Bibliographic record
Abstract
Mitochondria are quantifiably the most important sources of superoxide (O2●−) and hydrogen peroxide (H2O2) in mammalian cells. The overproduction of these molecules has been studied mostly in the contexts of the pathogenesis of human diseases and aging. However, controlled bursts in mitochondrial ROS production, most notably H2O2, also plays a vital role in the transmission of cellular information. Striking a balance between utilizing H2O2 in second messaging whilst avoiding its deleterious effects requires the use of sophisticated feedback control and H2O2 degrading mechanisms. Mitochondria are enriched with H2O2 degrading enzymes to desensitize redox signals. These organelles also use a series of negative feedback loops, such as proton leaks or protein S-glutathionylation, to inhibit H2O2 production. Understanding how mitochondria produce ROS is also important for comprehending how these organelles use H2O2 in eustress signaling. Indeed, twelve different enzymes associated with nutrient metabolism and oxidative phosphorylation (OXPHOS) can serve as important ROS sources. This includes several flavoproteins and respiratory complexes I-III. Progress in understanding how mitochondria generate H2O2 for signaling must also account for critical physiological factors that strongly influence ROS production, such as sex differences and genetic variances in genes encoding antioxidants and proteins involved in mitochondrial bioenergetics. In the present review, I provide an updated view on how mitochondria budget cellular H2O2 production. These discussions will focus on the potential addition of two acyl-CoA dehydrogenases to the list of ROS generators and the impact of important phenotypic and physiological factors such as tissue type, mouse strain, and sex on production by these individual sites.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".