Shear Containment of BDNF within Molecular Hydrogels Promotes Human Stem Cell Engraftment and Postinfarction Remodeling in Stroke
Bibliographic record
Abstract
Abstract Improved control over spatiotemporal delivery of growth factors is needed to enhance tissue repair. Current methods are limited–requiring invasive procedures, poor tissue targeting, and/or limited control over dosage and duration. Incorporation into implantable biomaterials enables stabilized delivery and avoids burst release/fluctuating doses. Here, the physical forces of fibrils formed by self‐assembly of epitope‐containing peptides are exploited. This biomimetic hydrogel is loaded with neurotrophic factor BDNF via a shear‐induced gel–solution transition, unique to noncovalent hydrogels. This results in a biomaterial with three desirable features: a nanofibrillar scaffold, presentation of a laminin epitope, and slow release of BDNF. In a stroke‐injury model, synergistic actions of this trimodal strategy on the integration of transplanted human neural progenitor cells, and protection of peri‐infarct tissue are identified. These BDNF‐functionalized hydrogels promote the integration of transplanted human embryonic stem cell–derived neural progenitors–resulting in larger grafts with greater cortical differentiation, appropriate for neuronal replacement. Furthermore, BDNF promotes the infiltration of host endothelial cells into the graft to augment vascularization of the graft, and adjacent penumbra tissue. These findings demonstrate the benefits of multifaceted tissue‐specific hydrogels to provide biomimetics of the host tissue, while sustain protein delivery, to promote endogenous and graft‐derived tissue repair.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".