Investigating chromosome damage using fluorescent<i>in situ</i>hybridization to identify biomarkers of radiosensitivity in prostate cancer patients
Bibliographic record
Abstract
PURPOSE: In order to evaluate fluorescent in situ hybridization (FISH) as a method for predicting radiosensitivity, this study examined the incidence of translocations, after exposure to in vitro radiation, in both normally responding patients and those exhibiting severe late effects after radiotherapy treatment. MATERIALS AND METHODS: Patients were selected from a randomized trial for intermediate-risk prostate cancer. Of the patients entered on trial with mature follow-up, 3% developed grade 3 late proctitis. Blood samples were taken from this radiosensitive cohort along with matched control patients with no late proctitis. Whole blood samples were exposed to 0 or 4 Gy and cultured according to the International Atomic Energy Agency (IAEA) recommended methods. Colour junctions were evaluated in the resulting metaphases and scored according to the Protocol for Aberration Identification and Nomenclature Terminology (PAINT) system. RESULTS: Both groups were statistically similar at 0 Gy. After 4 Gy in vitro radiation, the radiosensitive group had significantly higher rates of chromosome damage in the number of colour junctions per cell (p = 0.002), the number of deletions per cell (p = 0.01) and the number of dicentrics per cell (p = 0.005). CONCLUSIONS: These results indicate that the analysis of translocations using FISH after in vitro irradiation correlates with clinical response to radiation. This cytogenetic assay should be considered as a potential predictor of radiosensitivity.
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 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".