Intravenous Administration of Adipose-Derived Stem Cell Protein Extracts Improves Neurological Deficits in a Rat Model of Stroke
Bibliographic record
Abstract
Treatment of adipose-derived stem cell (ADSC) substantially improves the neurological deficits during stroke by reducing neuronal injury, limiting proinflammatory immune responses, and promoting neuronal repair, which makes ADSC-based therapy an attractive approach for treating stroke. However, the potential risk of tumorigenicity and low survival rate of the implanted cells limit the clinical use of ADSC. Cell-free extracts from ADSC (ADSC-E) may be a feasible approach that could overcome these limitations. Here, we aim to explore the potential usage of ADSC-E in treating rat transient middle cerebral artery occlusion (tMCAO). We demonstrated that intravenous (IV) injection of ADSC-E remarkably reduces the ischemic lesion and number of apoptotic neurons as compared to other control groups. Although ADSC and ADSC-E treatment results in a similar degree of a long-term clinical beneficial outcome, the dynamics between two ADSC-based therapies are different. While the injection of ADSC leads to a relatively mild but prolonged therapeutic effect, the administration of ADSC-E results in a fast and pronounced clinical improvement which was associated with a unique change in the molecular signature suggesting that potential mechanisms underlying different therapeutic approach may be different. Together these data provide translational evidence for using protein extracts from ADSC for treating stroke.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".