MétaCan
Menu
Back to cohort
Record W1963502925 · doi:10.3390/polym6051631

Development of a Biocompatible Layer-by-Layer Film System Using Aptamer Technology for Smart Material Applications

2014· article· en· W1963502925 on OpenAlexafffund
Amanda Foster, Maria C. DeRosa

Bibliographic record

VenuePolymers · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsCarleton University
FundersOntario Ministry of Research and InnovationAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsAptamerLayer by layerNanotechnologyMaterials scienceChitosanBiocompatible materialLayer (electronics)Nucleic acidPolyelectrolyteChemistryPolymerBiomedical engineeringOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Aptamers are short, single-stranded nucleic acids that fold into well-defined three dimensional (3D) structures that allow for binding to a target molecule with affinities and specificities that can rival or in some cases exceed those of antibodies. The compatibility of aptamers with nanostructures such as thin films, in combination with their affinity, selectivity, and conformational changes upon target interaction, could set the foundation for the development of novel smart materials. In this study, the development of a biocompatible aptamer-polyelectrolyte film system was investigated using a layer-by-layer approach. Using fluorescence microscopy, we demonstrated the ability of the sulforhodamine B aptamer to bind its cognate target while sequestered in a chitosan-hyaluronan film matrix. Studies using Ultraviolet-visible (UV-Vis) spectrophotometry also suggest that deposition conditions such as rinsing time and volume play a strong role in the internal film interactions and growth mechanisms of chitosan-hyaluronan films. The continued study and development of aptamer-functionalized thin films provides endless new opportunities for novel smart materials and has the potential to revolutionize the field of controlled release.

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 score0.045
Threshold uncertainty score0.648

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 head0.032
GPT teacher head0.295
Teacher spread0.262 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

Explore more

Same venuePolymersSame topicPolymer Surface Interaction StudiesFrench-language works237,207