Ultra-weak Photon Emissions Differentiate Malignant Cells from Non- Malignant Cells In Vitro
Why this work is in the frame
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Bibliographic record
Abstract
A fast, inexpensive, and accurate method for differentiating normal (non-malignant) cells from malignant cells could facilitate diagnosis and subsequently treatment. Although blood constituents are the current dominant indicators, we have found that Spectral Power Densities (SPD) obtained from only 100 s of measurements of spontaneous ultra-weak photon emissions (UPE) from cell cultures significantly differentiated malignant from non-malignant states. Breast cells were particularly differentiable from nonbreast cells according to their SPD profiles. More critically the combination of only 3 discrete frequency increment changes in SPD profiles accurately classified 85% of malignant breast cells from normal breast cells in culture. These results confirm results from our mouse experiments and preliminary observations from our human measurements that appropriately analyzed and interpreted SPD from very brief samples of UPE may be a viable tool for early detection of malignancy.
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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 | 0.001 |
| 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 it