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Record W3131473546 · doi:10.1107/s1600577521000400

Perspectives for microbeam irradiation at the SYRMEP beamline

2021· article· en· W3131473546 on OpenAlexaff
Elisabeth Schültke, Stefan Fiedler, R.H. Menk, Felix Jaekel, Diego Dreossi, K. Casarin, Giuliana Tromba, Stefan Bartzsch, Stephan Kriesen, Guido Hildebrandt, F. Arfelli

Bibliographic record

VenueJournal of Synchrotron Radiation · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of Saskatchewan
FundersDeutsche ForschungsgemeinschaftCentral European Research Infrastructure Consortium
KeywordsMicrobeamBeamlineIrradiationCollimatorNuclear medicineOpticsSynchrotronMaterials scienceBeam (structure)MedicinePhysicsNuclear physics

Abstract

fetched live from OpenAlex

It has been shown previously both in vitro and in vivo that microbeam irradiation (MBI) can control malignant tumour cells more effectively than the clinically established concepts of broad beam irradiation. With the aim to extend the international capacity for microbeam research, the first MBI experiment at the biomedical beamline SYRMEP of the Italian synchrotron facility ELETTRA has been conducted. Using a multislit collimator produced by the company TECOMET, arrays of quasi-parallel microbeams were successfully generated with a beam width of 50 µm and a centre-to-centre distance of 400 µm. Murine melanoma cell cultures were irradiated with a target dose of approximately 65 Gy at a mean photon energy of ∼30 keV with a dose rate of 70 Gy s−1 and a peak-to-valley dose of ∼123. This work demonstrated a melanoma cell reduction of approximately 80% after MBI. It is suggested that, while a high energy is essential to achieve high dose rates in order to deposit high treatment doses in a short time in a deep-seated target, for in vitro studies and for the treatment of superficial tumours a spectrum in the lower energy range might be equally suitable or even advantageous.

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)0.004
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.004

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 head0.009
GPT teacher head0.289
Teacher spread0.280 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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