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Record W3112653960 · doi:10.1038/s41598-020-79198-x

High yield cyclotron production of a novel 133/135La theranostic pair for nuclear medicine

2020· article· en· W3112653960 on OpenAlexaff
Bryce J. B. Nelson, John S. Wilson, Jan Andersson, Frank Wuest

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsCyclotronYield (engineering)RadiochemistryNuclear medicineProtonIrradiationMaterials scienceChemistryPhysicsNuclear physicsElectronMedicine

Abstract

fetched live from OpenAlex

Abstract This study reports the high-yield production of a novel 133/135 La theranostic pair at a 22 MeV proton beam energy as an attractive alternative to the recently introduced 132/135 La pair, demonstrating over an order of magnitude production increase of 133/135 La (231 ± 8 MBq 133 La and 166 ± 5 MBq 135 La at End of Bombardment (EOB)) compared to 11.9 MeV production of 132/135 La (0.82 ± 0.06 MBq 132 La and 19.0 ± 1.2 MBq 135 La) for 500 µA·min irradiations. A new sealed solid cyclotron target is introduced, which is fast to assemble, easy to handle, storable, and contains reusable components. Radiolabeling with macrocyclic chelators DOTA and macropa achieved full incorporation, with respective apparent 133 La molar activites of 33 ± 5 GBq/µmol and 30 ± 4 GBq/µmol. PET centers with access to a 22 MeV capable cyclotron could produce clinically-relevant doses of 133/135 La, via nat Ba irradiation, as a standalone theranostic agent for PET imaging and Auger electron therapy. With lower positron energies and less energetic and abundant gamma rays than 68 Ga, 44 Sc and 132 La, 133 La appears to be an attractive radiometal candidate for PET applications requiring a higher scanning resolution, a relatively long isotopic half-life, ease of handling, and a low patient dose.

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)0.001
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 score0.148
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.068
GPT teacher head0.313
Teacher spread0.246 · 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

Citations35
Published2020
Admission routes1
Has abstractyes

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