Oxygen toxicity and the health and survival of eukaryote cells: A new piece is added to the puzzle
Bibliographic record
Abstract
My first thought was “Superoxide dismutase: of what use is that to a cell?” It was 1976 and I was a graduate student in search of a topic for a “literature” seminar. Fortunately I chose to present the story of this exotic-sounding protein, “SOD,” to my colleagues and was rewarded with the beginnings of a long-term fascination with the concept that our aerobic (O2-based) lifestyle is fraught with oxidative (O2-based) hazards. This concept is now firmly linked to topics of enduring interest and importance such as aging, cancer, ischemia (anoxic tissue damage), aerotolerance, and immune system function. In this issue of PNAS, Luk et al . (1), using brewer's yeast cells, show how a key eukaryote antioxidant enzyme, mitochondrial Mn-cofactored SOD (SOD2), acquires its Mn cofactor and possibly its active conformation via a previously uncharacterized nuclear-encoded protein, thus providing both a tool for experimental oxy-radical stress manipulations and a more detailed understanding of how eukaryote cell oxygen defenses are assembled. By the 1960s it was clear that reactive oxygen species (ROS), namely, partially reduced O2 derivatives such as hydrogen peroxide (H2O2), the hydroxyl radical (*OH), and the superoxide anion radical (O2-) formed by ionizing radiation could oxidize and damage cell proteins, lipids, and nucleic acids. However, there was little convincing evidence that either O2-mediated damage or ROS were produced by normal respiratory (dioxygen-reducing) cellular processes. This changed in 1969 when McCord and Fridovich (2) demonstrated that the catalytic activity of a long-known red blood cell protein of unknown function, erythrocuprein, was to specifically dismute O2- to H2O2 and O2. If O2- were not a real and present danger, why would red cells contain an enzyme specifically degrading it? …
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".