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Record W4389490976 · doi:10.1021/acssensors.3c01791

R3-Noria-methanesulfonate: A Molecular Cage with Superior Hyperpolarized Xenon-129 MRI Contrast

2023· article· en· W4389490976 on OpenAlexafffund
Yurii Shepelytskyi, Vira Grynko, Viktoriia Batarchuk, Carson Hasselbrink, Antal H. Kovacs, Iulian C. Ruset, Karla Rodriguez, Nedal Al Taradeh, Tanu Talwar, Brenton DeBoef, Mitchell S. Albert

Bibliographic record

VenueACS Sensors · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsNOSM UniversityLakehead UniversityThunder Bay Regional Research Institute
FundersNatural Sciences and Engineering Research Council of CanadaMitacsOntario Research Foundation
KeywordsXenonChemistryNuclear magnetic resonanceHyperpolarization (physics)Magnetic resonance imagingNuclear magnetic resonance spectroscopyStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hyperpolarized (HP) xenon-129 ( 129 Xe) magnetic resonance imaging (MRI) has the potential to be used as a molecular imaging modality. For this purpose, numerous supramolecular cages have been developed and evaluated in the past. Herein, we report a novel and unique macrocycle that can be successfully utilized for xenon MRI, the resorcinarene trimer methanesulfonate (R3-Noria-MeSO 3 H). This molecule is capable of two different contrast mechanisms for xenon-MRI, resulting from an increase in the effective spin–spin relaxation and hyperpolarized chemical exchange saturation transfer (HyperCEST). We have demonstrated a superior negative contrast caused by R3-Noria-MeSO 3 H on HP 129 Xe MRI at 3.0 T as well as HyperCEST imaging of the studied macrocycle. Additionally, we have found that the complex aggregation behaviors of R3-Noria-methanesulfonate and its impact on xenon-129 relaxivity are an area for future study.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.255
Teacher spread0.245 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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