Winner of the Young Investigator Award of the Society for Biomaterials (USA) for 2016, 10th World Biomaterials Congress, May 17–22, 2016, Montreal QC, Canada: Aligned microribbon‐like hydrogels for guiding three‐dimensional smooth muscle tissue regeneration
Bibliographic record
Abstract
Smooth muscle tissue is characterized by aligned structures, which is critical for its contractile functions. Smooth muscle injury is common and can be caused by various diseases and degenerative processes, and there remains a strong need to develop effective therapies for smooth muscle tissue regeneration with restored structures. To guide cell alignment, previously cells were cultured on 2D nano/microgrooved substrates, but such method is limited to fabricating 2D aligned cell sheets only. Alternatively, aligned electrospun nanofiber has been employed as 3D scaffold for cell alignment, but cells can only be seeded post fabrication, and nanoporosity of electrospun fiber meshes often leads to poor cell distribution. To overcome these limitations, we report aligned gelatin-based microribbons (µRBs) as macroporous hydrogels for guiding smooth muscle alignment in 3D. We developed aligned µRB-like hydrogels using wet spinning, which allows easy fabrication of tissue-scale (cm) macroporous matrices with alignment cues and supports direct cell encapsulation. The macroporosity within µRB-based hydrogels facilitated cell proliferation, new matrix deposition, and nutrient diffusion. In aligned µRB scaffold, smooth muscle cells showed high viability, rapid adhesion, and alignment following µRB direction. Aligned µRB scaffolds supported retention of smooth muscle contractile phenotype, and accelerated uniaxial deposition of new matrix (collagen I/IV) along the µRB. In contrast, cells encapsulated in conventional gelatin hydrogels remained round with matrix deposition limited to pericellular regions only. We envision such aligned µRB scaffold can be broadly applicable in growing other anisotropic tissues including tendon, nerves and blood vessel.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".