DNA crosslinked alginate hydrogels: Characterization, microparticle development and applications in forensic science
Bibliographic record
Abstract
DNA-based hydrogels are attractive materials due to the integration of highly specific DNA sequences that can perform targeted functions for multiple fields. In this work we present a suite of materials with covalently bound ssDNA to an alginate-based hydrogel for targeted forensic applications. These crosslinked materials not only promote a more stable 3D polymeric network, but also achieve localization of functional ssDNA, a more desirable feature compared to previous DNA encapsulated versions. Specifically, dual amine terminated ssDNA (N-DNA-N) of three different concentrations was bound to alginate using carbodiimide chemistry. Rheological characterization showed that each DNA-crosslinked material forms similar structures, but the higher DNA concentration behaved like a dynamic viscoelastic material. FTIR analysis confirmed the formation of amide bonds, indicative of successful crosslinking between the N-DNA-N and alginate. SEM visualization also showed that each material had distinct topographies, where the covalent crosslinked alginate-DNA materials had more ordered particles and networked structures. We also investigated ssDNA with three different amine functionalities to understand the amine reactivity, which revealed that the N-DNA-N attaches primarily from the terminal primary amines on the DNA strands. From this, microparticles (MPs) using the DNA-crosslinked materials were developed, and the particle morphology and sizes were measured. It was determined that MPs made using DNA-crosslinked materials had larger particle diameters compared to the non-DNA controls, which is ideal for the generation of white blood cell (WBC) mimetics in forensic materials. In addition, these MPs could be successfully processed in a relevant forensic scenario through extraction, amplification, and genotyping, demonstrating the functionality of these materials to forensic blood simulants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".