Microspheres for 3D bioprinting: a review of fabrication methods and applications
Bibliographic record
Abstract
Bioprinting incorporates printable biomaterials into 3D printing to create intricate tissues that maintain a defined 3D structure while supporting the survival and function of relevant cell types. A major challenge in 3D bioprinting is tuning material properties to ensure compatibility with different types of cells, while accurately mimicking the physiological microenvironment. Developing novel bioinks tailored to specific applications can help address this challenge by combining various materials and additives to tune the bioink formulation. Microspheres - small spherical particles - can incorporate drugs or growth factors to enable their controlled release, encapsulate cells to provide protection during printing, and provide structural reinforcement to tune mechanical properties and enable complex architectures. The particles range in size from 1 to 1000 μm and can be tuned to meet desired functions by optimizing their mode of production and the materials used for fabrication. This review presents an overview of microsphere production methods and considerations for optimizing the production process. It then summarizes how microspheres have been used to date in bioprinting applications. Finally, the existing challenges associated with the creation and use of microspheres are discussed along with avenues for future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".