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Record W4224255841 · doi:10.3390/pharmaceutics14040886

An Overview of Cell Membrane Perforation and Resealing Mechanisms for Localized Drug Delivery

2022· review· en· W4224255841 on OpenAlexafffund
Stephanie He, Davindra Singh, Brandon Helfield

Bibliographic record

VenuePharmaceutics · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsConcordia University
FundersConcordia UniversityNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsHeart and Stroke Foundation of CanadaBurroughs Wellcome Fund
KeywordsMembraneDrug deliveryCell biologyPerforationCytoskeletonCell membraneCellChemistryDrugBiophysicsNanotechnologyMedicinePharmacologyBiologyMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Localized and reversible plasma membrane disruption is a promising technique employed for the targeted deposition of exogenous therapeutic compounds for the treatment of disease. Indeed, the plasma membrane represents a significant barrier to successful delivery, and various physical methods using light, sound, and electrical energy have been developed to generate cell membrane perforations to circumvent this issue. To restore homeostasis and preserve viability, localized cellular repair mechanisms are subsequently triggered to initiate a rapid restoration of plasma membrane integrity. Here, we summarize the known emergency membrane repair responses, detailing the salient membrane sealing proteins as well as the underlying cytoskeletal remodeling that follows the physical induction of a localized plasma membrane pore, and we present an overview of potential modulation strategies that may improve targeted drug delivery approaches.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.004

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 head0.183
GPT teacher head0.430
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2022
Admission routes2
Has abstractyes

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