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Record W2278748511 · doi:10.1126/science.aad4925

DNA-controlled dynamic colloidal nanoparticle systems for mediating cellular interaction

2016· article· en· W2278748511 on OpenAlexafffund
Seiichi Ohta, Dylan Glancy, Warren C. W. Chan

Bibliographic record

VenueScience · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of TorontoJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyProstate Cancer Canada
KeywordsNanoparticleNanotechnologyBiophysicsDNAFluorophoreChemistryFunction (biology)ColloidBiomoleculeDNA origamiMaterials scienceBiological systemCell biologyBiologyPhysicsNanostructureBiochemistryFluorescence

Abstract

fetched live from OpenAlex

Precise control of biosystems requires development of materials that can dynamically change physicochemical properties. Inspired by the ability of proteins to alter their conformation to mediate function, we explored the use of DNA as molecular keys to assemble and transform colloidal nanoparticle systems. The systems consist of a core nanoparticle surrounded by small satellites, the conformation of which can be transformed in response to DNA via a toe-hold displacement mechanism. The conformational changes can alter the optical properties and biological interactions of the assembled nanosystem. Photoluminescent signal is altered by changes in fluorophore-modified particle distance, whereas cellular targeting efficiency is increased 2.5 times by changing the surface display of targeting ligands. These concepts provide strategies for engineering dynamic nanotechnology systems for navigating complex biological environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.280
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), 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

Citations203
Published2016
Admission routes2
Has abstractyes

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