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Record W2018391174 · doi:10.1039/c3ra46757k

Strong and specific interaction of ultra small superparamagnetic iron oxide nanoparticles and human activated platelets mediated by fucoidan coating

2013· article· en· W2018391174 on OpenAlexfundno aff
Laure Bachelet-Violette, Amanda Silva, Murielle Maire, Aude Michel, Ovidiu Brinza, Phalla Ou, Véronique Ollivier, Antonino Nicoletti, Claire Wilhelm, Didier Letourneur, Christine Ménager, Frédéric Chaubet

Bibliographic record

VenueRSC Advances · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsnot available
FundersRyerson University
KeywordsFucoidanNanoparticlePlateletSuperparamagnetismIron oxideIron oxide nanoparticlesCoatingPlatelet activationChemistryNanotechnologyBiophysicsMaterials scienceBiochemistryPolysaccharideOrganic chemistryMagnetic field

Abstract

fetched live from OpenAlex

Activated platelets play a pivotal role in cardiovascular diseases such as atherothrombosis. Therefore, strategies enabling activated platelet molecular imaging are of great interest. Herein, a chemical protocol was investigated for coating superparamagnetic iron oxide nanoparticles with low molecular weight fucoidan, a ligand of P-selectin expressed on the surface of activated platelets. The physico–chemical characterization of the obtained product demonstrated successful fucoidan coating and its potential as a T2 MRI contrast agent. The specificity and the strength of the interaction between fucoidan-coated iron oxide nanoparticles and human activated platelets was demonstrated by flow cytometry. Micromagnetophoresis experiments revealed that platelets experience magnetically-induced motion in the presence of a magnetic field gradient created by a micromagnet. Altogether, these results indicate that superparamagnetic iron oxide nanoparticles coated with low molecular weight fucoidan may represent a promising molecular imaging tool for activated platelets in human diseases.

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 score0.002
Threshold uncertainty score0.672

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 communication0.0000.001
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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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