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Record W2033416927 · doi:10.1155/2010/491471

Development of Near Infrared‐Fluorescent Nanophosphors and Applications for Cancer Diagnosis and Therapy

2010· article· en· W2033416927 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 · 2010
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsInstitute for Biological Sciences
FundersNew Energy and Industrial Technology Development Organization
KeywordsMaterials scienceFluorescenceCancer therapyNanotechnologyInfraredCancerOpticsMedicine

Abstract

fetched live from OpenAlex

The use of near infrared (NIR) light for biomedical photonics in the wavelength region between 800 and 2000 nm, which is called “biological window”, has received particular attention since water and biological tissues have minimal optical loss due to scattering and absorption as well as autofluorescence in this region. Recent development of InGaAs CCD enables observations in this wavelength region. In the present paper, we report development of Yb and Er‐doped yttrium oxide nanoparticles (Y 2 O 3 :YbEr‐NP) which show strong NIR emission under NIR excitation (NIR‐NIR emission). We also demonstrate that NIR emission can be observed through swine colon wall. Based on these results, we propose a possible application of Y 2 O 3 :YbEr‐NP for cancer diagnosis and therapy using NIR‐NIR imaging system. Our results also suggest potential applications of Y 2 O 3 :YbEr‐NP for noninvasive detection of various 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.

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 score0.158
Threshold uncertainty score0.358

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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