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Record W7042860462

Quantifying the shear stress necessary to cause temporary cell permeabilization with an impinging inert gas jet

2013· dissertation· en· W7042860462 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2013
Typedissertation
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsShear (geology)Shear rateInertMicrofluidicsShear stressJet (fluid)Inert gasMembraneCapillary action
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.025
GPT teacher head0.260
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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