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Record W3152973233 · doi:10.1039/d1cc00495f

The Hantzsch reaction for nitrogen-13 PET: preparation of [<sup>13</sup>N]nifedipine and derivatives

2021· article· en· W3152973233 on OpenAlexaff
Julia E. Blower, Michelle Ma, Fahad Al-Salemee, Antony D. Gee

Bibliographic record

VenueChemical Communications · 2021
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsSt. Thomas Hospital
FundersCentre For Medical Engineering, King’s College LondonKing's College LondonEngineering and Physical Sciences Research CouncilNational Institute for Health and Care ResearchKing's College Hospital NHS Foundation TrustCancer Research UKWellcome TrustWellcome
KeywordsRadiosynthesisChemistryNifedipineRadiochemistryNitrogenPet imagingNuclear chemistryOrganic chemistryPositron emission tomographyNuclear medicineCalcium

Abstract

fetched live from OpenAlex

Nitrogen-13 is an attractive but under-used PET radionuclide for labelling molecules of biological and pharmaceutical interest, complementing other PET radionuclides. Its short half-life (t1/2 = 9.97 min) imposes synthetic challenges, but we have expanded the hitherto limited pool of 13N labelling strategies and tracers by adapting the multicomponent Hantzsch condensation reaction to prepare a library of 13N-labelled 1,4-dihydropyridines from [13N]ammonia, including the widely-used drug nifedipine. This represents a key advance in 13N PET radiochemistry, and will serve to underpin the renewed interest in clinical opportunities offered by short-lived PET tracers.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.037
GPT teacher head0.342
Teacher spread0.305 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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