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Record W1856119847 · doi:10.1089/ars.2014.5980

Redox-Responsive Drug Delivery

2014· review· en· W1856119847 on OpenAlexaff
Marc A. Gauthier

Bibliographic record

VenueAntioxidants and Redox Signaling · 2014
Typereview
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsRedoxDrugDrug deliveryNanotechnologyDrug discoverySmall moleculeDrug developmentChemistryPharmacologyBiologyBiochemistryMaterials science

Abstract

fetched live from OpenAlex

The objective of most modern drug delivery strategies is to maximize the effectiveness of drug molecules at diseased tissue and to minimize their effects in healthy ones. This is most often achieved using (bio-)synthetic carrier systems that release the drug at the target location. One emerging strategy to achieve this is to destabilize carriers and release therapeutics using natural redox gradients in the body or associated with disease. The body, however, is composed of numerous microenvironments whose redox homeostasis, as well as its dysregulation due to disease, is complex. The original article and authoritative reviews that constitute this Forum discuss some of the particular redox features associated with diseases and present an overview of how, chemically, redox-responsive drug delivery carriers can be designed to respond to these opportunities.

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 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.254
Teacher spread0.239 · 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 teacher head, not a consensus.

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

Citations27
Published2014
Admission routes1
Has abstractyes

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