Zinc presence in invasive ductal carcinoma of the breast and its correlation with oestrogen receptor status
Bibliographic record
Abstract
Zinc is known to play an important role in many cellular processes, and the levels of zinc are controlled by specific transporters from the ZIP (SLC39A) influx transporter group and the ZnT (SLC30A) efflux transporter group. The distribution of zinc was measured in 59 samples of invasive ductal carcinoma of breast using synchrotron radiation micro probe x-ray fluorescence facilities. The samples were formalin fixed paraffin embedded tissue micro arrays (TMAs) enabling a high throughput of samples and allowing us to correlate the distribution of trace metals with tumour cell distribution and, for the first time, important biological variables. The samples were divided into two classes, 34 oestrogen receptor positive (ER+ve) and 25 oestrogen receptor negative (ER-ve) based on quantitative immunohistochemistry assessment. The overall levels of zinc (i.e. in tumour and surrounding tissue) in the ER+ve samples were on average 60% higher than those in the ER-ve samples. The zinc levels were higher in the ER+ve tumour areas compared to the ER-ve tumour areas with the mean levels in the ER+ve samples being approximately 80% higher than the mean ER-ve levels. However, the non-tumour tissue regions of the samples contained on average the same levels of zinc in both types of breast cancers. The relative levels of zinc in tumour areas of the tissue were compared with levels in areas of non-tumour surrounding tissue. There was a significant increase in zinc in the tumour regions of the ER+ve samples compared to the surrounding regions (P < 0.001) and a non-significant increase in the ER-ve samples. When comparing the increase in zinc in the tumour regions expressed as a percentage of the surrounding non-tumour tissue zinc level in the same sample, a significant difference between the ER+ve and ER-ve samples was found (P < 0.01).
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".