Mechanical Characterization of Amniotic‐Based Scaffolds Containing Silk Fibroin and Sodium Alginate Nanofibers
Bibliographic record
Abstract
Due to its availability and biocompatibility, the human amniotic membrane (hAM) is being investigated by a large number of researchers with the goal of gaining a better understanding of the materials' mechanical behavior and structural integrity and optimizing them for various Tissue Engineering applications. In this research, biopolymers sodium alginate (SA) and silk fibroin (SF) were electrospun onto a decellularized hAM, resulting in two types of hybrid scaffolds: hAM/SF and hAM/SF/SA. The mechanical characteristics of these nanofibers were then analyzed to guide scaffold optimization for applications using these materials. Two mechanical experiments were conducted; uniaxial tension in both wet and dry configurations, and stress-relaxation tests. According to the results, the mechanical characteristics of the manufactured materials were significantly different from those of the amniotic membrane, indicating the effect of novel materials. Tensile testing in the dry condition revealed a small variation in stiffness between the amniotic membrane and the new nanofibers. Simultaneously, a significant reduction in maximum tension and stretch was observed in the aforementioned materials compared to amniotic matrices. In addition, tensile testing in a wet configuration indicated that the new nanofibers are stronger and stiffer than amniotic membrane but less stretchy, owing to the improved mechanical properties of SF, which can be considered as the membrane's or tissue's load-bearer. The addition of SF increases the stiffness and durability of the fabricated scaffold. In addition, when compared to the amniotic membrane, relaxation tests revealed significant differences in peak tension rather than equilibrium state for the novel nanofibers in wet conditions. The results of this investigation will enable us to have a comprehensive grasp of the mechanical properties of freshly created wound dressings.
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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 | 0.001 | 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".