Seeking the Source of Transience for a Unique Magnetic Field Pattern That Completely Dissolves Cancer Cells <i>in Vitro</i>
Bibliographic record
Abstract
Purpose: Exposure to a particular pattern of weak (~3 to 5 μT) magnetic fields produced by computer-generated point durations within three-dimensions completely dissolved malignant cancer cells but not healthy cells. Biomolecular analyses and confocal microscopy indicated excessive expansion followed by contraction contributed to the “explosion” of the cell. However, after months of replicable effects, the phenomenon slowly ceased. Considering the potency of the complete dissolution of cancer cell lines after 5 days of 6.5-hour daily exposures and the implications for human treatment, the potential source of the disappearance of the effect was pursued by summarizing all of the 50 experiments and assessing the likely etiologies. Materials and Methods: B16-BL6, MDAMB 231 and MCF7 malignant cells and HSG, a non-malignant cell line, were exposed to a sham-field condition or to a specific pattern of computer-generated magnetic fields produced from converting different voltages, each with point durations of 3 ms to 3-D magnetic fields. Conclusion: The specific serial presentation of the two field patterns (one frequency modulated; the other amplitude and frequency modulated) completely dissolved malignant cells but not normal cells within a “zone” within the exposure volume at the conjunction of the three planes of the applied magnetic fields. The affected cells underwent massive melanin production, expansion, contraction and “beading” of submembrane actin structures before fragmentation within this zone. However, this powerful all-or-none phenomenon may have been disrupted by moving the cells, excess mechanical agitation during exposure, or non-optimal point durations of the field parameters. Indirect effects from communication signals (WIFI) through line currents that operated the incubators could not be excluded.
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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".