Biophysical Insights into Cancer Transformation and Treatment
Bibliographic record
Abstract
Biological systems are hierarchically self-organized complex structures characterized by nonlinear interactions. Biochemical energy is transformed into work of physical forces required for various biological functions. We postulate that energy transduction depends on endogenous electrodynamic fields generated by microtubules. Microtubules and mitochondria colocalize in cells with microtubules providing tracks for mitochondrial movement. Besides energy transformation, mitochondria form a spatially distributed proton charge layer and a resultant strong static electric field, which causes water ordering in the surrounding cytosol. These effects create conditions for generation of coherent electrodynamic field. The metabolic energy transduction pathways are strongly affected in cancers. Mitochondrial dysfunction in cancer cells (Warburg effect) or in fibroblasts associated with cancer cells (reverse Warburg effect) results in decreased or increased power of the generated electromagnetic field, respectively, and shifted and rebuilt frequency spectra. Disturbed electrodynamic interaction forces between cancer and healthy cells may favor local invasion and metastasis. A therapeutic strategy of targeting dysfunctional mitochondria for restoration of their physiological functions makes it possible to switch on the natural apoptotic pathway blocked in cancer transformed cells. Experience with dichloroacetate in cancer treatment and reestablishment of the healthy state may help in the development of novel effective drugs aimed at the mitochondrial function.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".