DNA as a promising biomaterial for bone regeneration and potential mechanisms of action
Bibliographic record
Abstract
DNA nanotechnology has created new possibilities for the use of DNA in tissue regeneration - an important advance for DNA use beyond its paradigmatic role as the hereditary biomacromolecule. Biomaterials containing synthetic or natural DNA have been proposed for several applications including drug and gene delivery, and more recently, as osteoconductive biomaterials. This review provides an in-depth discussion of studies that have used DNA-based materials for biomineralization and/or bone repair, with expansion on the topic of DNA hydrogels specifically, and the advantages they offer for advancing the field of bone regeneration. Four mechanisms of action for the osteoconductive capabilities of DNA-based materials are discussed, and a proposed model for degradation of these materials and its link to their osteoconductive properties is later presented. Finally, the review considers current limitations of DNA-based materials and summarizes important aspects that need to be addressed for future application of DNA nanotechnology in tissue repair. STATEMENT OF SIGNIFICANCE: Herein we summarize the developing field of DNA-based materials for biomineralization and bone repair, with a focus on DNA hydrogels. We first provide a comprehensive review of different forms of DNA-based materials described thus far which have been shown to enhance bone repair and mineralization (namely DNA coatings, DNA-containing pastes, DNA nanostructures and DNA hydrogels). Next, we describe four different mechanisms by which DNA-based materials could be exerting their osteogenic effect. Then, we propose a novel model that links DNA degradation and osteoconductivity. Lastly, we suggest possible research directions to enhance DNA-based materials for future clinical application. The suggested mechanisms and the proposed model can guide future research to better understand how DNA functions as a mineral- and bone-promoting molecule.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".