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Record W1989499312 · doi:10.1166/jbn.2010.1114

Real-Time Imaging of Interactions Between Dipalmitoylphosphatidylcholine Monolayers and Gelatin Based Nanoparticles Using Brewster Angle Microscopy

2010· article· en· W1989499312 on OpenAlexafffund
Patrick Lai, Safia Nathoo, Tabitha Ku, Simardeep Gill, Shirzad Azarmi, Wilson Roa, Raimar Löbenberg, Elmar J. Prenner

Bibliographic record

VenueJournal of Biomedical Nanotechnology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsDipalmitoylphosphatidylcholineBrewster's angleMonolayerPulmonary surfactantNanomedicineNanoparticleGelatinDrug deliveryNanotechnologyMaterials scienceMicroscopyPhospholipidChemical engineeringChemistryOpticsOrganic chemistryMembranePhysics

Abstract

fetched live from OpenAlex

Given the current interest in the pulmonary route for targeted drug delivery, assessing the impact of drug delivery vehicles on the surfactant layer lining the surface of the lung alveoli is critical. As gelatin-based nanoparticles are one such vehicle, this study addresses their interaction with the major saturated phospholipid component of native lung surfactant, dipalmitoylphosphatidylcholine (DPPC). Nanoparticles are colloidal particles in the size range of 1 to 1000 nm that are presently investigated for site-specific drug delivery in the emerging field of nanomedicine. Monolayer studies of DPPC films were performed both in the presence and absence of nanoparticles in order to assess the interaction in terms of average molecular areas occupied at given surface pressures. In Brewster angle microscopy experiments, nanoparticles significantly changed the shape and reduced the size of DPPC domains suggesting a considerable interaction of the two systems. For safe pulmonary drug delivery, understanding this interaction is a prerequisite so nanoparticles can be a feasible alternative to more conventional therapies in the future.

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 score0.015
Threshold uncertainty score0.421

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 head0.281
Teacher spread0.273 · 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
Published2010
Admission routes2
Has abstractyes

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