Designer DNA biomolecules as a defined biomaterial for 3D bioprinting applications
Bibliographic record
Abstract
DNA has excellent features such as the presence of functional and targeted molecular recognition motifs, tailorability, multifunctionality, high-precision molecular self-assembly, hydrophilicity, and outstanding biocompatibility. Due to these remarkable features, DNA has emerged as a leading next-generation biomaterial of choice to make hydrogels by self-assembly. In recent times, novel routes for the chemical synthesis of DNA, advances in tailorable designs, and affordable production ways have made DNA as a building block material for various applications. These advanced features have made researchers continuously explore the interesting properties of pure and hybrid DNA for 3D bioprinting and other biomedical applications. This review article highlights the topical advancements in the use of DNA as an ideal bioink for the bioprinting of cell-laden three-dimensional tissue constructs for regenerative medicine applications. Various bioprinting techniques and emerging design approaches such as self-assembly, nucleotide sequence, enzymes, and production cost to use DNA as a bioink for bioprinting applications are described. In addition, various types and properties of DNA hydrogels such as stimuli responsiveness and mechanical properties are discussed. Further, recent progress in the applications of DNA in 3D bioprinting are emphasized. Finally, the current challenges and future perspectives of DNA hydrogels in 3D bioprinting and other biomedical applications are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".