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Depth of photothermal conversion of gold nanorods embedded in a tissue-like phantom

2009· article· en· W2004734404 on OpenAlexafffund
Candice L. Didychuk, Pinhas Ephrat, Astrid Chamson-Reig, Steven L. Jacques, Jeffrey J. L. Carson

Bibliographic record

VenueNanotechnology · 2009
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePhotothermal therapyImaging phantomNanorodFluenceLaserBiomedical engineeringNanosecondPulse (music)Photothermal effectOpticsOptoelectronicsNanotechnologyMedicine

Abstract

fetched live from OpenAlex

Gold nanorod (AuNR)-assisted photothermal therapy has emerged as a viable method for selective killing of cancer cells and shows promise for tumor destruction in vivo. This study examined the distribution of AuNR conversion expected to occur during photothermal therapy in vivo. Tissue-like phantoms were prepared with polyethylene glycol AuNRs distributed homogeneously at a concentration representative of a systemic injection. Phantoms were illuminated with a nanosecond pulsed laser (800 nm) at a variety of combinations of pulse energy (12-120 mJ) and pulse count (1-1000). Operating at the American National Standards Institute safety limit for human skin exposure (30 mJ cm(-2)), a diameter of 13 mm and a depth of 7.6 mm of AuNR conversion were observed in the gel phantoms after 1000 laser pulses (100 s exposure). Significant AuNR conversion was measured to a depth of 6 mm after only 100 pulses. Comparison of the measured AuNR conversion distribution with Monte Carlo simulation suggested that the fluence threshold for AuNR conversion estimated from phantom measurements was in the range of 20-43 mJ cm(-2). The results suggest that AuNR-assisted photothermal therapy will be effective for tumors within 10 mm of the illuminated tissue surface.

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 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 score0.010
Threshold uncertainty score0.318

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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations50
Published2009
Admission routes2
Has abstractyes

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