The use of bio‐metal concentrations correlated with clinical prognostic factors to assess human breast tissues
Why this work is in the frame
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Bibliographic record
Abstract
Worldwide, breast cancer is the most frequently diagnosed cancer in women and the leading cause of cancer death among women. The concentrations of bio‐metals are crucial for the homeostasis of human health and are being shown to have significantly different concentrations when comparing human cancer tissue and normal tissue. This is the first study that correlates the findings of the differences in the levels of certain elements between individual tumours, to the clinical prognostic factors such as oestrogen receptor (ER) status, lymph node status, tumour size, grade, menopause status, human epidermal growth factor receptor 2 status, epidermal growth factor receptor status, relapsed status and survival status. Micro probe synchrotron radiation X‐ray fluorescence techniques have been used to determine the localization and the relative concentrations of Zn, Cu, Fe and Ca in 128 formalin‐fixed paraffin‐embedded invasive ductal breast cancer (IDC) samples and normal surrounding breast tissue. The statistical analysis reveals a significant increase in the levels of Ca, Fe, Cu and Zn concentrations by 85%, 20%, 23% and 117%, respectively, in IDC tissue when compared to the normal breast tissue. Our study shows that increased relative expressions of Zn, Fe and Ca are all associated with ER positive breast cancers and also indicates that the imbalance in iron concentration (deficiency) should be viewed as an important risk factor that is associated with aggressive features of the cancer. Characterisation of the difference of bio‐metals in tumour to normal regions will help in selecting treatment for breast cancer with novel agents that chelate iron or zinc. Copyright © 2013 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it