Structured Water and Cancer: Orthomolecular Hydration Therapy
Bibliographic record
Abstract
It is a common practice to envision cancer exclusively as a genetic disease, however, in our perspective, changes in gene expression leading to malignancy are secondary to biochemical disturbances and at its core we consider cancer as a metabolic energetic disease. In this regard, incongruence with the concept of the bioenergetic theory of carcinogenesis, we propose structured water (EZ water), as an element that facilitates the correction of the fundamental energy disruption and the reestablishment of health. The prime approach for this therapy would be to infuse kosmotropic osmolytes by the intravenous route to improve the physiological conditions and promote the reduction of cancer growth with no side effects. By doing so, we could expect that the cells will regain their communication ability with a functioning Ras and p53 proteins and other metabolic and transcription factors. The end goal is to support the cell in overcoming its low-energy anaerobic glycolytic metabolism that favors uncontrolled growth and regain the full energetic potential of oxidative phosphorylation that supports controlled cell division and differentiation. To achieve this goal, we propose the use of metabolic correction to improve the membrane function of the mitochondria. The use of precursors, enzymatic cofactors, and a variety of biological response modifiers which includes structured water and its kosmotropic properties in enzyme dynamics are part of the metabolic correction concept.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".