MétaCan
Menu
Back to cohort
Record W2285177445 · doi:10.1021/acsami.6b00461

Molecularly Imprinted Polymers with DNA Aptamer Fragments as Macromonomers

2016· article· en· W2285177445 on OpenAlexafffund
Zijie Zhang, Juewen Liu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAptamerMolecularly imprinted polymerIsothermal titration calorimetryCombinatorial chemistryDNADissociation constantMolecular imprintingMolecular recognitionMonomerPolymerMoleculeMaterials scienceChemistryBiophysicsOrganic chemistryBiochemistrySelectivityMolecular biologyBiology

Abstract

fetched live from OpenAlex

Molecularly imprinted polymers (MIPs) are produced in the presence of a template molecule. After removing the template, the cavity can selectively rebind the template. MIPs are attractive functional materials with a low cost and high stability, but traditional MIPs often suffer from low binding affinity. This study employs DNA aptamer fragments as macromonomers to improve MIPs. The DNA aptamer for adenosine was first split into two halves, fluorescently labeled, and copolymerized into MIPs. With a fluorescence quenching assay, the importance of imprinting was confirmed. Further studies were carried out using isothermal titration calorimetry (ITC). Compared to the mixture of the free aptamer fragments, their MIPs doubled the binding affinity. Each free aptamer fragment alone cannot bind adenosine, whereas MIPs containing each fragment are effective binders. We further shortened one of the aptamer fragments, and the DNA length was pushed to as short as six nucleotides, yielding MIPs with a dissociation constant of 27 μM adenosine. This study provides a new method for preparing functional MIP materials by combining high-affinity biopolymer fragments with low-cost synthetic monomers, allowing higher binding affinity and providing a method for signaling binding based on DNA chemistry.

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 score0.006
Threshold uncertainty score0.795

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 head0.005
GPT teacher head0.231
Teacher spread0.227 · 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

Citations84
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueACS Applied Materials & InterfacesSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207