Intracellular Zinc Excess as One of the Main Factors in the Etiology of Prostate Cancer
Bibliographic record
Abstract
Numerous studies show that prevalence of prostate cancer (PCa) drastically increases with age, these malignant tumours are mainly formed in the peripheral zone of the prostate gland, and a high intake of red meat is associated with a statistically significant elevation in risk of PCa. The factors which cause all these well-specified features of the PCa are currently unclear. Here we describe one factor which can play an important role in etiology of malignant transformation of the prostate and is connected with the above-mentioned features of PCa. It is hypothesized that the prostatic intracellular Zn concentrations are probably one of the most important factors in the etiology of PCa. For an endorsement of our standpoint the estimation of changes of intracellular Zn concentrations over males lifespan was obtained using morphometric and Zn content data for the peripheral zone of prostate tissue, as well as Zn concentration in prostatic fluid. It was shown that the Zn concentrations in prostatic cells for men aged over 45 years are 10-fold higher than in those aged 18 to 30 years and this excessive accumulation of Zn may disturb the cells functions, resulting in cellular degeneration, death or malignant transformation.We hypothesize this excessive intracellular Zn concentration in cells of the prostate gland periphery has previously unrecognized and most important consequences, associated with PCa.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".