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Record W2987188373 · doi:10.1002/adtp.201900144

Progress Toward Absorption, Distribution, Metabolism, Elimination, and Toxicity of DNA Nanostructures

2019· article· en· W2987188373 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Therapeutics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanotechnologyDNAModularity (biology)NanostructureComputational biologyIn vivoDrug deliveryChemistryBiologyBiophysicsMaterials scienceBiochemistryBiotechnologyGenetics

Abstract

fetched live from OpenAlex

Abstract DNA nanostructures are perfectly defined nanomaterials, and their shape/structure/surface chemistry (e.g., appended ligands) can be conveniently modulated by designing the sequence of their constituent DNA strands. No other natural or synthetic drug delivery system offers such predictability or modularity. As such, DNA nanostructures may provide exciting and potentially new opportunities for delivering drugs to diseased cell populations, or to specific sub‐cellular compartments. To date, however, most studies have been performed in cell culture and only recently has the field advanced to in vivo testing. Considering how rapidly the field is evolving, this Progress Report surveys available studies involving the testing of DNA nanostructures in vivo, in an effort to elucidate trends and provide guidelines for future developments. This contribution presents the current progress toward characterizing the absorption, distribution, metabolism, elimination, and toxicity of DNA nanostructures.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.028
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.268
Teacher spread0.260 · 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