Induction of Anoikis Following Myoblast Transplantation into SCID Mouse Muscles Requires the Bit1 and FADD Pathways
Bibliographic record
Abstract
Seventy-five percent of the myoblasts transplanted in the mouse muscle die during the first 4 days following transplantation. The purpose of this study was to determine if anoikis plays a role in this phenomenon. Survival and proliferation of myoblasts in vitro were determined by Hoescht-PI labeling and cell counts respectively. In vivo cell survival and proliferation were quantified by injecting human male myoblasts labeled with (14)C-thymidine in SCID mouse muscles. Survival and proliferation of the transplanted myoblasts were evaluated by scintigraphy and quantitative PCR of human Y chromosomal DNA. Inclusion of the extracellular matrix protein fibronectin enhanced transplanted myoblast survival by 1.7-fold while vitronectin improved their proliferation by 1.8-fold. Reductions in FADD and Bit1 expression reduced anoikis in vitro and improved the injected myoblast survival in vivo. Ectopic expression of the anti-apoptotic protein Bcl-2 completely abolished myoblast anoikis in vitro and enhanced cell survival by 3.1-fold in vivo. Cell death following transplantation appears to me mediated in part by anoikis. Inclusion of extracellular matrix proteins enhanced both survival and proliferation. Reduced expression of the proapoptotic proteins Bit1 and FADD or overexpression of Bcl-2 improved myoblast survival.
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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 | 0.001 | 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) | 0.002 | 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".