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Record W4324330079 · doi:10.1155/2023/4131117

Magnetic Particle Imaging in Vascular Imaging, Immunotherapy, Cell Tracking, and Noninvasive Diagnosis

2023· article· en· W4324330079 on OpenAlexfundno aff
Prashant Chandrasekharan, Renesmee Kuo, K. L. Barry Fung, Chinmoy Saayujya, Jacob Bryan, Mariam Yousuf, Benjamin Fellows, Caylin Colson, Quincy Huynh, Owen Doyle, Allison Hartley, Khadija Yousuf, Patrick Goodwill, Steven Conolly

Bibliographic record

VenueMolecular Imaging · 2023
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSiebel Scholars FoundationCancer Research Coordinating CommitteeUCBTobacco-Related Disease Research ProgramNational Institutes of HealthNational Science Foundation
KeywordsMagnetic particle imagingIron oxide nanoparticlesMolecular imagingParticle (ecology)Magnetic particle inspectionIron oxideMagnetic nanoparticlesTracking (education)NanotechnologyMaterials scienceBiomedical engineeringNanoparticleMedicineBiology

Abstract

fetched live from OpenAlex

Magnetic particle imaging (MPI) is a new tracer-based imaging modality that is useful in diagnosing various pathophysiology related to the vascular system and for sensitive tracking of cytotherapies. MPI uses nonradioactive and easily assimilated nanometer-sized iron oxide particles as tracers. MPI images the nonlinear Langevin behavior of the iron oxide particles and has allowed for the sensitive detection of iron oxide-labeled therapeutic cells in the body. This review will provide an overview of MPI technology, the tracer, and its use in vascular imaging and cytotherapies using molecular targets.

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 categoriesMeta-epidemiology (narrow)
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 score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.200
Teacher spread0.195 · 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.

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

Citations24
Published2023
Admission routes1
Has abstractyes

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