Microinjection in a microfluidic format using flexible and compliant channels and electroosmotic dosage control
Bibliographic record
Abstract
We present a novel PDMS-based microinjection system in a microfluidic format with precise electroosmotic dosage control. The device architecture is fully scalable and enables high-throughput microinjections with integrated pre- and post-processing operations. The injection mechanism greatly simplifies current methods as only a single degree of freedom is required for injections. The injections are performed inside a fully enclosed channel by an integrated microneedle. Actuation of the needle is achieved by the compliant deformation of the channel structure by an external actuator. Reagent transport is achieved using electroosmotic flow (EOF) which provides non-pulsating flow and precise electrical dosage control. The potentials used for injections were between 5 V-25 V. The electrical properties and flow rates for the device were characterized for Zebrafish embryos and Rhodamine B and Methylene blue in pH 10 buffer solution. We also propose a method to enable precise individual dosing of embryos using direct electrical feedback. Additionally, we show that electrical feedback can be used to verify the location of the needle inside the injection target. A preliminary viability study of our device was conducted using Zebrafish (Danio rerio) embryos. The study involved the injection of ultrapure water into the embryos in an E3 buffer, and resulted in embryos that showed normal development at 48 hours.
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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 | 0.000 |
| 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 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".