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Record W4409222012 · doi:10.1002/ange.202503678

A Single‐Molecule Liposome Assay for Membrane Permeabilization

2025· article· en· W4409222012 on OpenAlexaff
Krzysztof M. Bąk, Dylan B. George, Bhanu Singh, Ryan Ferguson, Tianxiao Zhao, Kristin Piché, Ariel Louwrier, Scott L. Cockroft, Mathew H. Horrocks

Bibliographic record

VenueAngewandte Chemie · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsAptose Biosciences (Canada)
FundersUK Dementia Research InstituteLeverhulme Trust
KeywordsLiposomeChemistryMembraneMoleculeBiophysicsCombinatorial chemistryBiochemistryOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Abstract Cell membrane disruption is associated with numerous diseases and underlies the activity of various antimicrobial agents. The rapid screening of compounds capable of disrupting or permeabilizing biological membranes is essential to the search for new therapeutic drugs. Here, we present a single‐molecule confocal microscopy assay integrated with fast‐flow microfluidics to study membrane permeabilization in large unilamellar vesicles (LUVs) containing as few as seven dye molecules. This assay eliminates the need for liposome immobilization and achieves detection rates in the range of 1000 vesicles per minute, offering unparalleled sensitivity and detection limits as low as 135 pM, corresponding to just eight permeabilizing molecules per vesicle for active compounds such as ionomycin. It provides a robust platform for investigating membrane‐disrupting agents, including those with antimicrobial properties or implicated in neurodegenerative diseases.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations1
Published2025
Admission routes1
Has abstractyes

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