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Record W4411441575 · doi:10.1002/anie.202506590

Decoy DNA Protects Molecular Tension Probes from DNase Degradation

2025· article· en· W4411441575 on OpenAlexafffund
Seong Ho Kim, Isaac T. S. Li

Bibliographic record

VenueAngewandte Chemie International Edition · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMichael Smith Health Research BC
KeywordsDecoyDNAMechanobiologyDegradation (telecommunications)BiophysicsNucleic acidMechanotransductionNanotechnologyComputational biologyChemistryCell biologyComputer scienceBiologyMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

DNA-based molecular probes are essential tools for visualizing and quantifying mechanotransduction at the single-molecule level. However, their application in live-cell environments is severely limited by DNase-mediated degradation, which shortens probe lifespan and introduces false-positive signals. Here, we present a decoy DNA strategy where an excess of unmodified double-stranded DNA competitively binds DNases, effectively preserving functional DNA probes. This approach extends probe stability from 1-2 h to beyond 24 h, substantially improving signal integrity in live-cell tension imaging. In contrast to structurally modified nucleic acids, decoy DNA can be readily applied to existing DNA probe systems, enabling seamless integration without the need for additional validation or calibration. This cost-effective and scalable strategy provides a generalizable framework for stabilizing DNA-based molecular tools in DNase-rich environments, enabling high-precision mechanobiology studies across diverse cell types and extended experiment durations.

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: none
Teacher disagreement score0.638
Threshold uncertainty score0.521

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 head0.006
GPT teacher head0.268
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

Citations4
Published2025
Admission routes2
Has abstractyes

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