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Record W4402047080 · doi:10.1002/smll.202404152

Membrane‐Nanoparticle Interactions: The Impact of Membrane Lipids

2024· review· en· W4402047080 on OpenAlexafffund
Travis Issler, Raymond J. Turner, Elmar J. Prenner

Bibliographic record

VenueSmall · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMembraneNanoparticleNanotechnologyMaterials scienceBiophysicsChemistryBiochemistryBiology

Abstract

fetched live from OpenAlex

The growing field of nanotechnology presents opportunity for applications across many sectors. Nanostructures, such as nanoparticles, hold distinct properties based on their size, shape, and chemical modifications that allow them to be utilized in both highly specific as well as broad capacities. As the classification of nanoparticles becomes more well-defined and the list of applications grows, it is imperative that their toxicity be investigated. One such cellular system that is of importance are cellular membranes (biomembranes). Membranes present one of the first points of contact for nanoparticles at the cellular level. This review will address current studies aimed at defining the biomolecular interactions of nanoparticles at the level of the cell membrane, with a specific focus of the interactions of nanoparticles with prominent lipid 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.040
GPT teacher head0.354
Teacher spread0.314 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes2
Has abstractyes

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