MétaCan
Menu
Back to cohort
Record W3082995009 · doi:10.1021/acsomega.0c02650

Advances in Medical Imaging: Aptamer- and Peptide-Targeted MRI and CT Contrast Agents

2020· review· en· W3082995009 on OpenAlex
Anna Koudrina, Maria C. DeRosa

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Omega · 2020
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMagnetic resonance imagingAptamerMedical imagingMolecular imagingMedicineContrast (vision)Computed tomographyMedical physicsRadiologyComputer scienceArtificial intelligenceBiology

Abstract

fetched live from OpenAlex

Computed tomography (CT) and magnetic resonance imaging (MRI) are among the most well-established modalities in the field of noninvasive medical imaging. Despite being powerful tools, both suffer from a number of limitations and often fall short when it comes to full delineation of pathological tissues. Since its conception, molecular imaging has been commonly utilized to further the understanding of disease progression, as well as monitor treatment efficacy. This has naturally led to the advancement of the field of targeted imaging. Targeted imaging research is currently dominated by ligand-modified contrast media for applications in MRI and CT imaging. Although a plethora of targeting ligands exist, a fine balance between their size and target binding efficiency must be considered. This review will focus on aptamer- and peptide-modified contrast agents, outlining selected formulations developed in recent years while highlighting the advantages offered by these targeting ligands.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.298
Teacher spread0.281 · 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