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Record W2519133187 · doi:10.1021/acs.langmuir.6b02945

Model Lung Surfactant Films: Why Composition Matters

2016· article· en· W2519133187 on OpenAlexafffund
Sahana L. Selladurai, Renaud Miclette Lamarche, Rolf Schmidt, Christine DeWolf

Bibliographic record

VenueLangmuir · 2016
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsConcordia University
FundersArgonne National LaboratoryNatural Sciences and Engineering Research Council of CanadaOffice of ScienceCanada Foundation for InnovationU.S. Department of Energy
KeywordsPulmonary surfactantBrewster's angleMonolayerChemistryViscoelasticityRheologyCounterionChemical engineeringPhase (matter)MembraneThermodynamicsCrystallographyMaterials scienceOrganic chemistryComposite materialIonOptics

Abstract

fetched live from OpenAlex

Lung surfactant replacement therapies, Survanta and Infasurf, and two lipid-only systems both containing saturated and unsaturated phospholipids and one containing additional palmitic acid were used to study the impact of buffered saline on the surface activity, morphology, rheology, and structure of Langmuir monolayer model membranes. Isotherms and Brewster angle microscopy show that buffered saline subphases induce a film expansion, except when the cationic protein, SP-B, is present in sufficient quantities to already screen electrostatic repulsion, thus limiting the effect of changing pH and adding counterions. Grazing incidence X-ray diffraction results indicate an expansion not only of the liquid expanded phase but also an expansion of the lattice of the condensed phase. The film expansion corresponded in all cases with a significant reduction in the viscosity and elasticity of the films. The viscoelastic parameters are dominated by liquid expanded phase properties and do not appear to be dependent on the structure of the condensed phase domains in a phase separated film. The results highlight that the choice of subphase and film composition is important for meaningful interpretations of measurements using model systems.

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 score0.333
Threshold uncertainty score0.486

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 head0.019
GPT teacher head0.260
Teacher spread0.240 · 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

Citations17
Published2016
Admission routes2
Has abstractyes

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