Combining PLGA Scaffold and MSCs for Brain Tissue Engineering: A Potential Tool for Treatment of Brain Injury
Bibliographic record
Abstract
Nerve tissue engineering is an important strategy for the treatment of brain injuries. Mesenchymal stem cell (MSC) transplantation has been proven to be able to promote repair and functional recovery of brain damage, and poly (lactic-co-glycolic acid) (PLGA) has also been found to have the capability of bearing cells. In the present study, to observe the ability of PLGA scaffold in supporting the adherent growth of MSCs and neurons in vivo and vitro and to assess the effects of PLGA scaffold on proliferation and neural differentiation of MSCs, this study undertakes the following steps. First, MSCs and neurons were cultured and labeled with green fluorescent protein (GFP) or otherwise identified and the PLGA scaffold was synthesized. Next, MSCs and neurons were inoculated on PLGA scaffolds and their adhesion rates were investigated and the proliferation of MSCs was evaluated by using MTT assay. After MSCs were induced by a neural induction medium, the morphological change and neural differentiation of MSCs were detected using scanning electron microscopy (SEM) and immunocytochemistry, respectively. Finally, cell migration and adhesion in the PLGA scaffold in vivo were examined by immunohistochemistry, nuclear staining, and SEM. The experimental results demonstrated that PLGA did not interfere with the proliferation and neural differentiation of MSCs and that MSCs and neuron could grow and migrate in PLGA scaffold. These data suggest that the MSC-PLGA complex may be used as tissue engineering material for brain injuries.
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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 | 0.000 |
| 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".