Development of Brain-Derived Bioscaffolds for Neural Progenitor Cell Culture
Bibliographic record
Abstract
Biomaterials derived from brain extracellular matrix (ECM) have the potential to promote neural tissue regeneration by providing instructive cues that can direct cell survival, proliferation, and differentiation. This study focused on the development and characterization of microcarriers derived from decellularized brain tissue (DBT) as a platform for neural progenitor cell culture. First, a novel detergent-free decellularization protocol was established that effectively reduced the cellular content of porcine and rat brains, with a >97% decrease in the dsDNA content, while preserving collagens (COLs) and glycosaminoglycans (GAGs). Next, electrospraying methods were applied to generate ECM-derived microcarriers incorporating the porcine DBT that were stable without chemical cross-linking, along with control microcarriers fabricated from commercially sourced bovine tendon COL. The DBT microcarriers were structurally and biomechanically similar to the COL microcarriers, but compositionally distinct, containing a broader range of COL types and higher sulfated GAG content. Finally, we compared the growth, phenotype, and neurotrophic factor gene expression levels of rat brain-derived progenitor cells (BDPCs) cultured on the DBT or COL microcarriers within spinner flask bioreactors over 2 weeks. Both microcarrier types supported BDPC attachment and expansion, with immunofluorescence staining results suggesting that the culture conditions promoted BDPC differentiation toward the oligodendrocyte lineage, which may be favorable for cell therapies targeting remyelination. Overall, our findings support the further investigation of the ECM-derived microcarriers as a platform for neural cell derivation for applications in regenerative medicine.
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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".