A high-throughput alpha particle irradiation system for monitoring DNA damage repair, genome instability and screening in human cell and yeast model systems
Bibliographic record
Abstract
Ionizing radiation (IR) is environmentally prevalent and, depending on dose and linear energy transfer (LET), can elicit serious health effects by damaging DNA. Relative to low LET photon radiation (X-rays, gamma rays), higher LET particle radiation produces more disease causing, complex DNA damage that is substantially more challenging to resolve quickly or accurately. Despite the majority of human lifetime IR exposure involving long-term, repetitive, low doses of high LET alpha particles (e.g. radon gas inhalation), technological limitations to deliver alpha particles in the laboratory conveniently, repeatedly, over a prolonged period, in low doses and in an affordable, high-throughput manner have constrained DNA damage and repair research on this topic. To resolve this, we developed an inexpensive, high capacity, 96-well plate-compatible alpha particle irradiator capable of delivering adjustable, low mGy/s particle radiation doses in multiple model systems and on the benchtop of a standard laboratory. The system enables monitoring alpha particle effects on DNA damage repair and signalling, genome stability pathways, oxidative stress, cell cycle phase distribution, cell viability and clonogenic survival using numerous microscopy-based and physical techniques. Most importantly, this method is foundational for high-throughput genetic screening and small molecule testing in mammalian and yeast cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".