Ischemic Central Necrosis in Pockets of Transplanted Myoblasts in Nonhuman Primates: Implications for Cell-Transplantation Strategies
Bibliographic record
Abstract
BACKGROUND: Several cell-transplantation strategies implicate the injection of cells into tissues. Avascular accumulations of implanted cells are then formed. Because the diffusion of oxygen and nutrients from the surrounding tissue throughout the implanted cell accumulations may be limited, central ischemic necrosis could develop. We analyzed this possibility after myoblast transplantation in nonhuman primates. METHODS: Macaca monkeys were injected intramuscularly with different amounts of myoblasts per single site. These sites were sampled 1 hr later and at posttransplantation days 1, 3, 5, and 7 and analyzed by histological techniques. RESULTS: One day posttransplantation, the largest pockets of implanted cells showed cores of massive necrosis. The width of the peripheral layer of living cells was approximately 100-200 microm. We thus analyzed the relationship between the amount of myoblasts injected per site and the volume of ischemic necrosis. Delivering 0.1 x 10(6) and 0.3 x 10(6) myoblasts did not produce ischemic necrosis; pockets of 1 x 10(6), 3 x 10(6), 10 x 10(6), and 20 x 10(6) myoblasts exhibited, respectively, a mean of 2%, 9%, 41%, and 59% of central necrosis. Intense macrophage infiltration took place in the muscle, invading the accumulations of necrotic cells and eliminating them by posttransplantation days 5 to 7. CONCLUSIONS: The desire to create more neoformed tissue by delivering more cells per injection site is confronted with the fact that the acute survival of the implanted cells is restricted to the peripheral layer that can profit of the diffusion of oxygen and nutriments from the surrounding recipient's tissue.
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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 | 0.001 | 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 | 0.001 |
| 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".