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Record W2097457582 · doi:10.1517/17425247.4.6.621

Advances and challenges of nanotechnology-based drug delivery systems

2007· review· en· W2097457582 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

VenueExpert Opinion on Drug Delivery · 2007
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiodistributionDrug deliveryNanotechnologyDrugTargeted drug deliverySmall moleculePayload (computing)Computer sciencePharmacologyMaterials scienceMedicineChemistryBiologyIn vivo

Abstract

fetched live from OpenAlex

The ability to deliver highly efficient therapeutic compounds specifically to diseased sites is crucial for effectively treating all human illnesses. Unfortunately, conventional therapeutic strategies require unnecessarily high systemic administration due to non-specific biodistribution and rapid metabolism of free drug molecules prior to reaching their targeted sites. Using the tools of nanotechnology, drug delivery systems within the nanometer size regime can be developed to alter both pharmacological and therapeutic effects of drug molecules. Due to their small size, these novel DDS offer superior advantages, such as altered pharmacokinetic behaviour and improved payload, over traditional large-scale systems. In addition, the relative ease in modifying their surface chemistry permits the attachment of targeting and therapeutic molecules for specific therapeutic applications. Finally, complex nanostructures can be assembled using different building blocks with multiple functionalities ranging from targeting, detecting, imaging and therapeutic capabilities.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.055
GPT teacher head0.317
Teacher spread0.262 · 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