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Record W2139212129 · doi:10.1517/17425247.5.5.499

Recent developments in nanoparticle-based drug delivery and targeting systems with emphasis on protein-based nanoparticles

2008· review· en· W2139212129 on OpenAlexaff
Guilin Wang, Hasan Uludağ

Bibliographic record

VenueExpert Opinion on Drug Delivery · 2008
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDrug deliveryBiodistributionNanotechnologyNanoparticleDrugSystemic circulationMaterials scienceTargeted drug deliveryIn vivoNanomedicineDrug carrierPharmacologyMedicineBiotechnologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Drug delivery systems with nm dimensions (nanoparticles [NPs]) are attracting increasing attention because they can sequester drugs in systemic circulation, prevent non-specific biodistribution, and target to specific tissues. OBJECTIVE: We reviewed the recent literature pertinent to NP-based drug delivery, primarily emphasizing NPs fabricated from proteins. METHODS: A summary of common NP fabrication techniques is provided along with the range of sizes and functional properties obtained. The NP properties critical for injectable drug delivery are reviewed, as well as the attempts to design 'tissue-specific' NPs. RESULTS/CONCLUSIONS: It has been possible to design > 100 nm NPs from different biomaterials, and further understanding of in vivo stability and interactions with physiologic systems will lead to improved drug delivery systems.

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)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.114
GPT teacher head0.399
Teacher spread0.285 · 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

Citations121
Published2008
Admission routes1
Has abstractyes

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