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Record W2083676022 · doi:10.1021/la062513a

Interfacial Organizations of Gel Phospholipid and Cholesterol in Bovine Lung Surfactant Films

2007· article· en· W2083676022 on OpenAlexaff
Kaushik Nag, Maurícia Fritzen-Garcia, Ravi Devraj, Amiya Kumar Panda

Bibliographic record

VenueLangmuir · 2007
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPulmonary surfactantPhospholipidChemical engineeringCholesterolChemistryChromatographyBiochemistryMembrane

Abstract

fetched live from OpenAlex

Pulmonary surfactants stabilize the lung by way of reducing surface tension at the air-lung interface of the alveolus. 31P NMR, thin-layer chromatography, and electrospray ionization mass spectroscopy of bovine lipid extract surfactant (BLES) confirmed dipalmitoylphosphatidylcholine (DPPC) to be the major phospholipid species, with significant amounts of palmitoyl-oleoylphosphatidylcholine, palmitoyl-myristoylphosphatidylcholine, and palmitoyl-oleoylphosphatidylglycerol. BLES and DPPC spread at the air-water interface were studied through surface pressure area, fluorescence, and Brewster angle microscopy measurements. Langmuir-Blodgett films of monomolecular films, deposited on mica, were characterized by atomic force microscopy. BLES films displayed shape, size, and vertical height profiles distinct from those of DPPC alone. Calcium ions in the subphase altered BLES film domain structure. The addition of cholesterol (4 mol %) resulted in the destabilization of compressed BLES films at higher surface pressures (>40 mN m-1) and the formation of multilayered structures, apparently consisting of stacked monolayers. The studies suggested potential roles for individual surfactant lipid components in supramolecular arrangements, which could be the contributing factors in pulmonary surfactant to attain low surface tension at the air-water 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.228

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 head0.007
GPT teacher head0.262
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 designObservational
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

Citations21
Published2007
Admission routes1
Has abstractyes

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