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Record W2135032572 · doi:10.1039/c5nr02283e

DNA topology influences molecular machine lifetime in human serum

2015· article· en· W2135032572 on OpenAlexaff
Sara Goltry, Natalya Hallstrom, Tyler Clark, Wan Kuang, Jeunghoon Lee, Cheryl L. Jorcyk, William B. Knowlton, Bernard Yurke, William L. Hughes, Elton Graugnard

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsToronto Metropolitan University
FundersNational Institutes of HealthNational Center for Research ResourcesNational Institute of General Medical SciencesBoise State UniversityW. M. Keck Foundation
KeywordsDNATopology (electrical circuits)Human bloodComputer scienceMaterials scienceNanotechnologyBiological systemComputational biologyEngineeringBiologyGeneticsElectrical engineeringPhysiology

Abstract

fetched live from OpenAlex

DNA nanotechnology holds the potential for enabling new tools for biomedical engineering, including diagnosis, prognosis, and therapeutics. However, applications for DNA devices are thought to be limited by rapid enzymatic degradation in serum and blood. Here, we demonstrate that a key aspect of DNA nanotechnology-programmable molecular shape-plays a substantial role in device lifetimes. These results establish the ability to operate synthetic DNA devices in the presence of endogenous enzymes and challenge the textbook view of near instantaneous degradation.

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 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 score0.014
Threshold uncertainty score0.523

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 head0.010
GPT teacher head0.291
Teacher spread0.280 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations48
Published2015
Admission routes1
Has abstractyes

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