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Record W4367056843 · doi:10.1134/s106377962302003x

Radiochemical Investigations for Radiopharmaceutical Nuclear Medicine at JINR Laboratory of Nuclear Problems

2023· article· en· W4367056843 on OpenAlexaff
D.V. Filosofov, Ayagoz Baimukhanova, Elena Kurakina, D. V. Karaivanov, A. Velichkov, Valery Radchenko, E. Yakushev

Bibliographic record

VenuePhysics of Particles and Nuclei · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRadionuclideNuclear medicineMedical physicsRadionuclide therapyRadiochemistryMedicinePhysicsComputer scienceNuclear physicsChemistry

Abstract

fetched live from OpenAlex

Abstract In recent years, radiopharmaceuticals have been increasingly used for diagnostics and treatment of cancer. In addition to a biological vector, a modern radiopharmaceutical includes a chelator that binds the radionuclide, as well as a linker for connecting the vector and the chelator. The development of such an approach requires the improvement of methods for obtaining and purifying radionuclides, and the development of methods for the synthesis of radiopharmaceuticals, i.e., preparative direction. It is also necessary to search for new vectors and chelators. This implies the development of methods for analyzing the properties of radiopharmaceuticals in general, as well as their precursors, i.e., analytical direction. In this review, we describe the prerequisites for successfully solving a wide range of challenges in these two areas of nuclear medicine at the Scientific and Experimental Department of Nuclear Spectroscopy and Radiochemistry of the Laboratory of Nuclear Problems of the Joint Institute for Nuclear Research (LNP JINR). These prerequisites are due to rich experience in obtaining the widest range of radionuclides and their application for various spectrometric studies. Both the past and present works on radiopharmaceutical topics carried out in the department are described, and ways of future development are outlined.

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 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 score0.087
Threshold uncertainty score0.426

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 studies0.0000.001
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 head0.342
Teacher spread0.275 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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