Characterization and functional analysis of the adipose tissue-derived stromal vascular fraction of pediatric patients with osteogenesis imperfecta
Bibliographic record
Abstract
Pediatric patients with Osteogenesis Imperfecta (OI), a heritable connective tissue disorder, frequently suffer from long bone deformations. Surgical correction often results in bone non-unions, necessitating revision surgery with autogenous bone grafting using bone-marrow-derived stem cells (BM-SC) to regenerate bone. BM-SC harvest is generally invasive and limited in supply; thus, adipose tissue's stromal vascular fraction (SVF) has been introduced as an alternative stem cell reservoir. To elucidate if OI patients' surgical site dissected adipose tissue could be used as autologous bone graft in future, we investigated whether the underlying genetic condition alters SVF's cell populations and in vitro differentiation capacity. After optimizing SVF isolation, we demonstrate successful isolation of SVF of pediatric OI patients and non-OI controls. The number of viable cells was comparable between OI and controls, with about 450,000 per gram tissue. Age, sex, type of OI, disease-causing collagen mutation, or anatomical site of harvest did not affect cell outcome. Further, SVF-containing cell populations were similar between OI and controls, and all isolated SVF's demonstrated chondrogenic, adipogenic, and osteogenic differentiation capacity in vitro. These results indicate that SVF from pediatric OI patients could be used as a source of stem cells for autologous stem cell therapy in OI.
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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 | 0.000 |
| 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".