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Record W4415952920 · doi:10.1051/epjconf/202533809010

Microdosimetry of kidney cells exposed to <sup>243</sup> Am and X-ray irradiation

2025· article· en· W4415952920 on OpenAlexaff
Pia Kahle, Christian Senwitz, Anne Heller, Anja Seifert, Steffen Taut, T. Kormoll

Bibliographic record

VenueEPJ Web of Conferences · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsInstitute of Particle Physics
FundersBundesministerium für Bildung und Forschung
KeywordsAmericiumRadiobiologyIonizing radiationIrradiationDose rateAbsorbed doseKidneyDosimetryDose dependence

Abstract

fetched live from OpenAlex

In vitro experiments with rat kidney cells exposed to 243 Am in cell culture medium show a decreasing cell viability with increasing americium concentration. Both the chemical cytotoxicity of americium and the dose caused by ionizing radiation have to be considered as possible causes. To determine the dose rate, a model which was reduced to the dosimetric relevant circumstances was set up. Since the size of the cells is in the micrometer range α -radiation makes the biggest contribution to the dose rate. Using the characteristic behaviour of α -particles the dose rate can be calculated from fluence and stopping power. Biological aspects like uptake of Americium into the cells as well as inaccuracies arising from inhomogeneity and dynamic behaviour in biological systems were taken into account. The dose rate was considered pointwise in the cell center and averaged over the whole cell. Calculations show that dose rates up to the Gy/h range were achieved at the highest americium concentrations of 8⋅10 -4 M. For an experimental comparison of the effect in dependence of the dose reference samples of untreated kidney cells were exposed to analog doses in an X-ray field. Taking relative biological effectiveness into account the cell viability curves depending on the dose show good agreement.

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 score0.055
Threshold uncertainty score0.322

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 studies0.0000.000
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 head0.021
GPT teacher head0.303
Teacher spread0.282 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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