was also developed to study the effect of electric stimulation on the heartrate. In Objective 2, attention was given to investigate the effect of electric current, voltage, and field direction on the zebrafish larvae’s response to find an optimized setting which can induce a traceable response in zebrafish. Using different habituation-dishabituation strategies, we also investigated if the zebrafish larvae show adaptation towards repeated exposures to electric stimuli. In Objective 3, we developed a quadruple-fish device to enhance the behavioral throughput of our microfluidic platform and showed the technique's effectiveness for larger sample size and faster behavioral assay. In Objective 4, our quadruple-fish device was employed to investigate the involvement of dopaminergic neurons in electric-induced movement response of zebrafish larvae. Lastly, since we could monitor the electric-induced behavioral responses of zebrafish larvae, in Objective 5, the applicability of our proposed technique in chemical toxicity and gene screening assays was investigated. \nThis study is expected to introduce a microfluidic platform for on-demand and phenotypic behavioral screening of zebrafish larvae with applications in chemical screening and drug discovery.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".