Labile Iron Controllng Therapy To Prevent Cancers and Alzheimer’s Disease
Bibliographic record
Abstract
It has been pointed out that so-called plasma labile iron (or NTBI = non-transferrin-bound iron) is the essential cause of many cancers and Alzheimer’s disease, but its actual properties have not been clarified. Nishida have concluded that the structure of highly toxic plasma labile iron is an oxo-bridged diiron species through the experimental results using many artificial iron chelating agents. Based on this result, he synthesized non-toxic chelating agents (SP9 and SP10) that prevent the formation of the oxo-bridged di-iron species, and found that these chelates effectively suppress the proliferation of various types of cancers, proving the correctness of his conclusion. He also obtained many related results which support that SP9 and SP10 can be applied to prevent Alzheimer’s disease. Furthermore, Nishida have demonstrated that some zinc ions can remove these dangerous oxo-bridged diiron species through the formation of iron deposition. Nishida have shown that his universal healthcare, so-called “Labile Iron Controllng Therapy” (LICT), which prevents cancers and Alzheimer’s disease through suppressing the formation of dangerous oxo-bridged di-iron species in the human body, can be achieved through daily diet that contain suitable zinc ions and natural lignin derivatives.
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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 | 0.001 |
| 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".