Tubulyzine<sup>®</sup>, a novel tri-substituted triazine, prevents the early cell death of transplanted myogenic cells and improves transplantation success
Bibliographic record
Abstract
Myoblast transplantation (MT) is a potential therapeutic approach for several muscular dystrophies. A major limiting factor is that only a low percentage of the transplanted myoblasts survives the procedure. Recent advances regarding how and when the myoblasts die indicate that events preceding actual tissue implantation and during the first days after the transplantation are crucial. Myoseverin, a recently identified tri-substituted purine, was shown to induce in vitro the fission of multinucleated myotubes and affect the expression of a variety of growth factors, and immunomodulation, extracellular matrix-remodeling, and stress response genes. Since the effects of myoseverin are consistent with the activation of pathways involved in wound healing and tissue regeneration, we have investigated whether pretreatment and co-injection of myoblasts with Tubulyzine (microtubule lysing triazine), an optimized myoseverin-like molecule recently identified from a triazine library, could reduce myoblast cell death following their transplantation and consequently improves the success of myoblast transplantation. In vitro, using annexin-V labeling, we showed that Tubulyzine (5 microM) prevents normal myoblasts from apoptosis induced by staurosporine (1 microM). In vivo, the pretreatment and co-injection of immortal and normal myoblasts with Tubulyzine reduced significantly cell death (assessed by the radio-labeled thymidine of donor DNA) and increased survival of myoblasts transplanted in Tibialis anterior (TA) muscles of mdx mice, thus giving rise to more hybrid myofibers compared to transplanted untreated cells. Our results suggest that Tubulyzine can be used as an in vivo survival factor to improve the myoblast-mediated gene transfer approach.
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) | 0.000 | 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) | 0.001 | 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".