An automated microfluidic system for screening <i>Caenorhabditis elegans</i> behaviors using electrotaxis
Bibliographic record
Abstract
Caenorhabditis elegans (C. elegans) is a widely used animal model to study mechanisms of biological processes and human diseases. To facilitate manipulations of C. elegans in the laboratory, researchers have developed various tools that permit careful monitoring of behavior and changes in cellular processes. Earlier, we had reported a novel microfluidic assay device to study the neuronal basis of movement and to investigate the effects of cellular and environmental factors that can induce degeneration in certain neurons leading to movement disorder. The system involved the use of an electric field to perform electrotaxis assays, which allows detailed examination of movement responses of animals. One of the potential uses of this system is to perform genetic and chemical screenings for neuroprotective factors; however, it could not be done due to manual operations and low throughput. In this paper, we present an integrated microfluidic system that automates screening of C. elegans behavioral response using electrotaxis. The core component of system is a multilayer poly dimethyl siloxane (PDMS) device, which enables C. elegans loading, capture, flush, release, electrotaxis, and clean sequentially with the help of other components. The system is capable of screening C. elegans, at a throughput of more than 20 worms per hour, automatically and continually without human intervention. To demonstrate the effectiveness of the system, C. elegans neuronal mutants were screened, and the phenotype data were extracted and analyzed. We envision that the automatic screening potential of the system will accelerate the study of neuroscience, drug discovery, and genetic screens in C. elegans.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".