Inhibition of Cancer Cell Growth by Exposure to a Specific Time-Varying Electromagnetic Field Involves T-Type Calcium Channels
Bibliographic record
Abstract
Electromagnetic field (EMF) exposures affect many biological systems. The reproducibility of these effects is related to the intensity, duration, frequency, and pattern of the EMF. We have shown that exposure to a specific time-varying EMF can inhibit the growth of malignant cells. Thomas-EMF is a low-intensity, frequency-modulated (25-6 Hz) EMF pattern. Daily, 1 h, exposures to Thomas-EMF inhibited the growth of malignant cell lines including B16-BL6, MDA-MB-231, MCF-7, and HeLa cells but did not affect the growth of non-malignant cells. Thomas-EMF also inhibited B16-BL6 cell proliferation in vivo. B16-BL6 cells implanted in syngeneic C57b mice and exposed daily to Thomas-EMF produced smaller tumours than in sham-treated controls. In vitro studies showed that exposure of malignant cells to Thomas-EMF for > 15 min promoted Ca(2+) influx which could be blocked by inhibitors of voltage-gated T-type Ca(2+) channels. Blocking Ca(2+) uptake also blocked Thomas-EMF-dependent inhibition of cell proliferation. Exposure to Thomas-EMF delayed cell cycle progression and altered cyclin expression consistent with the decrease in cell proliferation. Non-malignant cells did not show any EMF-dependent changes in Ca(2+) influx or cell growth. These data confirm that exposure to a specific EMF pattern can affect cellular processes and that exposure to Thomas-EMF may provide a potential anti-cancer therapy.
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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".