Quantifying the shear stress necessary to cause temporary cell permeabilization with an impinging inert gas jet
Bibliographic record
Abstract
Studying, diagnosing, and treating diseases are all becoming reliant on our ability to introduce drugs and genes into cells. Using an impinging inert gas jet promises to avoid the severe health risks associated with viral and chemical delivery, while also maintaining a low cost. This approach relies on applying a shear stress on the cellular membrane that will temporarily disrupt the membrane, allowing for macromolecules to passively diffuse into the cell. Work done at McGill University identified a range of gas dynamic pressures that cause temporary cell permeabilization for the specific setup used. This setup has been replicated to quantify the levels of shear acting on the membrane at the identified conditions. Computational fluid dynamics and an experimental technique known as photochromic molecular flow visualization were used to measure shear. The shear rate and spatial shear gradient were calculated for capillary diameters of 0.5mm, 0.68mm and 0.86mm at flowrates corresponding to the limits of temporary pore formation previously discovered. While a minimum shear rate is likely required for permeabilization to occur, the results indicate that the previously observed pattern of cell permeabilization is better matched by the shear gradient. These findings pave the way for the development of precise physical delivery methods.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".