Mesenchymal stem cell‐seeded multilayered dense collagen‐silk fibroin hybrid for tissue engineering applications
Bibliographic record
Abstract
Tissue engineering of multilayered constructs that model complex tissues poses a significant challenge for regenerative medicine. In this study, a three-layered scaffold consisting of an electrospun silk fibroin (SF) mat sandwiched between two dense collagen (DC) layers was designed and characterized. It was hypothesized that the SF layer would endow the DC-SF-DC construct with enhanced mechanical properties (e.g., apparent modulus, tensile strength, and toughness), while the surrounding DC layers provide an extracellular matrix-like environment for mesenchymal stem cell (MSC) growth. MSC-seeded DC-SF-DC hybrids were produced using the plastic compression technique and characterized morphologically, chemically, and mechanically. Moreover, MSC viability was assessed for up to 1 wk in culture. Scaffold analyses confirmed compaction and integration of the meso-scaled multilayered DC-SF-DC hybrid, which was reflected in a significantly higher toughness value when compared to DC and SF alone. MSCs directly incorporated into the DC layers remained viable for up to day 7. The ease of multilayered construct fabrication, enhanced biomechanical properties, along with uniformity of cell distribution confirmed the possibility for the incorporation and segregation of different cell types within distinct layers for the regeneration of complex tissues, such as skin, or central nervous system dura mater.
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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".