Zinc Supplementation Protects THP‐1 Cells against Etoposide‐Induced Apoptosis through Promoting Metallothionein Expression
Bibliographic record
Abstract
Chemotherapy drugs can cause detrimental side effects on noncancerous cells (e.g. hematopoietic cells). Strategies to minimize these side effects include dosage reduction and delay in chemotherapy. Consequently, the effectiveness of chemotherapy could be seriously compromised. This study was to determine whether zinc supplementation increased the tolerance of human blood mononuclear THP‐1 cells to etoposide and to explore the possible cellular defense mechanisms involved. THP‐1 cells were cultured in DMEM + 10% FBS for 3 days followed by zinc treatment (ZnSO 4 ) at 0, 25, 50 or 100 μM for 24 h. Zinc supplementation resulted in a dose‐dependent increase in total cellular zinc concentration and the abundance of the labile intracellular pool of zinc ( p < 0.05). Zinc supplementation at 50 and 100 μM increased MT1x and MT2a mRNA levels, while zinc supplementation at 100 μM increased MT3a mRNA level ( p < 0.05). When THP‐1 cells were exposed to etoposide (2μM) for 24 h, zinc supplementation at 50 and 100 μM reduced etoposide‐induced apoptosis by 15 and 33%, respectively, with an inverse correlation ( r 2 = 0.99). Zinc supplementation also reduced caspase‐3 and ‐9 activities, and etoposide‐induced oxidative DNA damage ( p < 0.05). Overall, these results showed that zinc supplementation reduced etoposide‐induced cytotoxicity in THP‐1 cells, likely though induction of metallothionein synthesis. ( Supported by NSERC ).
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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".