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Record W4367180567 · doi:10.1021/acs.jmedchem.2c01685

Tetrazine-Derived Near-Infrared Dye for Targeted Photoacoustic Imaging of Bone

2023· article· en· W4367180567 on OpenAlexafffund
Rowan Swann, Samantha R. Slikboer, Afaf R. Genady, Luis Rafael Silva, Nancy Janzen, Amber Faraday, John F. Valliant, Saman Sadeghi

Bibliographic record

VenueJournal of Medicinal Chemistry · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchOntario Institute for Cancer ResearchCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsPretargetingChemistryIn vivoPhotoacoustic imaging in biomedicineTetrazineConjugateBisphosphonateBioorthogonal chemistryIndocyanine greenOsteoporosisCombinatorial chemistrySurgeryInternal medicineOrganic chemistry

Abstract

fetched live from OpenAlex

A near-infrared photoacoustic probe was used to image bone in vivo through active and bioorthogonal pretargeting strategies that utilized coupling between a tetrazine-derived cyanine dye and a trans -cyclooctene-modified bisphosphonate. In vitro hydroxyapatite binding of the probe via active and pretargeting strategies showed comparable increases in percent binding vs a nontargeted control. Intrafemoral injection of the bisphosphonate-dye conjugate showed retention out to 24 h post-injection, with a 14-fold increase in signal over background, while the nontargeted dye exhibited negligible binding to bone and signal washout by 4 h post-injection. Intravenous injection, using both active and pretargeting strategies, demonstrated bone accumulation as earlier as 4 h post-injection, where the signal was found to be 3.6- and 1.5-fold higher, respectively, than the signal from the nontargeted dye. The described bone-targeted dye enabled in vivo photoacoustic imaging, while the synthetic strategy provides a convenient building block for developing new targeted photoacoustic probes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.204
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

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