A combination of mesenchymal stem cells and scaffolds promotes motor functional recovery in spinal cord injury: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: There is controversy about the role of scaffolds as an adjunctive therapy to mesenchymal stem cell (MSC) transplantation in spinal cord injury (SCI). Thus, the authors aimed to design a meta-analysis on preclinical evidence to evaluate the effectiveness of combination therapy of scaffold + MSC transplantation in comparison with scaffolds alone and MSCs alone in improving motor dysfunction in SCI. METHODS: Electronic databases including Medline, Embase, Scopus, and Web of Science were searched from inception until the end of August 2018. Two independent reviewers screened related experimental studies. Animal studies that evaluated the effectiveness of scaffolds and/or MSCs on motor function recovery following experimental SCI were included. The findings were reported as standardized mean difference (SMD) and 95% confidence interval (CI). RESULTS: A total of 34 articles were included in the meta-analysis. Analyses show that combination therapy in comparison with the scaffold group alone (SMD 2.00, 95% CI 1.53-2.46, p < 0.0001), the MSCs alone (SMD 1.58, 95% CI 0.84-2.31, p < 0.0001), and the nontreated group (SMD 3.52, 95% CI 2.84-4.20, p < 0.0001) significantly improved motor function recovery. Co-administration of MSCs + scaffolds only in the acute phase of injury (during the first 3 days after injury) leads to a significant recovery compared to scaffold alone (SMD 2.18, p < 0.0001). In addition, the cotransplantation of scaffolds with bone marrow-derived MSCs (SMD 1.99, p < 0.0001) and umbilical cord-derived MSCs (SMD 1.50, p = 0.001) also improved motor function following SCI. CONCLUSIONS: The findings showed that scaffolds + MSCs is more effective than scaffolds and MSCs alone in improving motor function following SCI in animal models, when used in the acute phase of injury.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".