Passive microinjection within high-throughput microfluidics for controlled actuation of droplets and cells
Bibliographic record
Abstract
Microinjection is an effective actuation technique used for precise delivery of molecules and cells into droplets or controlled delivery of genes, molecules, proteins, and viruses into single cells. Several microinjection techniques have been developed for actuating droplets and cells. However, they are still time-consuming, have shown limited success, and are not compatible with the needs of high-throughput (HT) serial microinjection. We present a new passive microinjection technique relying on pressure-driven fluid flow and pulsative flow patterns within an HT droplet microfluidic system to produce serial droplets and manage rapid and highly controlled microinjection into droplets. A microneedle is secured within the injection station to confine droplets during the microinjection. The confinement of droplets on the injection station prevents their movement or deformation during the injection process. Three-dimensional (3D) computational analysis is developed and validated to model the dynamics of multiphase flows during the emulsion generation. We investigate the influence of pulsative flows, microneedle parameters and synchronization on the efficacy of microinjection. Finally, the feasibility of implementing our microinjection model is examined experimentally. This technique can be used for tissue engineering, cells actuation and drug discovery as well as developing new strategies for drug delivery.
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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".