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Initial insights into the interaction of antibodies radiolabeled with Lutetium-177 and Actinium-225 with tumor microenvironment in experimental human and canine osteosarcoma

2024· article· en· W4396536625 on OpenAlexafffund
Sabeena Giri, Kevin J. Allen, Chandra Bose Prabaharan, Jonathan Bonet Ramírez, L. Fiore, Maruti Uppalapati, Ekaterina Dadachova

Bibliographic record

VenueNuclear Medicine and Biology · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchCanadian Institute for Military and Veteran Health Research
KeywordsOsteosarcomaMonoclonal antibodyCancer researchChemistryAntibodyCancerTumor microenvironmentMedicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Lu) radionuclides with the OS cells and tumor microenvironment (TME) in experimental human and canine OS. BASIC PROCEDURES: Ac-labeled IF3 antibody, sacrificed at 24, 72 or 168 h post-treatment and their tumors were analyzed by immunohistochemistry (IHC) for the presence of OS cells, various elements of TME as well as for the double DNA strand breaks with γH2AX and caspase 3 assays. MAIN FINDINGS: Ac]Ac-IF3 was observed at 24 and 72 h post-RIT. PRINCIPAL CONCLUSIONS: These findings suggest that radiolabeled antibodies offer a hopeful avenue for personalized OS treatment, emphasizing the importance of understanding their impact on the TME and potential synergies with immunotherapy.

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 head0.031
GPT teacher head0.348
Teacher spread0.317 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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