The use of bio‐metal concentrations correlated with clinical prognostic factors to assess human breast tissues
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 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".