Role of Oxygen and Its Radicals in Peripheral Nerve Regeneration: From Hypoxia to Physoxia to Hyperoxia
Bibliographic record
Abstract
Oxygen is compulsory for mitochondrial function and energy supply, but it has numerous more nuanced roles. The different roles of oxygen in peripheral nerve regeneration range from energy supply, inflammation, phagocytosis, and oxidative cell destruction in the context of reperfusion injury to crucial redox signaling cascades that are necessary for effective axonal outgrowth. A fine balance between reactive oxygen species production and antioxidant activity draws the line between physiological and pathological nerve regeneration. There is compelling evidence that redox signaling mediated by the Nox family of nicotinamide adenine dinucleotide phosphate (NADPH) oxidases plays an important role in peripheral nerve regeneration. Further research is needed to better characterize the role of Nox in physiological and pathological circumstances, but the available data suggest that the modulation of Nox activity fosters great therapeutic potential. One of the promising approaches to enhance nerve regeneration by modulating the redox environment is hyperbaric oxygen therapy. In this review, we highlight the influence of various oxygenation states, i.e., hypoxia, physoxia, and hyperoxia, on peripheral nerve repair and regeneration. We summarize the currently available data and knowledge on the effectiveness of using hyperbaric oxygen therapy to treat nerve injuries and discuss future directions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".