Abstract WP110: The Next Generation of Autologous Bone Marrow Stromal Cell Transplantation Against Stroke
Bibliographic record
Abstract
Background and purpose: Recent studies have elucidated that the bone marrow stromal cells (BMSCs) have therapeutic potential against stroke. Some clinical trials have been starting up in practice. We aimed to evolve the autologous BMSC transplantation for stroke into the next generation. Materials and methods: Human BMSCs were cultured with human platelet lysate (hPL) instead of fetal calf serum (FCS). They were labeled with superparamagnetic iron oxide (SPIO). Rat ischemic stroke models were made and 5x105 cells were injected into the ipsilateral striatum stereotactically 7 days post-insult. Behavioral analysis, MRI for cell tracking, (18)F-FDG PET, and (123)I-Iomazenil SPECT were performed. The animals were sacrificed 5 to 8 weeks post-transplantation and histological analysis was performed. Results: There was no difference in the surface markers and cell proliferation between hPL and FCS. Although rotarod test showed that motor function deteriorated in rats suffered from permanent MCAo, BMSC-hPL transplantation enhanced recovery of the motor function, significantly. MRI demonstrated that SPIO-BMSCs aggressively migrated towards the lesion. Moreover, (18)F-FDG PET and (123)I-Iomazenil SPECT showed that BMSC transplantation promoted recovery of the glucose utilization and the binding potential of iomazenil in the peri-infarct area, respectively. Histological analysis supported the findings on MRI and showed the inclination for neural differentiation of donor cells. Conclusion: The hPL may be valuable and safe in expanding BMSCs. The application of bio-imaging techniques is also valuable for BMSC transplantation for stroke. Now we prepare the novel clinical trial against stroke, Research on Advanced Intervention using Novel Bone marrOW stem cell (RAINBOW) study. The present results are translated into the optimal design of the trial.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".