High-Throughput Automated Injection of Individual Biological Cells
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Bibliographic record
Abstract
The ability of efficiently delivering soluable/insoluable drug compounds or biomolecules into individual biological cells and quantifying their cellular responses is important for genetics, proteomics, and drug discovery. This paper presents a fully automated system for zebrafish embryo injection, which overcomes the problems inherent in manual injection, such as human fatigue and large variations in success rates due to poor reproducibility. Based on ldquolooking-then-movingrdquo control, the microrobotic system performs injection at a speed of 15 zebrafish embryos (chorion unremoved) per minute. Besides a high injection speed that compares favorably with that of a highly proficient injection technician, a vacuum-based embryo holding device enables fast immobilization of a large number of zebrafish embryos, shortening the embryo patterning process from minutes to seconds. The recognition of embryo structures from image processing identifies a desired destination inside the embryo for material deposition, together with precise motion control resulting in a success rate of 100%. Carefully tuning suction pressure levels as well as injection and retraction speeds produced a high survival rate of 98%. The quantitative performance evaluation of the automated system was based on the continuous injection of 250 zebrafish embryos. The technologies can be extended to other biological injection applications such as the injection of mouse embryos, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Drosophila</i> embryos, and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C.</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">elegans</i> to enable high-throughput biological and pharmaceutical research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it