Transplantation of human mesenchymal stem cells promotes functional improvement and increased expression of neurotrophic factors in a rat focal cerebral ischemia model
Bibliographic record
Abstract
Previous studies have suggested that intravenous transplantation of mesenchymal stem cells (MSCs) in rat ischemia models reduces ischemia-induced brain damage. Here, we analyzed the expression of neurotrophic factors in transplanted human MSCs and host brain tissue in rat middle cerebral artery occlusion (MCAO) ischemia model. At 1 day after transient MCAO, 3 x 10(6) immortalized human MSC line (B10) cells or PBS was intravenously transplanted. Behavioral tests, infarction volume, and B10 cell migration were investigated at 1, 3, 7, and 14 days after MCAO. The expression of endogenous (rat origin) and exogenous (human origin) neurotrophic factors and cytokines was evaluated by quantitative real-time RT-PCR and Western blot analysis. Compared with PBS controls, rats receiving MSC transplantation showed improved functional recovery and reduced brain infarction volume at 7 and 14 days after MCAO. In MSC-transplanted brain, among many neurotrophic factors, only human insulin-like growth factor 1 (IGF-1) was detected in the core and ischemic border zone at 3 days after MCAO, whereas host cells expressed markedly higher neurotrophic factors (rat origin) than control rats, especially vascular endothelial growth factor (VEGF) at 3 days and epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF) at 7 days after MCAO. Intravenously transplanted human MSCs induced functional improvement, reduced infarct volume, and neuroprotection in ischemic rats, possibly by providing IGF-1 and inducing VEGF, EGF, and bFGF neurotrophic factors in host brain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".