Computational analysis of the number, area and density of γ-H2AX foci in breast cancer cells exposed to<sup>111</sup>In-DTPA-hEGF or γ-rays using Image-J software
Bibliographic record
Abstract
PURPOSE: To develop a simple method for the quantification of gamma-H2AX focus number, density and size. METHODS: MDA-MB-468 human breast cancer cells were treated overnight with (111)In-diethylenetriaminepentaacetic acid human epidermal growth factor ((111)In-DTPA-hEGF, 0-142 kBq/pmol) or exposed to gamma-radiation to induce DNA double strand breaks (DSB). DNA DSB formation was evaluated by detection of phosphorylated histone H2AX on serine 139 (gamma-H2AX) using immunofluorescence. Confocal microscopy was used to capture images of gamma-H2AX foci and cell nuclei. Image-J software with customized macros was used to quantify gamma-H2AX foci. RESULTS: The number of gamma-H2AX foci per nucleus scored using Image-J correlated strongly with the number scored using direct visual confirmation (coefficient of determination, R(2) = 0.950; 60 nuclei scored). The mean density (grayscale values per pixel), area and integrated density (IntDen) of individual foci increased linearly as the specific radioactivity (SR) increased up to 67 kBq/pmol (R(2) values of 0.826, 0.964, 0.978, respectively). The mean number of foci per nucleus, the combined area of gamma-H2AX foci per nucleus and the IntDen per nucleus also increased linearly with SR, giving R(2) values of 0.926, 0.974 and 0.983, respectively. Similar linear relationships were observed with the gamma-ray absorbed dose up to 3.0 Gy. CONCLUSIONS: The density, area and IntDen of individual foci, as well as the number of gamma-H2AX foci, total focus area and IntDen per nucleus were successfully quantified using Image-J with customized macros.
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".