Inducible nitric oxide synthase (iNOS) in muscle wasting syndrome, sarcopenia, and cachexia
Bibliographic record
Abstract
Muscle atrophy-also known as muscle wasting-is a debilitating syndrome that slowly develops with age (sarcopenia) or rapidly appears at the late stages of deadly diseases such as cancer, AIDS, and sepsis (cachexia). Despite the prevalence and the drastic detrimental effects of these two syndromes, there are currently no widely used, effective treatment options for those suffering from muscle wasting. In an attempt to identify potential therapeutic targets, the molecular mechanisms of sarcopenia and cachexia have begun to be elucidated. Growing evidence suggests that inflammatory cytokines may play an important role in the pathology of both syndromes. As one of the key cytokines involved in both sarcopenic and cachectic muscle wasting, tumor necrosis factor α (TNFα) and its downstream effectors provide an enticing target for pharmacological intervention. However, to date, no drugs targeting the TNFα signaling pathway have been successful as a remedial option for the treatment of muscle wasting. Thus, there is a need to identify new effectors in this important pathway that might prove to be more efficacious targets. Inducible nitric oxide synthase (iNOS) has recently been shown to be an important mediator of TNFα-induced cachectic muscle loss, and studies suggest that it may also play a role in sarcopenia. In addition, investigations into the mechanism of iNOS-mediated muscle loss have begun to reveal potential therapeutic strategies. In this review, we will highlight the potential for targeting the iNOS/NO pathway in the treatment of muscle loss and discuss its functional relevance in sarcopenia and cachexia.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".