Winner of the Young Investigator Award of the Society for Biomaterials at the 10th World Biomaterials Congress, May 17–22, 2016, Montreal QC, Canada: Microribbon‐based hydrogels accelerate stem cell‐based bone regeneration in a mouse critical‐size cranial defect model
Bibliographic record
Abstract
Stem cell-based therapies hold great promise for enhancing tissue regeneration. However, the majority of cells die shortly after transplantation, which greatly diminishes the efficacy of stem cell-based therapies. Poor cell engraftment and survival remain a major bottleneck to fully exploiting the power of stem cells for regenerative medicine. Biomaterials such as hydrogels can serve as artificial matrices to protect cells during delivery and guide desirable cell fates. However, conventional hydrogels often lack macroporosity, which restricts cell proliferation and delays matrix deposition. Here we report the use of injectable, macroporous microribbon (μRB) hydrogels as stem cell carriers for bone repair, which supports direct cell encapsulation into a macroporous scaffold with rapid spreading. When transplanted in a critical-sized, mouse cranial defect model, μRB-based hydrogels significantly enhanced the survival of transplanted adipose-derived stromal cells (ADSCs) (81%) and enabled up to three-fold cell proliferation after 7 days. In contrast, conventional hydrogels only led to 27% cell survival, which continued to decrease over time. MicroCT imaging showed μRBs enhanced and accelerated mineralized bone repair compared to hydrogels (61% vs. 34% by week 6), and stem cells were required for bone repair to occur. These results suggest that paracrine signaling of transplanted stem cells are responsible for the observed bone repair, and enhancing cell survival and proliferation using μRBs further promoted the paracrine-signaling effects of ADSCs for stimulating endogenous bone repair. We envision μRB-based scaffolds can be broadly useful as a novel scaffold for enhancing stem cell survival and regeneration of other tissue types. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 1321-1331, 2016.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".