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Record W2165299361 · doi:10.1517/17425247.3.3.311

Nanoparticle–aptamer bioconjugates for cancer targeting

2006· review· en· W2165299361 on OpenAlexaff
Omid C. Farokhzad, Jeffrey M. Karp, Róbert Langer

Bibliographic record

VenueExpert Opinion on Drug Delivery · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
FundersNational Institute of Biomedical Imaging and BioengineeringNational Cancer InstituteNational Institutes of Health
KeywordsAptamerNanotechnologyDrug deliveryTargeted drug deliveryCancer therapyDrug carrierSmall moleculeCancerChemistryMaterials scienceMedicineBiologyBiochemistryMolecular biology

Abstract

fetched live from OpenAlex

The combination of targeted drug delivery and controlled-release technology may pave the road for more effective yet safer chemotherapeutic options for cancer therapy. Drug-encapsulated polymeric nanoparticle-aptamer bioconjugates represent an emerging technology that can facilitate the delivery of chemotherapeutics to primary and metastatic tumours. Aptamers are short nucleic acid molecules with binding properties and biochemical characteristics that may make them suitable for use as targeting molecules. The goal of this review is to summarise the key components that are required for creating effective cancer targeting nanoparticle-aptamer bioconjugates. The field of controlled release and the structure and properties of aptamers, as well as the criteria for constructing effective conjugates, will be discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.005

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 head0.031
GPT teacher head0.359
Teacher spread0.327 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations251
Published2006
Admission routes1
Has abstractyes

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