Cross-talk between skeletal muscle and immune cells: muscle-derived mediators and metabolic implications
Bibliographic record
Abstract
Skeletal muscles contain resident immune cell populations and their abundance and type is altered in inflammatory myopathies, endotoxemia or different types of muscle injury/insult. Within tissues, monocytes differentiate into macrophages and polarize to acquire pro- or anti-inflammatory phenotypes. Skeletal muscle macrophages play a fundamental role in repair and pathogen clearance. These events require a precisely regulated cross-talk between myofibers and immune cells, involving paracrine/autocrine and contact interactions. Skeletal muscle also undergoes continuous repair as a result of contractile activity that involves participation of myokines and anti-inflammatory input. Finally, skeletal muscle is the major site of dietary glucose disposal; therefore, muscle insulin resistance is essential to the development of whole body insulin resistance. Notably, muscle inflammation is emerging as a potential contributor to insulin resistance. Recent reports show that inflammatory macrophage numbers within muscle are elevated during obesity and that muscle cells in vitro can mount autonomous inflammatory responses under metabolic challenge. Here, we review the nature of skeletal muscle inflammation associated with muscle exercise, damage, and regeneration, endotoxin presence, and myopathies, as well as the new evidence of local inflammation arising with obesity that potentially contributes to insulin resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".