Determination of Zn, Cu, Fe and Mn in Muscle Cells as Potential Markers of Oxidative Stress by Laser Ablation and Solution Based ICP-MS
Bibliographic record
Abstract
Oxidative stress is imbalance between oxidant and antioxidant levels in living systems. Human cells are protected from reactive oxygen species (ROS) by endogenous enzymatic antioxidants, such as superoxide dismutase (SOD) and catalase. Most of these compounds require particular redox metals in their structures as cofactors to allow them to scavenge the free radicals, in particular Cu, Zn or Mn-with SOD and Fe with catalase. The aim of this study was to quantify these metals in human cells to evaluate their effectiveness as novel biomarkers for measuring oxidative stress. The metals (Zn, Cu, Fe, Mn) were measured in vitro in skeletal muscle cells (C2C12) which were incubated under hypoxia or hyperoxia conditions generated by varying oxygen levels from 1% - 60% for 24 and 48 hours. Two methods were used to perform the analyses. Solution-based inductively coupled plasma mass spectrometry (ICP-MS) was applied to quantify Zn, Cu, Fe and Mn in cell populations, and laser ablation (LA)-ICP-MS was employed to compare their relative levels in individual cells. The data acquired from both techniques were positively correlated confirming the validity of the two approaches. The results showed that the concentration of the measured elements increased dramatically in cells grown at 25% - 60% O2, the most significant increase being in Cu at 60% O2. None showed any increase at 5% - 15% O2, indicating normoxia states. At 1% O2, all elements, except Fe, showed a significant increase and the most remarkable growth was in Mn. Increasing the incubation time to 48 hours had differing effects on the elements. Zn and Cu concentrations were unaffected by increasing incubation time except at 60% O2 where they showed further growth. In contrast, Mn concentration grew sharply for oxygen levels of 30% - 50% with no further effect at 1%, while Fe concentration decreased at 1% O2 and grew steadily for oxygen levels of 5% - 60%. It can be concluded that all four elements were significantly affected by stress conditions applied to cells, but at different rates. Importantly, this paper describes a novel method for estimating oxidative stress in cells based on the determination of redox elements in single cells and cell populations using ICP-MS.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".