PV-0572: Biological effects by the next generation of ultra-fast dose rate ionizing radiation ‘FLASH’
Bibliographic record
Abstract
Purpose or ObjectiveProton pencil beam scanning delivers a more conformal dose distribution than the best photon modality thereby diminishing low and medium dose levels to the normal tissues.Proton radiobiology studies have primarily focused on identifying the relative biological effectiveness (RBE) of proton radiation.It is becoming evident that the RBE increases along the proton track as the linear energy transfer (LET) reaches maximum levels at the distal end of the spread-out Bragg peak.Transcriptomic changes in normal tissues receiving sublethal proton radiation doses have only been scarcely studied.At this point, it is still unclear whether proton beam scanning and photon radiation induce equivalent changes in the coding transcriptome.The aim of the present study was to identify signal transduction pathways regulated differently by relatively low and high LET proton beams.These differences were also compared with pathway regulation in Co-60 irradiated fibroblasts. Material and MethodsThe study used 12 primary subcutaneous fibroblast cell cultures.Mono-layered fibroblasts were irradiated at positions in the entrance and SOBP distal edge of the proton beam profile.Dose was delivered in 3 fractions x 3.5 GyE (RBE 1.1).Cobalt-60 irradiation was used as reference.RNA sequencing was performed using Illumina NextSeq 500 with high-output kit.The Tuxedo suite protocol was employed for data analysis.Real-time qPCR will be performed to validate RNA-sequencing findings. ResultsCytokines involved in modulation of the JAK-STAT and MAPK/ERK pathways appear to be more heavily upregulated in fibroblasts irradiated with Co-60 gamma rays than in proton irradiated fibroblasts; irrespective of LET.The profibrogenic genes COL3A1 and COL3A3 coding subunits of collagen type III are more upregulated by the relatively high LET at the distal edge of the SOBP compared with the lower LET at the entrance region. ConclusionUpcoming pathway analysis will disclose whether signal transduction is altered in pathways critical for development of normal tissue damage and secondary cancers depending on the radiation quality.An improved understanding of the transcriptomic changes in normal tissues will undoubtedly be helpful in optimizing proton therapy in the future. SP-0574 Investment cases at the global level: how can we translate successful precedents to radiotherapy? D.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".