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Record W4286854157 · doi:10.3791/50612-v

Permeabilization of Adhered Cells Using an Inert Gas Jet

2013· article· en· W4286854157 on OpenAlexaff
Scott Cooper, Paul Jonak, Guillaume Chouinard-Pelletier, Sylvain Coulombe, Elizabeth A. V. Jones, Richard L. Leask

Bibliographic record

VenueJournal of Visualized Experiments · 2013
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsMontreal Heart InstituteMcGill University
Fundersnot available
KeywordsBiophysicsBiomoleculeInertHeLaNanotechnologyInert gasJet (fluid)MembraneCell biologyIn vitroMaterials scienceChemistryBiologyBiochemistryPhysics

Abstract

fetched live from OpenAlex

Various cell transfection techniques exist and these can be broken down to three broad categories: viral, chemical and mechanical. This protocol describes a mechanical method to temporally permeabilize adherent cells using an inert gas jet that can facilitate the transfer of normally non-permeable macromolecules into cells. We believe this technique works by imparting shear forces on the plasma membrane of adherent cells, resulting in the temporary formation of micropores. Once these pores are created, the cells are then permeable to genetic material and other biomolecules. The mechanical forces involved do run the risk of permanently damaging or detaching cells from their substrate. There is, therefore, a narrow range of inert gas dynamics where the technique is effective. An inert gas jet has proven efficient at permeabilizing various adherent cell lines including HeLa, HEK293 and human abdominal aortic endothelial cells. This protocol is appropriate for the permeabilization of adherent cells both in vitro and, as we have demonstrated, in vivo, showing it may be used for research and potentially in future clinical applications. It also has the advantage of permeabilizing cells in a spatially restrictive manner, which could prove to be a valuable research tool.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.066
GPT teacher head0.430
Teacher spread0.364 · 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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