Process-induced cell damage: pneumatic versus screw-driven bioprinting
Bibliographic record
Abstract
During the bioprinting processes that employ either pneumatic or screw-driven mechanisms, living cells are subject to process-induced forces, which may cause cell injury or damage. However, the similarities and differences between these two mechanisms have not been discovered and documented in terms of process-induced forces and cell damage. In this paper, we examined the process-induced forces, including hydrostatic pressure, shear stress, extensional stress, and tensile/compressive forces that the cells experienced during the bioprinting processes by means of these two mechanisms; we also experimentally investigated the process-induced cell damage (featured by the rupture of the cell membrane) under various printing conditions or factors, including the volumetric flow rates, cell types, bioink solutions, needle types and sizes, and printing head-movement speeds. On this basis, we correlated the percent of cell damage to the process-induced forces, which were considered mainly responsible for the rupture of the cell membrane. Our results illustrate that compared to the pneumatic bioprinting process, the screw-driven bioprinting process generally induces more cell damage, varying with the printing conditions. This study, for the first time, discovers the similarities and differences between the pneumatic and screw-driven bioprinting processes and further demonstrates their merits and demerits for bioprinting in terms of printing-process control, process-induced forces, and cell damage.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".