Study of a Nano-Oleuropein’s Effect on the TCA Cycle`s Protein Expression in the Breast Cancer Cell Line Using Proteomics
Bibliographic record
Abstract
Breast cancer is the most common cancer and a common cause of death, which occurs due to cancer among women in the world. Cancer cells need a lot of energy to their uncontrolled growth, so it seems that the expression of the enzyme in the Krebs cycle is changing. There are some reports about mutations and altered expression of succinate dehydrogenase, fumarate Hydratase, and isocitrate dehydrogenase in human cancers. This research aimed to investigate the role of magnetite nanoparticle Oleuropein on the Krebs cycle proteins expression on the breast cancer cell line. Oleuropein is one of the polyphenolic components in olive trees and has some benefits in some diseases, including cancer. In addition to testing the viability test MTT (3- 4,5 Dimethylthiazol-2-yl -2,5-diphenyltetrazolium bromide) assay, in three levels of Oleuropein 0ppm, 300ppm, 600ppm proteomics analysis was also performed in cell line MCF7 in this study. The results of differential protein spots identification into two-dimensional electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS-MALDI-TOF-TOF), showed that fumarylacetoacetate hydrolase, succinate-coenzyme A ligase and isocitrate dehydrogenase1 are differential proteins upregulated after treated with 300ppm and 600ppm of oleuropein. It seems that Nano Oleuropein is a booster of Krebs cycle with upregulation of Fumarylacetoacetase, succinate-CoA ligase, and isocitrate dehydrogenase1. Uncoordinated Overexpression of some Krebs cycle protein can be one of the inhibition mechanisms on the breast cancer cell line under Oleuropein treatment.
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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 | 0.000 | 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) | 0.001 | 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".