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Record W1993148765 · doi:10.1021/la904335m

Effect of Cholesterol on Electrostatics in Lipid−Protein Films of a Pulmonary Surfactant

2010· article· en· W1993148765 on OpenAlexafffund
Éric Finot, Yuri Leonenko, Brad Moores, Lukas M. Eng, Matthias Amrein, Zoya Leonenko

Bibliographic record

VenueLangmuir · 2010
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsPulmonary surfactantElectrostaticsElectric fieldChemistryNanometreKelvin probe force microscopeBiophysicsSurface chargeStatic electricityCholesterolAtomic force microscopyChemical engineeringChemical physicsNanotechnologyAnalytical Chemistry (journal)Materials scienceChromatographyBiochemistryComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

We report the changes in the electrical properties of the lipid-protein film of pulmonary surfactant produced by excess cholesterol. Pulmonary surfactant (PS) is a complex lipid-protein mixture that forms a molecular film at the interface of the lung's epithelia. The defined molecular arrangement of the lipids and proteins of the surfactant film gives rise to the locally highly variable electrical surface potential of the interface, which becomes considerably altered in the presence of cholesterol. With frequency modulation Kelvin probe force microscopy (FM-KPFM) and force measurements, complemented by theoretical analysis, we showed that excess cholesterol significantly changes the electric field around a PS film because of the presence of nanometer-sized electrostatic domains and affects the electrostatic interaction of an AFM probe with a PS film. These changes in the local electrical field would greatly alter the interaction of the surfactant film with charged species and would immediately impact the manner in which inhaled (often charged) airborne nanoparticles and fibers might interact with the lung interface.

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 score0.018
Threshold uncertainty score0.248

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 head0.005
GPT teacher head0.260
Teacher spread0.255 · 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

Citations44
Published2010
Admission routes2
Has abstractyes

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