A review of fibrin and fibrin composites for bone tissue engineering
Bibliographic record
Abstract
Tissue engineering has emerged as a new treatment approach for bone repair and regeneration seeking to address limitations associated with current therapies, such as autologous bone grafting. While many bone tissue engineering approaches have traditionally focused on synthetic materials (such as polymers or hydrogels), there has been a lot of excitement surrounding the use of natural materials due to their biologically inspired properties. Fibrin is a natural scaffold formed following tissue injury that initiates hemostasis and provides the initial matrix useful for cell adhesion, migration, proliferation, and differentiation. Fibrin has captured the interest of bone tissue engineers due to its excellent biocompatibility, controllable biodegradability, and ability to deliver cells and biomolecules. Fibrin is particularly appealing because its precursors, fibrinogen, and thrombin, which can be derived from the patient's own blood, enable the fabrication of completely autologous scaffolds. In this article, we highlight the unique properties of fibrin as a scaffolding material to treat bone defects. Moreover, we emphasize its role in bone tissue engineering nanocomposites where approaches further emulate the natural nanostructured features of bone when using fibrin and other nanomaterials. We also review the preparation methods of fibrin glue and then discuss a wide range of fibrin applications in bone tissue engineering. These include the delivery of cells and/or biomolecules to a defect site, distributing cells, and/or growth factors throughout other pre-formed scaffolds and enhancing the physical as well as biological properties of other biomaterials. Thoughts on the future direction of fibrin research for bone tissue engineering are also presented. In the future, the development of fibrin precursors as recombinant proteins will solve problems associated with using multiple or single-donor fibrin glue, and the combination of nanomaterials that allow for the incorporation of biomolecules with fibrin will significantly improve the efficacy of fibrin for numerous bone tissue engineering applications.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".