Zinc stable isotope analysis reveals Zn dyshomeostasis in benign tumours, breast cancer, and adjacent histologically normal tissue
Bibliographic record
Abstract
The disruption of Zn homeostasis has been linked with breast cancer development and progression. To enhance our understanding of changes in Zn homeostasis both inside and around the tumour microenvironment, Zn concentrations and isotopic compositions (δ66Zn) were determined in benign (BT) and malignant (MT) tumours, healthy tissue from reduction mammoplasty (HT), and histologically normal tissue adjacent to benign (NAT(BT)) and malignant tumours (NAT(MT)). Mean Zn concentrations in NAT(BT) are 5.5 µg g-1 greater than in NAT(MT) (p = 0.00056) and 5.1 µg g-1 greater than in HT (p = 0.0026). Zinc concentrations in MT are 12.9 µg g-1 greater than in HT (p = 0.00012) and 13.3 µg g-1 greater than in NAT(MT) (p < 0.0001), whereas δ66Zn is 0.17‰ lower in MT than HT (p = 0.017). Benign tumour Zn concentrations are also elevated compared to HT (p = 0.00013), but are not significantly elevated compared to NAT(BT) (p = 0.32). The δ66Zn of BT is 0.15‰ lower than in NAT(BT) (p = 0.045). The similar light δ66Zn of BT and MT compared to HT and NAT may be related to the isotopic compensation of increased metallothionein (64Zn-rich) expression by activated matrix metalloproteinase (66Zn-rich) in MT, and indicates a resultant 66Zn-rich reservoir may exist in patients with breast tumours. Zinc isotopic compositions thus show promise as a potential diagnostic tool for the detection of breast tumours. The revealed differences of Zn accumulation in healthy and tumour-adjacent tissues require additional investigation.
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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 | 0.000 |
| 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".