Prolonged survival and expression of neural markers by bone marrow-derived stem cells transplanted into brain lesions.
Bibliographic record
Abstract
BACKGROUND: Bone marrow-derived stem cell transplantation is a potentially viable therapeutic option for the treatment of neurodegenerative disease. MATERIAL/METHODS: We have isolated bone marrow stem cells by standard method. We then evaluated the survival of rats' bone marrow mononuclear cells implanted in rats' brain. The cells were extracted from rats' femurs, and marked for monitoring purposes by adenoviral transduction with Green Fluorescent Protein (GFP). Labeled cells were implanted within the area of rats' striatum lesions that were induced a month earlier employing quinolinic acid-based method. The implants were phenotyped by monitoring CD34; CD38; CD45 and CD90 expression. Bone marrow stromal cells were extracted from rats' femurs and cultivated until monolayer bone marrow stromal cells were obtained. The ability of bone marrow stromal cells to express NGF and GDNF was evaluated by RT-PCR. RESULTS: Implanted cells survived for at least one month after transplantation and dispersed from the area of injection towards corpus callosum and brain cortex. Interestingly, passaged rat bone marrow stromal cells expressed NGF and GDNF mRNA. CONCLUSIONS: The bone marrow cells could be successfully transplanted to the brain either for the purpose of trans-differentiation, or for the expression of desired growth factors.
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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) | 0.001 | 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".