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Record W2034101835 · doi:10.1021/jp067636i

Fluorescent Probes Alter Miscibility Phase Boundaries in Ternary Vesicles

2007· article· en· W2034101835 on OpenAlexaff
Sarah L. Veatch, Sherry S.W. Leung, Robert E. W. Hancock, Jenifer Thewalt

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsMiscibilityVesicleMembraneFluorescencePhase (matter)Ternary operationPhosphatidylcholinePhase transitionMole fractionChemistryAnalytical Chemistry (journal)Phase boundaryMaterials scienceChromatographyOrganic chemistryThermodynamicsPhysical chemistryPolymerPhospholipidBiochemistry

Abstract

fetched live from OpenAlex

We use 2H NMR to study the effects of probes on the miscibility transition in multilamellar vesicles of di(18:1) phosphatidylcholine (PC; DOPC), chain perdeuterated di(16:0)PC (DPPCd62), and cholesterol both with and without 0.5 mol % of the fluorescent probes DiIC12 and DiOC18. Both probes raise the miscibility transition temperature in dispersions of 1:1 DOPC/DPPCd62 + 30% cholesterol but to differing extents. In membranes containing the popular probe DiIC12, the fraction of DPPCd62 lipids in the liquid disordered phase is increased, and the ordering of that phase is reduced even at low temperatures. All findings are consistent with a probe-induced expansion of the entire miscibility phase boundary. We examine membranes with smaller DiIC12 fractions and find a significant increase in transition temperature for samples with 0.05 mol % DiIC12, demonstrating that trace components can dramatically alter membrane phase behavior.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

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

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 head0.008
GPT teacher head0.289
Teacher spread0.280 · 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

Citations95
Published2007
Admission routes1
Has abstractyes

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