Reversible permeabilization using high‐intensity femtosecond laser pulses: Applications to biopreservation
Bibliographic record
Abstract
Non-invasive manipulation of live cells is important for cell-based therapeutics. Herein we report on the uniqueness of using high-intensity femtosecond laser pulses for reversibly permeabilizing mammalian cells for biopreservation applications. When mammalian cells were suspended in a impermeable hyperosmotic cryoprotectant sucrose solution, femtosecond laser pulses were used to transiently permeabilize cells for cytoplasmic solute uptake. The kinetics of cells exposed to 0.2, 0.3, 0.4, and 0.5 M sucrose, following permeabilization, were measured using video microscopy, and post-permeabilization survival was determined by a dual fluorescence membrane integrity assay. Using appropriate laser parameters, we observed the highest cell survival for 0.2 M sucrose solution (>90%), with a progressive decline in cell survival towards higher concentrations. Using diffusion equations describing the transport of solutes, the intracellular osmolarity at the inner surface of the membrane (x = 10 nm) and to a diffusive length of x = 10 microm was estimated, and a high loading efficiency (>98% for x = 10 nm and >70% for x = 10 microm) was calculated for cells suspended in 0.2 M sucrose. This is the first report of using femtosecond laser pulses for permeabilizing cells in the presence of cryoprotectants for biopreservation applications.
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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 | 0.001 | 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".