Injected matrix stimulates myogenesis and regeneration of mouse skeletal muscle after ischaemic injury
Bibliographic record
Abstract
Biomaterial-guided regeneration represents a novel approach for the treatment of myopathies. Revascularisation and the intramuscular extracellular matrix are important factors in stimulating myogenesis and regenerating muscle damaged by ischaemia. In this study, we used an injectable collagen matrix, enhanced with sialyl LewisX (sLeX), to guide skeletal muscle differentiation and regeneration. The elastic properties of collagen and sLeX-collagen matrices were similar to those of skeletal muscle, and culture of pluripotent mESCs on the matrices promoted their differentiation into myocyte-like cells expressing Pax3, MHC3, myogenin and Myf5. The regenerative properties of matrices were evaluated in ischaemic mouse hind-limbs. Treatment with the sLeX-matrix augmented the production of myogenic-mediated factors insulin-like growth factor (IGF)-1, and IGF binding protein-2 and -5 after 3 days. This was followed by muscle regeneration, including a greater number of regenerating myofibres and increased transcription of Six1, M-cadherin, myogenin and Myf5 after 10 days. Simultaneously, the sLeX-matrix promoted increased mobilisation and engraftment of bone marrow-derived progenitor cells, the development of larger arterioles and the restoration of tissue perfusion. Both matrix treatments tended to reduce maximal forces of ischaemic solei muscles, but sLeX-matrix lessened this loss of force and also prevented muscle fatigue. Only sLeX-matrix treatment improved mobility of mice on a treadmill. Together, these results suggest a novel approach for regenerative myogenesis, whereby treatment only with a matrix, which possesses an inherent ability to guide myogenic differentiation of pluripotent stem cells, can enhance the endogenous vascular and myogenic regeneration of skeletal muscle, thus holding promise for future clinical use.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".