MétaCan
Menu
Back to cohort
Record W2404958887 · doi:10.1021/acs.nanolett.5b02123

Controlling Hybridization Chain Reactions with pH

2015· article· en· W2404958887 on OpenAlexafffund
Andrea Idili, Alessandro Porchetta, Alessia Amodio, Alexis Vallée‐Bélisle, Francesco Ricci

Bibliographic record

VenueNano Letters · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité de Montréal
FundersEuropean Research CouncilNatural Sciences and Engineering Research Council of CanadaDirectorate-General for Research and InnovationAssociazione Italiana per la Ricerca sul Cancro
KeywordsConcatemerChain reactionTemplateDNARational designNanotechnologyChemistryBiophysicsPolymerizationDNA–DNA hybridizationNanoreactorCombinatorial chemistryMaterials scienceBiochemistryGeneBiologyPolymerNanoparticlePhotochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

By taking inspiration from nature, where self-organization of biomolecular species into complex systems is finely controlled through different stimuli, we propose here a rational approach by which the assembly and disassembly of DNA-based concatemers can be controlled through pH changes. To do so we used the hybridization chain reaction (HCR), a process that, upon the addition of an initiator strand, allows to create DNA-based concatemers in a controlled fashion. We re-engineered the functional units of HCR through the addition of pH-dependent clamp-like triplex-forming domains that can either inhibit or activate the polymerization reaction at different pHs. This allows to finely regulate the HCR-induced assembly and disassembly of DNA concatemers at either basic or acidic pHs in a reversible way. The strategies we present here appear particularly promising as novel tools to achieve better spatiotemporal control of self-assembly processes of DNA-based 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.

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 score0.120
Threshold uncertainty score0.283

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 head0.242
Teacher spread0.232 · 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

Citations59
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueNano LettersSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207