Biofabrication Strategies for Musculoskeletal Disorders: Evolution towards Clinical Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biofabrication has emerged as an attractive strategy to personalise medical care and provide new treatments for common organ damage or diseases. While it has made impactful headway in e.g., skin grafting, drug testing and cancer research purposes, its application to treat musculoskeletal tissue disorders in a clinical setting remains scarce. Albeit with several in vitro breakthroughs over the past decade, standard musculoskeletal treatments are still limited to palliative care or surgical interventions with limited long-term effects and biological functionality. To better understand this lack of translation, it is important to study connections between basic science challenges and developments with translational hurdles and evolving frameworks for this fully disruptive technology that is biofabrication. This review paper thus looks closely at the processing stage of biofabrication, specifically at the bioinks suitable for musculoskeletal tissue fabrication and their trends of usage. This includes underlying composite bioink strategies to address the shortfalls of sole biomaterials. We also review recent advances made to overcome long-standing challenges in the field of biofabrication, namely bioprinting of low-viscosity bioinks, controlled delivery of growth factors, and the fabrication of spatially graded biological and structural scaffolds to help biofabricate more clinically relevant constructs. We further explore the clinical application of biofabricated musculoskeletal structures, regulatory pathways, and challenges for clinical translation, while identifying the opportunities that currently lie closest to clinical translation. In this article, we consider the next era of biofabrication and the overarching challenges that need to be addressed to reach clinical relevance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it