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Record W3178341117 · doi:10.1080/09553002.2021.1951392

Modeling bystander effects that cause growth delay of breast cancer xenografts in bone marrow of mice treated with radium-223

2021· article· en· W3178341117 on OpenAlexaff
Didier A. Rajon, Brian S. Canter, Calvin N. Leung, Tom Bäck, J. Christopher Fritton, Edouard I. Azzam, Roger W. Howell

Bibliographic record

VenueInternational Journal of Radiation Biology · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsCanadian Nuclear Laboratories
FundersNational Cancer InstituteNational Institutes of HealthBritish Society of Criminology
KeywordsBystander effectBone marrowBreast cancerIn vivoCancer researchCancerMedicineCancer cellOncologyNuclear medicinePathologyImmunologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Rationale The role of radiation-induced bystander effects in cancer therapy with alpha-particle emitting radiopharmaceuticals remains unclear. With renewed interest in using alpha-particle emitters to sterilize disseminated tumor cells, micrometastases, and tumors, a better understanding of the direct effects of alpha particles and the contribution of the bystander responses they induce is needed to refine dosimetric models that help predict clinical benefit. Accordingly, this work models and quantifies the relative importance of direct effects (DE) and bystander effects (BE) in the growth delay of human breast cancer xenografts observed previously in the tibiae of mice treated with 223RaCl2.Methods A computational model of MDA-MB-231 and MCF-7 human breast cancer xenografts in the tibial bone marrow of mice administered 223RaCl2 was created. A Monte Carlo radiation transport simulation was performed to assess individual cell absorbed doses. The responses of the breast cancer cells to direct alpha particle irradiation and gamma irradiation were needed as input data for the model and were determined experimentally using a colony-forming assay and compared to the responses of preosteoblast MC3T3-E1 and osteocyte-like MLO-Y4 bone cells. Using these data, a scheme was devised to simulate the dynamic proliferation of the tumors in vivo, including DE and BE propagated from the irradiated cells. The parameters of the scheme were estimated semi-empirically to fit experimental tumor growth.Results A robust BE component, in addition to a much smaller DE component, was required to simulate the in vivo tumor proliferation. We also found that the relative biological effectiveness (RBE) for cell killing by alpha particle radiation was greater for the bone cells than the tumor cells.Conclusion This modeling study demonstrates that DE of radiation alone cannot explain experimental observations of 223RaCl2-induced growth delay of human breast cancer xenografts. Furthermore, while the mechanisms underlying BE remain unclear, the addition of a BE component to the model is necessary to provide an accurate prediction of the growth delay. More complex models are needed to further comprehend the extent and complexity of 223RaCl2-induced BE.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.325
Teacher spread0.306 · 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 designSimulation or modeling
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

Citations12
Published2021
Admission routes1
Has abstractyes

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