Adaptations in mitochondrial quality control and interactions with innate immune signaling within skeletal muscle: A narrative review
Bibliographic record
Abstract
Skeletal muscle health and function are essential determinants of metabolic health, physical performance, and overall quality of life. The quality of skeletal muscle is heavily dependent on the complex mitochondrial reticulum that contributes toward its unique adaptability. It is now recognized that mitochondrial perturbations can activate various innate immune pathways, such as the nucleotide-binding oligomerization domain (NOD)-like receptor protein 3 (NLRP3) inflammasome complex by propagating inflammatory signaling in response to damage-associated molecular patterns (DAMPs). The NLRP3 inflammasome is a multimeric protein complex and is a prominent regulator of innate immunity and cell death by mediating the activation of caspase-1, pro-inflammatory cytokines interleukin-1β and interleukin-18 and pro-pyroptotic protein gasdermin-D. While several studies have begun to demonstrate the relationship between various mitochondrial DAMPs (mtDAMPs) and NLRP3 inflammasome activation, the influence of various metabolic states on the production of these DAMPs and subsequent inflammatory profile remains poorly understood. This narrative review aimed to address this by highlighting the effects of skeletal muscle use and disuse on mitochondrial quality mechanisms including mitochondrial biogenesis, fusion, fission and mitophagy. Secondly, this review summarized the impact of alterations in mitochondrial quality control mechanisms following muscle denervation, aging, and exercise training in relation to NLRP3 inflammasome activation. By consolidating the current body of literature, this work aimed to further the understanding of innate immune signaling within skeletal muscle, which can highlight areas for future research and therapeutic strategies to regulate NLRP3 inflammasome activation during divergent metabolic conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".