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Functionalized Carbon Nanoparticles as Theranostic Agents and Their Future Clinical Utility in Oncology

2023· review· en· W4316038996 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioengineering · 2023
Typereview
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsMcGill UniversityJewish General Hospital
FundersFondation de l'Hôpital général juifJewish General Hospital
KeywordsNanotechnologyNanoparticleCancerNanomedicineMaterials scienceMedicineInternal medicine

Abstract

fetched live from OpenAlex

Over the years, research of nanoparticle applications in pre-clinical and clinical applications has greatly advanced our therapeutic and imaging approaches to many diseases, most notably neoplastic disorders. In particular, the innate properties of inorganic nanomaterials, such as gold and iron oxide, as well as carbon-based nanoparticles, have provided the greatest opportunities in cancer theranostics. Carbon nanoparticles can be used as carriers of biological agents to enhance the therapeutic index at a tumor site. Alternatively, they can also be combined with external stimuli, such as light, to induce irreversible physical damaging effects on cells. In this review, the recent advances in carbon nanoparticles and their use in cancer theranostics will be discussed. In addition, the set of evaluations that will be required during their transition from laboratory investigations toward clinical trials will be addressed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.083
GPT teacher head0.347
Teacher spread0.264 · 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