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Record W2033579280 · doi:10.1039/c3sm50837d

Electrostatically directed liposome adsorption, internalization and fusion on hydrogel microparticles

2013· article· en· W2033579280 on OpenAlexafffund
Youssef Helwa, Neeshma Dave, Juewen Liu

Bibliographic record

VenueSoft Matter · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsLiposomeSelf-healing hydrogelsCalceinLipid bilayerDrug deliveryLipid bilayer fusionBilayerBiophysicsChemistryAdsorptionMembraneNanotechnologyChemical engineeringMaterials scienceBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Supported lipid bilayers have found a diverse range of applications in understanding membrane biophysics, biosensor development, drug screening, and drug delivery. While silica has been the most frequently used supporting material, hydrogels might act as a superior alternative not only because of their soft nature allowing better interfacing with transmembrane proteins but also their porous interior for molecular containment. Unlike silica, where neutral and even same charged liposomes can readily fuse, electrostatic attraction is crucial for liposome fusion onto hydrogels. In addition to fusion, we systematically characterized other interactions including liposome adsorption onto and diffusion into hydrogels. The fused membrane forms a continuous bilayer for the most part, which is demonstrated by the observation that the diffusion of DNA is blocked but small dyes could still enter the gel. The kinetics of liposome–gel interaction is characterized using calcein loaded liposomes, where liposome rupture is observed only when the oppositely charged gel is added. With this work, a more complete picture about the interaction between liposomes and hydrogels is obtained.

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 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 score0.021
Threshold uncertainty score0.367

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)0.0000.000

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 head0.004
GPT teacher head0.212
Teacher spread0.208 · 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.

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

Citations26
Published2013
Admission routes2
Has abstractyes

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