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Record W2067351990 · doi:10.2217/nnm.14.110

Designing A Better Theranostic Nanocarrier for Cancer Applications

2014· review· en· W2067351990 on OpenAlexafffund
Trinh Nguyen, Amy Tekrony, Kristin Yaehne, David T. Cramb

Bibliographic record

VenueNanomedicine · 2014
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsNanocarriersLiposomeChorioallantoic membraneNanotechnologyFenestrationControlled releaseDrug carrierDrug deliveryChemistryPharmacologyMaterials scienceCancer researchMedicineAngiogenesis

Abstract

fetched live from OpenAlex

Nanocarriers show incredible potential in theranostic applications as they offer diagnostic capabilities along with the ability to encapsulate and protect drugs from degradation, be functionalized with targeting moieties and be designed with controlled release mechanisms. Most clinically approved nanocarrier drugs are liposomal formulations. As such, considerable research has been directed towards designing liposomal carriers that can release their payloads via exogenous or endogenous triggers. For triggered release to effectively increase drug bioavailability, nanocarriers must first accumulate at the tumor site via the enhanced retention and permeability effect. It has been demonstrated in the chicken embryo chorioallantoic membrane and murine xenografted models that nanoparticle surface charge and geometry, with respect to vascular endothelium fenestration size, drive this accumulation in angiogenic tissue.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.037
GPT teacher head0.333
Teacher spread0.297 · 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

Citations15
Published2014
Admission routes2
Has abstractyes

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