Ultra-weak Photon Emissions Differentiate Malignant Cells from Non- Malignant Cells In Vitro
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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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 | 0.000 |
| 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) | 0.001 | 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".