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Record W3024568395 · doi:10.1021/acs.jpclett.9b03833

Self-Healing Dyes—Keeping the Promise?

2020· article· en· W3024568395 on OpenAlexafffund
Michael Isselstein, Lei Zhang, Viktorija Glembockyte, Oliver Brix, Gonzalo Cosa, Philip Tinnefeld, Thorben Cordes

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaH2020 European Research CouncilDeutsche ForschungsgemeinschaftCanada Foundation for InnovationAlexander von Humboldt-Stiftung
KeywordsPhotobleachingBioconjugationFluorescenceNanotechnologyChemistrySelf-healingBiophysicsMaterials scienceBiology

Abstract

fetched live from OpenAlex

Self-healing dyes have emerged as a new promising class of fluorescent labels. They consist of two units, a fluorescent dye and a photostabilizer. The latter heals whenever the fluorescent dye is in danger of taking a reaction pathway toward photobleaching. We describe the underlying concepts and summarize the developmental history and state-of-the-art, including latest applications in high-resolution microscopy, live-cell, and single-molecule imaging. We further discuss remaining limitations, which are (i) lower photostabilization of most self-healing dyes when compared to solution additives, (ii) limited mechanistic understanding on the influence of the biochemical environment and molecular oxygen on self-healing, and (iii) the lack of cheap and facile bioconjugation strategies. Finally, we provide ideas on how to further advance self-healing dyes, show new data on redox blinking caused by double-stranded DNA, and highlight forthcoming work on intramolecular photostabilization of fluorescent proteins.

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)0.004
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.018
Open science0.0010.002
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0090.005

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 head0.251
Teacher spread0.244 · 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

Citations63
Published2020
Admission routes2
Has abstractyes

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