Effects of Patterned Electromagnetic Fields and Light-Emitting Diodes on Cancer Cells: Impact on Cell Density and Biophoton Emission When Applied Individually vs. Simultaneously
Bibliographic record
Abstract
It has been previously reported that time-varying EMFs and LEDs have the potential to modulate cellular activity and cell viability. It has also been shown that cellular activity and state can be inferred by measuring the biophoton emission derived from these same cells. To identify if the brief application (15 min) of an LED (635 nm at 3 klx) or EMF (1–3 uT) could influence cell growth and subsequent biophoton emission characteristics, B16-BL6 cells were grown to confluence and exposed to a time-varying, frequency-modulated EMF, LED, or both. Before and after EMF and LED exposure, photon emission measurements were taken for 1 min at a 50 Hz sampling rate. Following the exposure and photon emission measurements, cell viability was assessed via the use of a hemocytometer. The results demonstrated that after only 15 min of exposure to a time-varying EMF, there was a 41.6% reduction in viable cells when compared to sham controls [t(25) = 2.4, p = 0.02]. This effect approached significance in the LED alone condition [p = 0.07] but was completely absent in the condition wherein the LED and EMF were applied simultaneously [p < 0.8]. Additionally, following exposure to only the LED, there was a significant increase in biophoton emission SPD values at 13 Hz from whole cell cultures [t(60) = 2.3, p = 0.021]. This biophoton emission frequency was also strongly correlated with the number of nonviable cells [r = −0.514] in the dish. Taken together, these data point to biophotons emitted from cell cultures at 13 Hz as a potential indicator of the number of nonviable cells in vitro. The summation of data here corroborates previous work demonstrating the efficacy of specific time-varying EMFs as a novel therapeutic for the inhibition of cancer cell growth. It also furthers our assertion that biophoton emission can be used as a novel detection tool for cell activity.
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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 | 0.000 | 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".