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Record W2165311798 · doi:10.2217/17435889.1.1.115

Nanostructure-Mediated Thermal Therapy – The Path From Bench to Clinic

2006· letter· en· W2165311798 on OpenAlex
Tanya S. Hauck, Warren C. W. Chan

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

VenueNanomedicine · 2006
Typeletter
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsNanorodPhotothermal therapyNanostructureNanotechnologyMaterials scienceCancer therapyCancerMedicine

Abstract

fetched live from OpenAlex

Evaluation of: Huang X, El-Sayed IH, Qian W, El-Sayed MA: Cancer cell imaging and photothermal therapy in the near-infrared region by using gold nanorods. J. Amer. Chem. Soc. 128(6), 2115–2120 (2006) [1]. A recent paper, one of several recent studies on the use of nanostructures in thermal cancer therapy, is evaluated here. Functionalized gold nanorods have been used in vitro to selectively destroy cancer cells by converting near infrared radiation to heat. Nanostructures with tunable optical properties and biologically relevant size show great promise in highly selective thermal ablation therapy. Gold nanorods coated with targeting antibodies were demonstrated to be selective agents for the detection and destruction of cancer cells. We present the results of the El-Sayed paper, compare gold nanorods with other near infrared-absorptive nanostructures and discuss challenges of nanostructure-mediated thermal therapy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.359
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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