Comparison of the content of some chemical elements in cancerous and intact breast tissue adjacent to the tumor determined by the ICP-AES method: Original data and a mini-review
Bibliographic record
Abstract
Objective: In most countries of the world, breast cancer ranks first in the structure of oncological morbidity in women. The etiology of this disease remains largely unclear, although it is known that disturbances in the elemental homeostasis of somatic cells play a certain role in oncogenesis. The purpose of this study was to identify changes in the content of chemical elements during malignant transformation of breast tissue.Methods: For this purpose, we used the previously developed method of sample preparation, which allows determining the content of Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, P, S, Si, Sr, Ti, and Zn in micro samples of breast tissue by using atomic emission spectrometry with inductively coupled plasma. Samples of cancerous and visually unchanged breast tissue adjacent to the tumor were examined using the developed technique.Results: A significantly higher content of all the chemical elements studied, except for Si, was found in cancerous tissue compared to their content in intact tissue.Conclusions: The detected multiple increase in the content of many minor and trace elements in cancer tissue compared to adjacent intact breast tissue can be used to develop new methods for in vitro and in vivo cancer diagnostics, in which the ratios of chemical elements levels in these tissues will act as tumor markers. Further, more in-depth study and understanding of the discovered phenomenon will allow the development of new methods for the prevention and treatment of breast cancer.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".