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Nanoparticles in medicine

2017· book· en· W2744132332 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

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldMedicine
TopicCancer Research and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsNanomedicineNanotechnologyNanoparticleNanomaterialsHuman healthComputer scienceMaterials scienceBiochemical engineeringMedicineEngineering

Abstract

fetched live from OpenAlex

This article examines the applications of nanoparticles in medicine. Nanomedicine is a promising field that can make available different nanosystems whose novel, usually size-dependent, physical, chemical and/or biological properties are exploited to combat the disease of interest. One kind of particulate systems represents a vast array of either metallic,semiconductor, polymeric, protein or lipid nanoparticles that can be exploited for diagnosis and treatment of various diseases. This article first provides an overview of general issues related to physicochemical and biological properties of different nanoparticles. It then considers the current problems associated with the use of nanoparticles in medicine and suggests some solutions. It also discusses the interaction of nanoparticles with cells and factors that determine these interactions and concludes with some examples of new approaches for real-time imaging of experimental animals that could be useful, complementary methods for evaluations of effectiveness (or toxicity) of novel nanomaterials andnanomedicines.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.486
Threshold uncertainty score0.738

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 head0.056
GPT teacher head0.295
Teacher spread0.238 · 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