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Record W2781647603 · doi:10.1021/acs.langmuir.7b03189

Engineering Asymmetric Lipid Vesicles: Accurate and Convenient Control of the Outer Leaflet Lipid Composition

2018· article· en· W2781647603 on OpenAlexafffund
Marie Markones, Carina Drechsler, Michael Kaiser, Louma Kalie, Heiko Heerklotz, Sebastian Fiedler

Bibliographic record

VenueLangmuir · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsMembraneLipid bilayerChemistryLiposomeBiophysicsVesicleMembrane lipidsMembrane fluidityBilayerBiochemistryBiology

Abstract

fetched live from OpenAlex

The asymmetric distribution of lipids between the two bilayer leaflets represents a typical feature of biological membranes. The loss of this asymmetry, for example the exposure of negatively charged lipids on the extracellular membrane leaflet of mammalian cells, is involved in apoptosis and occurs in tumor cells. Thus, the controlled production of asymmetric liposomes helps to better understand such crucial cellular processes. Here, we present an approach that allows us to design asymmetric model-membrane experiments on a rational basis and predict the fraction of exchanged lipid. In addition, we developed a label-free and nondestructive assay to quantify the asymmetric uptake of negatively charged lipids in terms of the zeta potential. This significantly enhances the applicability, impact, and predictive power of model membranes.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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
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

Citations51
Published2018
Admission routes2
Has abstractyes

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