Comparison of 35 trace elements content in malignant breast tumors with their content in the normal female mammary gland: Original data and a mini-review
Bibliographic record
Abstract
Objective: In many countries, including Russia, breast cancer ranks first in the incidence of cancers in women. The etiology of this disease remains largely unclear, but there is evidence indicating that disturbances in the somatic homeostasis of trace elements may be involved in the process of oncogenesis. Therefore, this study was aimed at identifying changes in the content of trace elements during malignant transformation of breast tissue.Methods: For this purpose, an effective method of small sample analysis by means of inductively coupled plasma mass spectrometry was developed. The method makes it possible to determine the content of 35 trace elements in microsamples (with mass ≥ 10 mg) of breast tissue obtained by puncture biopsy. With the help of this technique, the samples of cancerous (n = 43) and normal (n = 38) breast tissue were studied.Results: In malignant breast tissue, the content of Al, As, B, Cd, Co, Cs, Cu, Mg, Mn, Mo, Ni, Rb, Se, Sr, Ti, Tl, U, V, Zn, and Zr was higher, while the content of Ge, Pb, Sb and Th was lower than in healthy gland tissue. All the identified differences were statistically significant.Conclusions: The significant disruption of somatic homeostasis of trace elements resulting from malignant transformation of breast tissue has been described, but its cause has not been determined, so additional research is required. Further the method we employ, which we have developed and described here, requires tissue samples weighing only a few milligrams, so it is possible to use it with tissue obtained from puncture tissue biopsies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".