Myoblasts Derived From Normal hESCs and Dystrophic hiPSCs Efficiently Fuse With Existing Muscle Fibers Following Transplantation
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) and human-induced pluripotent stem cells (hiPSCs) have an endless self-renewal capacity and can theoretically differentiate into all types of lineages. They thus represent an unlimited source of cells for therapies of regenerative diseases, such as Duchenne muscular dystrophy (DMD), and for tissue repair in specific medical fields. However, at the moment, the low number of efficient specific lineage differentiation protocols compromises their use in regenerative medicine. We developed a two-step procedure to differentiate hESCs and dystrophic hiPSCs in myogenic cells. The first step was a culture in a myogenic medium and the second step an infection with an adenovirus expressing the myogenic master gene MyoD. Following infection, the cells expressed several myogenic markers and formed abundant multinucleated myotubes in vitro. When transplanted in the muscle of Rag/mdx mice, these cells participated in muscle regeneration by fusing very well with existing muscle fibers. Our findings provide an effective method that will permit to use hESCs or hiPSCs for preclinical studies in muscle repair.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".