Drop-on-demand cell bioprinting via Laser Induced Side Transfer (LIST)
Bibliographic record
Abstract
We introduced and validated a drop-on-demand method to print cells. The method uses low energy nanosecond laser (wavelength: 532 nm) pulses to generate a transient microbubble at the distal end of a glass microcapillary supplied with bio-ink. Microbubble expansion results in the ejection of a cell-containing micro-jet perpendicular to the irradiation axis, a method we coined Laser Induced Side Transfer (LIST). We show that the size of the deposited bio-ink droplets can be adjusted between 165 and 325 µm by varying the laser energy. We studied the corresponding jet ejection dynamics and determined optimal conditions for satellite droplet-free bioprinting. We demonstrated droplet bio-printing up to a 30 Hz repetition rate, corresponding to the maximum repetition rate of the used laser. Jet ejection dynamics indicate that LIST can potentially reach 2.5 kHz. Finally, we show that LIST-printed human umbilical vein endothelial cells (HUVECs) present negligible loss of viability and maintain their abilities to migrate, proliferate and form intercellular junctions. Sample preparation is uncomplicated in LIST, while with further development bio-ink multiplexing can be attained. LIST could be widely adapted for applications requiring multiscale bioprinting capabilities, such as the development of 3D drug screening models and artificial tissues.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".