Recent advances in 3D bioprinting of musculoskeletal tissues
Bibliographic record
Abstract
The musculoskeletal system is essential for maintaining posture, protecting organs, facilitating locomotion, and regulating various cellular and metabolic functions. Injury to this system due to trauma or wear is common, and severe damage may require surgery to restore function and prevent further harm. Autografts are the current gold standard for the replacement of lost or damaged tissues. However, these grafts are constrained by limited supply and donor site morbidity. Allografts, xenografts, and alloplastic materials represent viable alternatives, but each of these methods also has its own problems and limitations. Technological advances in three-dimensional (3D) printing and its biomedical adaptation, 3D bioprinting, have the potential to provide viable, autologous tissue-like constructs that can be used to repair musculoskeletal defects. Though bioprinting is currently unable to develop mature, implantable tissues, it can pattern cells in 3D constructs with features facilitating maturation and vascularization. Further advances in the field may enable the manufacture of constructs that can mimic native tissues in complexity, spatial heterogeneity, and ultimately, clinical utility. This review studies the use of 3D bioprinting for engineering bone, cartilage, muscle, tendon, ligament, and their interface tissues. Additionally, the current limitations and challenges in the field are discussed and the prospects for future progress are highlighted.
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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".