Growth of injected melanoma cells is suppressed by whole body exposure to specific spatial-temporal configurations of weak intensity magnetic fields
Bibliographic record
Abstract
PURPOSE: To measure the effect of exposure to a specific spatial-temporal, hysiologically-patterned electromagnetic field presented using different geometric configurations on the growth of experimental tumours in mice. METHODS: C57b male mice were inoculated subcutaneously with B16-BL6 melanoma cells in two blocks of experiments separated by six months (to control for the effects of geomagnetic field). The mice were exposed to the same time-varying electromagnetic field nightly for 3 h in one of six spatial configurations or two control conditions and tumour growth assessed. RESULTS: Mice exposed to the field that was rotated through the three spatial dimensions and through all three planes every 2 sec did not grow tumours after 38 days. However, the mice in the sham-field and reference controls showed massive tumours after 38 days. Tumour growth was also affected by the intensity of the field, with mice exposed to a weak intensity field (1-5 nT) forming smaller tumours than mice exposed to sham or stronger, high intensity (2-5 microT) fields. Immunochemistry of tumours from those mice exposed to the different intensity fields suggested that alterations in leukocyte infiltration or vascularisation could contribute to the differences in tumour growth. CONCLUSIONS: Exposure to specific spatial-temporal regulated electromagnetic field configurations had potent effects on the growth of experimental tumours in mice.
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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".