Biophotonic markers of malignancy: Discriminating cancers using wavelength-specific biophotons
Bibliographic record
Abstract
Early detection is a critically important factor when successfully diagnosing and treating cancer. Whereas contemporary molecular techniques are capable of identifying biomarkers associated with cancer, surgical interventions are required to biopsy tissue. The common imaging alternative, positron-emission tomography (PET), involves the use of nuclear material which poses some risks. Novel, non-invasive techniques to assess the degree to which tissues express malignant properties are now needed. Recent developments in biophoton research have made it possible to discriminate cancerous cells from normal cells both in vitro and in vivo. The current study expands upon a growing body of literature where we classified and characterized malignant and non-malignant cell types according to their biophotonic activity. Using wavelength-exclusion filters, we demonstrate that ratios between infrared and ultraviolet photon emissions differentiate cancer and non-cancer cell types. Further, we identified photon sources associated with three filters (420-nm, 620-nm., and 950-nm) which classified cancer and non-cancer cell types. The temporal increases in biophoton emission within these wavelength bandwidths is shown to be coupled with intrisitic biomolecular events using Cosic's resonant recognition model. Together, the findings suggest that the use of wavelength-exclusion filters in biophotonic measurement can be employed to detect cancer in vitro.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".