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Record W4415115987 · doi:10.1021/acssensors.5c02443

A Fibrosis-Targeting T <sub>1</sub> MRI Contrast Agent Synthesized via Photooxidative Self-Desulfurization of Porphyrin Thiourea

2025· article· en· W4415115987 on OpenAlexafffund
Kyle D. W. Vollett, Anlan Hong, Wanda W. Janaeska, Daryn R. Browne, Hai‐Ling Margaret Cheng

Bibliographic record

VenueACS Sensors · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSulfur Compounds in Biology
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationGovernment of Ontario
KeywordsPorphyrinIn vivoThioureaGuanidineMagnetic resonance imagingConjugated systemSubstrate (aquarium)

Abstract

fetched live from OpenAlex

Fibrosis is a silent disease that becomes untreatable and incurable in late stages, ultimately leading to organ failure. Early intervention can slow or even reverse progression, but current noninvasive tools like magnetic resonance imaging (MRI) and ultrasound are nonspecific and detect only advanced scar tissue. We present a fibrosis-targeting, nongadolinium MRI contrast agent for sensitive and specific in vivo imaging of fibrosis. Unlike conventional methods that infer scar content via stiffness or empty dead tissue space, our agent directly binds the excess collagen substrate of the scar tissue. To synthesize the agent, we conjugated an “MRI active” manganese porphyrin to a fibrosis-targeting free-base porphyrin via the first demonstration of porphyrin photooxidative self-desulfurization of aryl thiourea linkages to stable urea or guanidine linkages. The compound exhibited high affinity for acid-soluble collagen in a new scar and significantly reduced T 1 relaxation time in collagen gels. In a mouse model of diffuse myocardial fibrosis, the fibrosis-targeting agent highlighted scar tissue that was undetected by conventional late-gadolinium MRI.

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 categoriesMeta-epidemiology (narrow)
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 score0.003
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.225
Teacher spread0.220 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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