AN ImageJ-BASED ALGORITHM FOR A SEMI-AUTOMATED METHOD FOR MICROSCOPIC IMAGE ENHANCEMENT AND DNA REPAIR FOCI COUNTING
Bibliographic record
Abstract
Proper evaluation of the health risks of low-dose ionizing radiation exposure heavily relies on the ability to accurately measure very low levels of DNA damage in cells. One of the most sensitive methods for measuring DNA damage levels is the quantification of DNA repair foci that consist of macromolecular aggregates of DNA repair proteins, such as γH2AX and 53BP1, forming around individual DNA double-strand breaks. They can be quantified using immunofluorescence microscopy and are widely used as markers of DNA double-strand breaks. However this quantification, if performed manually, may be very tedious and prone to inter-individual bias. Low-dose radiation studies are especially sensitive to this potential bias due to a very low magnitude of the effects anticipated. Therefore, we designed and validated an algorithm for the semi-automated processing of microscopic images and quantification of DNA repair foci. The algorithm uses ImageJ, a freely available image analysis software that is customizable to individual cellular properties or experimental conditions. We validated the algorithm using immunolabeled 53BP1 and γH2AX in normal human fibroblast AG01522 cells under both normal and irradiated conditions. This method is easy to learn, can be used by nontrained personnel, and can help avoiding discrepancies in inter-laboratory comparison studies examining the effects of low-dose radiation.
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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 | 0.001 | 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".