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Record W2535522231 · doi:10.1109/mnano.2016.2606685

Dynamically Tunable Smart Nanodrug Perspectives: Promises and challenges of nanoparticle-based drug delivery

2016· article· en· W2535522231 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

VenueIEEE Nanotechnology Magazine · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDrug deliveryDrugNanotechnologyBiodistributionTargeted drug deliveryPharmacologyRisk analysis (engineering)Computer scienceMaterials scienceMedicineIn vivoBiotechnology

Abstract

fetched live from OpenAlex

In the field of medicine, nanotechnology has garnered much attention, as it promises to address a number of issues related to conventional drug delivery techniques. A conventional application of drugs is characterized by limited effectiveness, poor biodistribution, and a lack of selectivity [1]. These limitations and drawbacks can be overcome by controlled drug delivery. In controlled drug delivery systems (DDSs), the drug is transported to the place of action, and, therefore, its influence on vital tissues and undesirable side effects can be minimized. In addition, a DDS protects the drug from rapid degradation or clearance and enhances drug concentration in target tissues, which reduces the frequency of the dosages taken by the patient and lowers the drug side-effects [1]. This modern form of therapy is especially important when it is necessary to find a fine balance between the concentration of a drug and its toxic effects [2].

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.217
Teacher spread0.205 · 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