Absolute cross sections for chemoradiation therapy: Damages to cisplatin-DNA complexes induced by 10 eV electrons
Bibliographic record
Abstract
In chemoradiation therapy, the synergy between the radiation and the chemotherapeutic agent (CA) can result in a super-additive treatment. A priori, this increased effectiveness could be estimated from model calculations, if absolute cross sections (ACSs) involved in cellular damage are substantially higher, when the CA binds to DNA. We measure ACSs for damages induced by 10 eV electrons, when DNA binds to the CA cisplatin as in chemotherapy. At this energy, DNA is damaged essentially by the decay of core-excited transient anions into bond-breaking channels. Films of cisplatin-DNA complexes of ratio 5:1 with thicknesses 10, 15, and 20 nm were irradiated in vacuum during 5–30 s. Conformation changes were quantified by electrophoresis and yields extrapolated from exposure-response curves. Base damages (BDs) were revealed and quantified by enzymatic treatment. The ACSs were generated from these yields by two mathematical models. For 3197 base-pair plasmid DNA, ACS for single strand breaks, double strand breaks (DSBs), crosslinks, non-DSB cluster damages, and total BDs is 71 ± 2, 9.3 ± 0.4, 10.1 ± 0.3, 8.2 ± 0.3, and 115 ± 2 ×10−15 cm2, respectively. These ACSs are higher than those of nonmodified DNA by factors of 1.6 ± 0.1, 2.2 ± 0.1, 1.3 ± 0.1, 1.3 ± 0.3, and 2.1 ± 0.4, respectively. Since LEEs are produced in large quantities by radiolysis and strongly interact with biomolecules, we expect such enhancements to produce substantial additional damages in the DNA of the nucleus of cancer cells during concomitant chemoradiation therapy. The increase damage appears sufficiently large to justify more elaborate simulations, which could provide a quantitative evaluation of molecular sensitization by Pt-CAs.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".