Assessment of Muscle Function Following hiPSC‐Derived Myoblast Transplantation in Dystrophic Mice
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Bibliographic record
Abstract
Muscular dystrophies are caused by genetic variants in genes encoding for proteins important for muscle structure or function, leading to a loss of muscle integrity and muscle wasting. To this day, no cure has been found for these diseases. Different therapeutic approaches are under intensive investigation. Cellular therapy has been extensively studied for diseases such as Duchenne Muscular Dystrophy, a debilitating disease caused by a mutation in the DMD gene, encoding for the dystrophin protein. Healthy myogenic cells transplanted into dystrophic muscles have the potential to engraft at long-term and fuse to donate their nuclei to the dystrophin-deficient myofibers, thereby restoring normal gene expression. Despite promising preclinical studies, the clinical trials had limited success so far due to many technical limitations. The recent technological advances in induced-pluripotent stem cells and genome editing opened new opportunities in this field. One of the keys to efficiently translate these new technologies into clinical benefits is to use relevant endpoints for preclinical studies. Considering that dystrophic muscles are susceptible to contraction-induced injury, the assessment of their resistance to repeated eccentric contractions is an optimal outcome to evaluate their functional recovery following cell transplantation. This protocol describes the procedure to generate induced-pluripotent stem cell-derived myoblasts, transplant these cells into skeletal muscle of immunosuppressed dystrophic mice, and assess muscle function in situ by measuring the resistance of the transplanted muscle to repeated eccentric contractions. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Generation of hiPSC-derived myoblasts. Basic Protocol 2: Transplantation of hiPSC-derived myoblasts in skeletal muscle of dystrophic mice. Basic Protocol 3: Assessment of muscle function in situ.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it