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Record W3017214270 · doi:10.1039/d0bm00222d

Overview of the application of inorganic nanomaterials in cancer photothermal therapy

2020· review· en· W3017214270 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

VenueBiomaterials Science · 2020
Typereview
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsHealth Sciences Centre
FundersEuropean Regional Development FundFundação para a Ciência e a Tecnologia
KeywordsPhotothermal therapyCancer therapyNanomaterialsNanotechnologyCancerChemistryMaterials scienceMedicineInternal medicine

Abstract

fetched live from OpenAlex

Cancer photothermal therapy (PTT) has captured the attention of researchers worldwide due to its localized and trigger-activated therapeutic effect. In this field, nanomaterials capable of converting the energy of the irradiation light into heat have been showing promising results in several pre-clinical and clinical assays. Such a therapeutic modality takes advantage of the innate capacity of nanomaterials to accumulate in the tumor tissue and their capacity to interact with NIR laser irradiation to exert a therapeutic effect. Therefore, several nanostructures composed of different materials and organizations for mediating a photothermal effect have been developed. In this review, the most common inorganic nanomaterials, such as gold, carbon-based materials, tungsten, copper, molybdenum, and iron oxide, which have been explored for mediating a tumor-localized photothermal effect, are summarized. Moreover, the physicochemical parameters of nanoparticles that influence the PTT effectiveness are discussed and the recent clinical advances involving inorganic nanomaterial-mediated cancer photothermal therapy are also presented.

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: Review · Consensus signal: Review
Teacher disagreement score0.192
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.062
GPT teacher head0.331
Teacher spread0.270 · 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