Macrophage metabolic rewiring rejuvenates muscle Raman signatures and cellular remodeling during regrowth in aged mice
Bibliographic record
Abstract
Impaired muscle regrowth in aging is underpinned by reduced proinflammatory macrophage function and subsequently impaired muscle cellular remodeling. Macrophage phenotype is metabolically controlled through TCA intermediate accumulation and activation of HIF1A. We hypothesized that transient hypoxia following disuse in old mice would enhance macrophage metabolic inflammatory function, thereby improving muscle cellular remodeling and recovery. Old (20 months) and young adult mice (4 months) were exposed to acute (24 hour) normobaric hypoxia immediately following 14 days of hind limb unloading and assessed during early reambulation (4 and 7 days) compared to age-matched controls. Treated aged mice had improved proinflammatory macrophage profiles, muscle cellular remodeling, and functional muscle recovery to the levels of young control mice. Likewise, young adult mice had enhanced muscle remodeling and functional recovery when treated with acute hypoxia. Treatment in aged mice restored the muscle molecular fingerprint and biochemical spectral patterns (Raman spectroscopy) observed in young mice and strongly correlated with improved collagen remodeling. Finally, intramuscular delivery of hypoxia-treated macrophages recapitulated the muscle remodeling and recovery effects of whole-body hypoxic exposure in old mice. These results emphasize the role of proinflammatory macrophages during muscle regrowth in aging and highlight immunometabolic approaches as a route to improve muscle cellular dynamics and regrowth.
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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) | 0.001 | 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".