Electrochemical Monitoring of the Superoxide Anion Radical with Quercetin and Metallo-Quercetin Complexes
Bibliographic record
Abstract
Excess production of reactive oxygen species (ROS), such as the superoxide anion radical (O2˙–) can contribute to oxidative stress, and subsequently the onset of some pathological diseases. The free radical, O2˙–, is naturally generated in the body via a single electron reduction of molecular oxygen.1-3 Phenolic compounds such as flavonoids have strong antioxidant properties against ROS, including O2˙–.1,3 Due to their ability to donate an electron to the ROS, antioxidants actively act as free radical scavengers, which allows for prevention of cell damage and related health issues induced by ROS production.1 In addition to antioxidant properties, flavonoids such as quercetin (QCR) also play a role as metal ion chelators, by which their coordination with biologically relevant metal ions results in the formation of metallo-flavonoid complexes.2 Metal ions are known to regulate ROS activity via Fenton reactions, which may also influence the antioxidant activity and capacity of the flavonoid upon complexation. The role of metal ions and metallo-flavonoid complexes as antioxidants towards O2˙– is not fully understood. Herein, cyclic voltammetry (CV) was employed for electrochemical generation of the O2˙–/O2 redox couple.4 The anodic and cathodic peaks associated with the redox couple were monitored prior and post interaction with QCR and its metallo-QCR complexes. Overall, CV data show a decrease in anodic and cathodic peak currents, demonstrating varying extents of antioxidant activity and O2 reactivity with the antioxidants. References [1] N. L. Zabik, S. Anwar, I Ziu, S. Martic-Milne, Electrochim. Acta, 296, 174-180 (2019). [2] M. M. Kasprzak, A. Erxleben, J. Ochocki, RSC Adv., 5, 45853-45877 (2015). [3] S. Ahmed, F. Shakeel, Czech J. Food Sci., 30, 153-163 (2012). [4] T. Lewis, W. Wallace, F. Dingman Peterson, S. Rafferty, S. Martic, Electrochem. Sci. Adv., e2100054 (2021).
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".