Pre- and Postnatal Transplantation of Fetal Mesenchymal Stem Cells in Osteogenesis Imperfecta: A Two-Center Experience
Bibliographic record
Abstract
Osteogenesis imperfecta (OI) can be recognized prenatally with ultrasound. Transplantation of mesenchymal stem cells (MSCs) has the potential to ameliorate skeletal damage. We report the clinical course of two patients with OI who received prenatal human fetal MSC (hfMSC) transplantation and postnatal boosting with same-donor MSCs. We have previously reported on prenatal transplantation for OI type III. This patient was retransplanted with 2.8 × 10(6) same-donor MSCs per kilogram at 8 years of age, resulting in low-level engraftment in bone and improved linear growth, mobility, and fracture incidence. An infant with an identical mutation who did not receive MSC therapy succumbed at 5 months despite postnatal bisphosphonate therapy. A second fetus with OI type IV was also transplanted with 30 × 10(6) hfMSCs per kilogram at 31 weeks of gestation and did not suffer any new fractures for the remainder of the pregnancy or during infancy. The patient followed her normal growth velocity until 13 months of age, at which time longitudinal length plateaued. A postnatal infusion of 10 × 10(6) MSCs per kilogram from the same donor was performed at 19 months of age, resulting in resumption of her growth trajectory. Neither patient demonstrated alloreactivity toward the donor hfMSCs or manifested any evidence of toxicities after transplantation. Our findings suggest that prenatal transplantation of allogeneic hfMSCs in OI appears safe and is of likely clinical benefit and that retransplantation with same-donor cells is feasible. However, the limited experience to date means that it is not possible to be conclusive and that further studies are required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".