Determination of Some Trace Elements in Breast Cancer Serum by Atomic Absorption Spectroscopy
Bibliographic record
Abstract
In the breast cancer significant differences occurs in the normal distribution of the trace elements, playing an important role in carcinogenic process. The aim of this study was to investigate the serum levels of some trace elements (Chromium, Cadmium, Manganese, Cobalt, Nickel, Selenium, Zinc, Iron, Copper and Magnesium) by using flame atomic absorption spectroscopy (FAAS). The present study included 150 females, the participates were divided into two main groups, control group which consisted of 75 apparently healthy females, 75patients with breast cancer group .The serum levels of Chromium (tcal2.9631 ,ttab1.960), Cadmium (tcal2.0798 ,ttab1.960), Manganese (tcal18.5676, ttab1.960), Selenium (tcal2.2759,ttab1.960), Iron (tcal2.9296,ttab1.960), Copper (tcal4.0869, ttab1.960),Magnesium (tcal2.6648, ttab1.960), Cobalt (tcal3.8615 , ttab1.960) and Zinc (tcal7.0160, ttab1.960) were statistically significant higher in the breast cancer patients group than the control group. Similarly, Nickel (tcal1.5988,ttab1.960) levels showed significant lower level in breast cancer patients group. On the other hand, the higher levels of trace elements could lead to formation of free radicals or other reactive oxygen species. The evaluation of these trace elements in serum maybe used as helpful tool in diagnosis of the 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".