Human Skeletal Muscle Myoblast Culture in Aligned Bacterial Nanocellulose and Commercial Matrices
Bibliographic record
Abstract
Bacterial nanocellulose (BNC) is a durable, flexible, and dynamic biomaterial capable of serving a wide variety of fields, sectors, and applications within biotechnology, healthcare, electronics, agriculture, fashion, and others. BNC is produced spontaneously in carbohydrate-rich bacterial culture media, forming a cellulosic pellicle via a nanonetwork of fibrils extruded from certain genera. Herein, we demonstrate engineering BNC-based scaffolds with tunable physical and mechanical properties through postprocessing. Human skeletal muscle myoblasts (HSMMs) were cultured on these scaffolds, and in vitro electrical stimulation was applied to promote cellular function for tissue engineering applications. We compared physiologic maturation markers of human skeletal muscle myoblast development using a 2.5-dimensional culture paradigm in fabricated BNC scaffolds, compared to two-dimensional (2D) controls. We demonstrate that the culture of human skeletal muscle myoblasts on BNC scaffolds developed under electrical stimulation produced highly aligned, physiologic morphology of human skeletal muscle myofibers compared to unstimulated BNC and standard 2D culture. Furthermore, we compared an array of metrics to assess the BNC scaffold in a rigorous head-to-head study with commercially available, clinically approved matrices, Kerecis Omega3 Wound Matrix (Marigen) and Phoenix as well as a gelatin methacryloyl (GelMA) hydrogel. The BNC scaffold outcompeted industry standard matrices as well as a 20% GelMA hydrogel in durability and sustained the support of human skeletal muscle myoblasts in vitro. This work offers a robust demonstration of BNC scaffold cytocompatibility with human skeletal muscle cells and sets the basis for future work in healthcare, bioengineering, and medical implant technological development.
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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) | 0.001 | 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".