Quantifying intracellular glutathione levels in chemotherapeutic sensitive and resistant cells
Bibliographic record
Abstract
Glutathione is an important cellular molecule for its antioxidant abilities and has a role in multidrug resistance. Literature has attempted to quantify glutathione using a variety of techniques and has shown conflicting results of glutathione content between chemotherapeutic sensitive and resistant cell lines. The purpose of this study was to develop a universal method of glutathione quantification in HeLa and multidrug resistant variant HeLa cells in order to establish the relationship between glutathione content and multidrug resistance via multidrug resistance associated protein-1 membrane transporters. To do so, we have employed three analytical techniques: Western blotting in order to confirm resistance in multidrug resistant variant HeLa cells, high-performance liquid chromatography analysis with use of an IS to obtain an absolute value of glutathione content and flow cytometry analysis of chloromethyl fluorescein diacetate fluorescence to corroborate our high-performance liquid chromatography findings. This study was unable to quantify glutathione concentration using high-performance liquid chromatography analysis due to instrumental and column problems but demonstrated proof of concept on an amine and C-18 column. Our findings confirmed multidrug resistance associated protein-1 over-expression and lower glutathione content by fluorescence in multidrug resistant variant HeLa cells. This study contributes to the conflicting debate of glutathione content by establishing its support that lower glutathione is observed in resistant variants of human cancer cells.
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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 | 0.001 |
| 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".