Molecular Characterization of a Superoxide-Generating NAD(P)H Oxidase in the Ventilatory Muscles
Bibliographic record
Abstract
The molecular sources of reactive oxygen species (ROS) in skeletal muscles are not well understood. We hypothesized that nonphagocyte NAD(P)H oxidase could be a source of ROS in muscle fibers. We thus investigated the existence, structure, and contribution of nonphagocyte NAD(P)H oxidase to ROS production in rat skeletal muscles. ROS production and NAD(P)H oxidase activity were evaluated by lucigenin-enhanced chemiluminescence and NADH consumption rate, whereas enzyme composition was monitored by reverse transcription-polymerase chain reaction and immunoblotting. Basal O(-)(2) production in muscle strips from normal rats averaged 1.4 nmol/mg per 10 min and increased to approximately 18 nmol/mg per 10 min in the presence of NADH. Muscle O(-)(2) production and NADH consumption were inhibited by Tiron, superoxide dismutase, apocynin, and diphenyleneiodonium but not by inhibitors of cyclo-oxygenases, xanthine oxidase, nitric oxide synthases (NOS), and mitochondrial enzymes. We detected mRNA and proteins of p22(phox), gp91(phox), p47(phox), and p67(phox) subunits in normal rat muscles. These subunits were localized in close proximity to the sarcolemma. Induction of sepsis in rats doubled muscle O(-)(2) production with no major changes in muscle NADPH oxide subunit expression. In lipopolysaccharide-treated but not in control muscles, O(-)(2) production was increased significantly by NOS inhibition. We conclude that a constitutively active NAD(P)H oxidase enzyme complex exists in normal skeletal muscle fibers and contributes to ROS production. In septic rats, this production is increased but measurable O(-)(2) is reduced by enhanced NO production.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".