Zinc stable isotopes in urine as diagnostic for cancer of secretory organs
Bibliographic record
Abstract
Breast, prostate, and pancreatic cancers alter the zinc (Zn) metabolism. Combined analyses of urinary Zn concentrations [Zn] and Zn stable isotope compositions (δ66Zn) may provide a non-invasive approach for tracing malignancy-induced Zn dyshomeostasis. In this study, we measured [Zn] and δ66Zn in urine from prostate (n = 22), breast (n = 16), and from women with benign breast disease (n = 14) and compared those with age-matched healthy controls (22-49 years or 50+ years) and published data for pancreatic cancer (n = 17). Our results show that cancer-induced changes are reflected in higher urinary [Zn] and lower urinary δ66Zn for pancreatic and prostate cancer and benign breast disease when compared with healthy controls. For prostate cancer, the progression of low [Zn] and high δ66Zn for patients of low-risk disease toward high [Zn] and low δ66Zn for the higher risk patients demonstrates that [Zn] and δ66Zn in urine could serve as a reliable prognostic tool. Urinary excretion of isotopically light Zn by patients with prostatic and pancreatic cancer is probably the result of increased reactive oxygen species in cancerous cells, which limits the scavenging of hydroxyl radicals and thus facilitates the oxidation of metalloproteins with sulfur-rich ligands. Urine from breast cancer patients shows undistinguishable δ66Zn to healthy controls, implying that the expression of metalloproteins with sulfur-rich ligands is stronger in breast cancer tissues. In conclusion, urinary δ66Zn may provide a non-invasive diagnostic tool for pancreatic cancer and support disease prognosis for prostate cancer. These findings should translate to comprehensive transverse and longitudinal cohort studies in future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".