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Meitner-Auger Electron Emitters for Targeted Radionuclide Therapy: Mercury-197m/g and Antimony-119.

2021· article· en· W3119167128 on OpenAlex
Parmissa Randhawa, Aeli P. Olson, Shaohuang Chen, Kaley Lexi Gower-Fry, Cornelia Hoehr, Jonathan W. Engle, Caterina F. Ramogida, Valery Radchenko

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCurrent Radiopharmaceuticals · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsTRIUMFSimon Fraser University
FundersNational Cancer InstituteNational Institute on AgingNatural Sciences and Engineering Research Council of Canada
KeywordsAntimonyMercury (programming language)RadionuclideAuger electron spectroscopyRadiochemistryMaterials scienceAugerChemistryNuclear physicsMetallurgyAtomic physicsPhysicsComputer science

Abstract

fetched live from OpenAlex

Targeted Radionuclide Therapies (TRTs) based on Auger emitting radionuclides have the potential to deliver extremely selective therapeutic payloads on the cellular level. However, to fully exploit this potential, suitable radionuclides need to be applied in combination with appropriate delivery systems. In this review, we summarize the state-of-the-art production, purification, chelation and applications of two promising candidates for Targeted Auger Therapy, namely antimony- 119 (<sup>119</sup>Sb) and mercury-197 (<sup>197</sup>Hg). Both radionuclides have great potential to become efficient tools for TRT. We also highlight our current progress on the production of both radionuclides at TRIUMF and the University of Wisconsin.

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.

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 score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.410
Teacher spread0.343 · 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