CD8 T Cells Are Involved in Skeletal Muscle Regeneration through Facilitating MCP-1 Secretion and Gr1high Macrophage Infiltration
Bibliographic record
Abstract
Inflammatory microenvironments play a key role in skeletal muscle regeneration. The infiltration of CD8 T cells into injured muscle has been reported. However, the role of CD8 T cells during skeletal muscle regeneration remains unclear. In this study, we used cardiotoxin-induced mouse skeletal muscle injury/regeneration model to investigate the role of CD8 T cells. Muscle regeneration was impaired and matrix deposit was increased in CD8α-deficient mice compared with wild-type (WT) mice whose CD8 T cells were infiltrated into damaged muscle after cardiotoxin injection. Adoptive transfer of CD8 T cells to CD8α-deficient mice improved muscle regeneration and inhibited matrix remodeling. Compared with WT mice, CD8α deficiency limited the recruitment of Gr1(high) macrophages (MPs) into muscle, resulting in the reduction of satellite cell number. The expression of MCP-1 (MCP-1/CCL2), which regulates the migration of Gr1(high) MPs, was reduced in CD8α-deficient mice compared with WT mice. Coculture CD8 T cells with MPs promoted MCP-1 secretion. The i.m. injection of MCP-1 markedly promoted the recruitment of Gr1(high) MPs and improved muscle regeneration in CD8α-deficient mice. We conclude that CD8 T cells are involved in skeletal muscle regeneration by regulating the secretion of MCP-1 to recruit Gr1(high) MPs, which facilitate myoblast proliferation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".