Extracellular matrix: Brick and mortar in the skeletal muscle stem cell niche
Bibliographic record
Abstract
The extracellular matrix (ECM) is an interconnected macromolecular scaffold occupying the space between cells. Amongst other functions, the ECM provides structural support to tissues and serves as a microenvironmental niche that conveys regulatory signals to cells. Cell-matrix adhesions, which link the ECM to the cytoskeleton, are dynamic multi-protein complexes containing surface receptors and intracellular effectors that control various downstream pathways. In skeletal muscle, the most abundant tissue of the body, each individual muscle fiber and its associated muscle stem cells (MuSCs) are surrounded by a layer of ECM referred to as the basal lamina. The core scaffold of the basal lamina consists of self-assembling polymeric laminins and a network of collagens that tether proteoglycans, which provide lateral crosslinking, establish collateral associations with cell surface receptors, and serve as a sink and reservoir for growth factors. Skeletal muscle also contains the fibrillar collagenous interstitial ECM that plays an important role in determining tissue elasticity, connects the basal laminae to each other, and contains matrix secreting mesenchymal fibroblast-like cell types and blood vessels. During skeletal muscle regeneration fibroblast-like cell populations expand and contribute to the transitional fibronectin-rich regenerative matrix that instructs angiogenesis and MuSC function. Here, we provide a comprehensive overview of the role of the skeletal muscle ECM in health and disease and outline its role in orchestrating tissue regeneration and MuSC function.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".