Fabrication of highly porous tissue-engineering scaffolds using selective spherical porogens
Bibliographic record
Abstract
Tissue engineering holds great promise as an alternative strategy to current treatment modalities of diseased or otherwise failed tissues. Most strategies of tissue engineering rely on three-dimensional porous scaffolds to mimic the natural extracellular matrix (ECM) as templates onto which cells attach, multiply, migrate and function. When cells are harvested from a donor and seeded, scaffolds facilitate the organization of these cells into a three-dimensional architecture, control cell behavior and subsequently direct the formation of organ-specific tissue. In view of its role, scaffold fabrication methods target the creation of highly porous and interconnected pore structures. Among the different scaffolds fabrication methods explored, solvent casting followed by precipitation or particulate leaching is one of the most straightforward methods. In this paper, we conducted a comparative study of two methods to prepare spherical porogens using poly(vinyl pyrrolidone) (PVP) and poly(vinyl alcohol) (PVA) as dispersing agents and we used these porogens to fabricate cylindrical porous scaffolds using a combination of pressure differential and solvent casting/particulate leaching method. Porogen particle size analyses showed that at 0.6% dispersing agent concentration, PVP produced smaller particles with narrower distribution (100-300 mum) than poly(vinyl alcohol) (100-500 mum) presumably due to the fast adsorption kinetics of the former. Scaffolds fabricated from PVP-stabilized porogens had higher open porosities and high pore interconnectivity than those based on porogens prepared using PVA stabilizer. Preliminary cell culture work also showed that scaffolds fabricated using PVP-stabilized porogens support attachment and spreading of human coronary artery smooth muscle cells (HCASMC) better than the PVA counterparts. This is the first time that such direct comparative studies on the porogen preparation methods and its effect on the scaffold porosity and cell attachment property is reported.
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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) | 0.000 | 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 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".