Spontaneous Tumor Regression and Reversion: Insights and Associations with Reduced Dietary Phosphate
Bibliographic record
Abstract
Tumors that spontaneously shrink from unknown causes in tumor regression, and that return to normal cells in tumor reversion, are phenomena with the potential to contribute new knowledge and novel therapies for cancer patient survival. Tumorigenesis is associated with dysregulated phosphate metabolism and an increased transport of phosphate into tumor cells, potentially mediated by phosphate overload from excessive dietary phosphate intake, a significant problem in Western societies. This paper proposes that reduced dietary phosphate overload and reregulated phosphate metabolism may reverse an imbalance of kinases and phosphatases in cell signaling and cellular proliferation, thereby activating autophagy in tumor regression and reversion. Dietary phosphate can also be reduced by sickness-associated anorexia, fasting-mimicking diets, and other diets low in phosphate, all of which have been associated with tumor regression. Tumor reversion has also been demonstrated by transplanting cancer cells into a healthy microenvironment, plausibly associated with normal cellular phosphate concentrations. Evidence also suggests that the sequestration and containment of excessive phosphate within encapsulated tumors is protective in cancer patients, preventing the release of potentially lethal amounts of phosphate into the general circulation. Reducing dietary phosphate overload has the potential to provide a novel, safe, and effective reversion therapy for cancer patients, and further research is warranted.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".