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Record W4324132554 · doi:10.1117/12.2649420

Use of an indocyanine green nano-emulsion for the treatment of cutaneous melanoma by photothermal therapy

2023· article· en· W4324132554 on OpenAlexaff
Letícia Palombo Martinelli, Gabriel Oliveira Jasinevicius, Lílian Tan Moriyama, Hilde Harb Buzzá, Juan Chen, Gang Zheng, Cristina Kurachi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhotothermal therapyIndocyanine greenMelanomaMedicineAnimal modelRadiation therapyDermatologySurgeryCancer researchMaterials scienceInternal medicineNanotechnology

Abstract

fetched live from OpenAlex

Melanoma is the most aggressive type of skin cancer with the highest mortality rate, with surgery being the standard treatment. In this study, the effect of indocyanine green nanoemulsion in an animal model was evaluated for the treatment of cutaneous melanoma, using photothermal therapy. Different irradiation protocols, and nanoICG intratumoral and systemic delivery ways were tested. Macroscopic and histological analyses and Kaplan-Meier curves for animal survival are presented for comparison of the different investigated protocols.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.028
GPT teacher head0.244
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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