Designer DNA biomolecules as a defined biomaterial for 3D bioprinting 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
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it