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Record W4232386059 · doi:10.1017/9781108526227.014

Electroporation Models in Engineered Artificial Membranes

2018· book-chapter· en· W4232386059 on OpenAlexaff
William Hoiles, Vikram Krishnamurthy, Bruce Cornell

Bibliographic record

VenueCambridge University Press eBooks · 2018
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectroporationMembraneIonBiophysicsMaterials scienceIon channelChemistryNanotechnologyBiochemistryBiology

Abstract

fetched live from OpenAlex

Introduction A lipid membrane that contains negligible defects and no ion channels will not allow ions to pass through the membrane. Indeed a membrane can be viewed as an electric capacitor. However, if an excitation is applied to the membrane this will cause the temporary breakdown of the membrane, causing water-filled pores to form that are large enough to allow the passage of ions and molecules through the membrane – this process is called electroporation. Electroporation is the process of aqueous pore formation in biological membranes when a voltage potential (e.g., ion gradient) is applied across the membrane. Several microbiology techniques are based on the use of electroporation to increase the permeability of the cell membrane to allow the passage of macromolecules, drugs, DNA, and antimicrobial peptides into the cell. These microbiology techniques are focused on how to design the excitation potential to allow the passage of these molecules through the membrane while ensuring the membrane is not irreversibly damaged. In this chapter we construct mesoscopic diffusion models of electroporation in engineered tethered membranes. The formulation in this chapter relies heavily on the Smoluchowski–Einstein equation, which is a probabilistic model for the number of aqueous pores formed. Additionally, we use statistical mechanics and electrodiffusive dynamic models to estimate lumped circuit parameters such as membrane conductance, membrane capacitance, and double-layer capacitance in the presence of electroporation. Recall that Chapters 4 and 6 dealt with the construction and experimental measurement methods for the electroporation measurement platform. Put simply, this chapter gives mathematical models and associated insight at the mesoscopic level to explain the experimental results of Chapter 6. Applications of Electroporation Electroporation has been used in a variety of in vitro and in vivo biotechnical applications for antitumor treatment, protein insertion, cell fusion, and gene and drug delivery. The process of electroporation results in the formation of pores in the membrane that allow molecules and ions to pass through the hydrophobic bilayer membrane. During electroporation the lipid molecules remain intact; they merely undergo a collective configurational change to form nanometer-sized aqueous pores. These aqueous pores allow ions and molecules to pass through the membrane. There are two types of electroporation: Reversible electroporation. Reversible electroporation occurs if, after the excitation potential is removed, the lipids can rearrange to form an impermeable membrane.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.228
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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