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Nuclear Targeting of Gold Nanoparticles for Improved Therapeutics

2015· review· en· W2402298570 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

VenueCurrent Topics in Medicinal Chemistry · 2015
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNanomedicineColloidal goldCancer therapyNanotechnologyCancerCancer researchDNA damageCytoplasmRadiation therapyNucleusCancer cellChemistryNanoparticleComputational biologyMedicineDNABiologyCell biologyMaterials scienceBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

Nanomedicine is an exponentially growing field, and gold nanoparticles (GNPs) in particular are extensively used in research due to their abilities as anti-cancer drug carriers for chemotherapy and as dose enhancers in radiotherapy. Most GNP research in the past involved a system where GNP localization was in the cytoplasm of the cell. However, it is predicted that therapy response can be enhanced if GNPs can be effectively targeted into the nucleus. With nuclear targeting, there is a possibility in producing additional free radicals in response to irradiation within the nucleus. This can cause more damage to the DNA of cancer cells. In this review article, we discuss the successful NP-based platforms available for nuclear targeting. In addition, we also present the possible mechanisms of nuclear targeting in detail followed by its applications in cancer 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)0.001
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 score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.078
GPT teacher head0.358
Teacher spread0.280 · 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