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Record W2154199241 · doi:10.1155/2013/148578

Applications of Nanoparticles for MRI Cancer Diagnosis and Therapy

2013· article· en· W2154199241 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

VenueJournal of Nanomaterials · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsThunder Bay Regional Research InstituteUniversity of AlbertaUniversity of VictoriaUniversity of Calgary
Fundersnot available
KeywordsMaterials scienceCancer therapyCancerNanoparticleNanotechnologyMedical physicsNuclear magnetic resonanceMedicineInternal medicine

Abstract

fetched live from OpenAlex

Recent technological advances in nanotechnology, molecular biology, and imaging technology allow the application of nanomaterials for early and specific cancer detection and therapy. As early detection is a prerequisite for successful treatment, this area of research has been rapidly growing. This paper provides an overview of recent advances in production, functionalization, toxicity reduction, and application of nanoparticles to cancer diagnosis, treatment, and treatment monitoring. This review focuses on superparamagnetic nanoparticles used as targeted contrast agents in MRI, but it also describes nanoparticles applied as contrasts in CT and PET. A very recent development of core/shell nanoparticles that promises to provide positive contrast in MRI of cancer is provided. The authors concluded that despite unenviable obstacles, the progress in the area will lead to rapidly approaching applications of nanotechnology to medicine enabling patient‐specific diagnosis and treatment.

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)0.000
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 score0.007
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.021
GPT teacher head0.282
Teacher spread0.261 · 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