Human meniscus allograft augmentation by allogeneic mesenchymal stromal/stem cell injections
Bibliographic record
Abstract
Meniscus allograft transplantations (MATs) represent established surgical procedures with proven outcomes. Yet, storage as frozen specimens and limited cellular repopulation may impair graft viability. This proof-of-concept study tests the feasibility of injecting allogeneic mesenchymal stromal/stem cells (MSCs) in meniscus allograft tissue. We investigated the injectable cell quantity, survival rate, migration, and proliferation ability of MSCs up to 28 days of incubation. In this controlled laboratory study, seven fresh-frozen human allografts were injected with human allogeneic MSCs. Cells were labeled and histological characteristics were microscopically imaged up to 28 days. Mock-injected menisci were included as negative controls in each experiment. Toluidine blue staining demonstrated that a 100-µl volume can be injected while retracting and rotating the inserted needle. Immediately after injection, labeled MSCs were distributed throughout the injection channel and eventually migrated into the surrounding tissues. Histological assessment revealed that MSCs cluster in disc-like shapes, parallel to the intrinsic lamination of the meniscus and around the vascular network. Quantification showed that more than 60% of cells were present in horizontally injected grafts and more than 30% were observed in vertically injected samples. On Day 14, cells adopted a spindle-shaped morphology and exhibited proliferative and migratory behaviors. On Day 28, live/dead ratio assessment revealed an approximately 80% cell survival. The study demonstrated the feasibility of injecting doses of MSCs (>0.1 million) in meniscus allograft tissue with active cell proliferation, migration, and robust cell 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 | 0.000 | 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) | 0.002 | 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".