A microfluidic device for automated, high-speed microinjection of <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
The nematode worm Caenorhabditis elegans has been widely used as a model organism in biological studies because of its short and prolific life cycle, relatively simple body structure, significant genetic overlap with human, and facile/inexpensive cultivation. Microinjection, as an established and versatile tool for delivering liquid substances into cellular/organismal objects, plays an important role in C. elegans research. However, the conventional manual procedure of C. elegans microinjection is labor-intensive and time-consuming and thus hinders large-scale C. elegans studies involving microinjection of a large number of C. elegans on a daily basis. In this paper, we report a novel microfluidic device that enables, for the first time, fully automated, high-speed microinjection of C. elegans. The device is automatically regulated by on-chip pneumatic valves and allows rapid loading, immobilization, injection, and downstream sorting of single C. elegans. For demonstration, we performed microinjection experiments on 200 C. elegans worms and demonstrated an average injection speed of 6.6 worm/min (average worm handling time: 9.45 s/worm) and a success rate of 77.5% (post-sorting success rate: 100%), both much higher than the performance of manual operation (speed: 1 worm/4 min and success rate: 30%). We conducted typical viability tests on the injected C. elegans and confirmed that the automated injection system does not impose significant adverse effect on the physiological condition of the injected C. elegans. We believe that the developed microfluidic device holds great potential to become a useful tool for facilitating high-throughput, large-scale worm biology research.
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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 | 0.001 | 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".