Tissue engineering of skeletal muscle, tendons and nerves: A review of manufacturing strategies to meet structural and functional requirements
Bibliographic record
Abstract
Additive manufacturing technologies have become at the forefront in tissue engineering, enabling the fabrication of complex tissues with intricate geometries that were not feasible using conventional manufacturing techniques. Due to the rapid progress in this field, it has become difficult not only to choose the most appropriate method, but also the optimal material, biological model (i.e., cells and bioactive compounds), and processing technique to fulfill the macro- and microstructural architecture and functions of biological tissues. The aim of this review is to describe recent advances in tissue engineering fabrication methods, from established electrospinning to emerging additive manufacturing technologies, with particular emphasis on tissues that exhibit hierarchically organized anisotropic architecture (skeletal muscle, tendons, and peripheral nerves). One of the current challenges is that the designs are usually dictated by the constraints imposed by the methods, rather than by criteria based on mechanical and biological requirements. Therefore, the review focuses on describing how the anatomical structure and function of muscles, tendons, and nerves should serve as the basis for an efficient three-dimensional design that considers both micro and macro aspects of the tissue. In addition, the individual factors that influence the fabrication strategy are discussed and related to the mechanical and biological properties of the three tissue types. The review highlights the advantages and limitations of each fabrication strategy and provides an overview of critical aspects relevant to future research strategies in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".