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Record W2080655569 · doi:10.1021/nl404816m

Dual-Modal Magnetic Resonance and Fluorescence Imaging of Atherosclerotic Plaques in Vivo Using VCAM-1 Targeted Tobacco Mosaic Virus

2014· article· en· W2080655569 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNano Letters · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAtherosclerosis and Cardiovascular Diseases
Canadian institutionsWestern University
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood Institute
KeywordsIn vivoMagnetic resonance imagingNuclear magnetic resonanceTobacco mosaic virusFluorescenceMaterials scienceMedicineVirusVirologyRadiologyBiologyPhysicsOptics

Abstract

fetched live from OpenAlex

The underlying cause of major cardiovascular events, such as myocardial infarctions and strokes, is atherosclerosis. For accurate diagnosis of this inflammatory disease, molecular imaging is required. Toward this goal, we sought to develop a nanoparticle-based, high aspect ratio, molecularly targeted magnetic resonance (MR) imaging contrast agent. Specifically, we engineered the plant viral nanoparticle platform tobacco mosaic virus (TMV) to target vascular cell adhesion molecule (VCAM)-1, which is highly expressed on activated endothelial cells at atherosclerotic plaques. To achieve dual optical and MR imaging in an atherosclerotic ApoE(-/-) mouse model, TMV was modified to carry near-infrared dyes and chelated Gd ions. Our results indicate molecular targeting of atherosclerotic plaques. On the basis of the multivalency and multifunctionality, the targeted TMV-based MR probe increased the detection limit significantly; the injected dose of Gd ions could be further reduced 400x compared to the suggested clinical use, demonstrating the utility of targeted nanoparticle cargo delivery.

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.

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 score0.324
Threshold uncertainty score0.797

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 head0.201
Teacher spread0.193 · 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