Label-free evaluation of short oligonucleotide bound alginate hydrogels using circular dichroism spectroscopy
Bibliographic record
Abstract
Oligonucleotide-based biosensors have attracted interest due to the increasing global demand for disease detection, environmental monitoring, and detection of degraded samples such as those found in forensic contexts. In this study, we explore the efficacy of circular dichroism (CD) spectroscopy to detect DNA and RNA hybridization on label-free short oligo-alginate hydrogels, without the need for amplification. We use an EDC/NHS coupling reaction to synthesize alginate-azide molecules, which are then crosslinked to DNA-alkyne or RNA-alkyne oligonucleotides using a copper-catalyzed azide-alkyne cycloaddition (CuAAC). A complementary strand to the bound oligonucleotide is added to the hydrogel and hybridization is assessed using CD spectroscopy. We report a limit of detection of 0.73 nmol and 0.17 nmol for DNA- and RNA-based biosensors, respectively. Biosensor specificity is evaluated by adding solution mixtures containing up to four different non-complementary strands to the alginate-oligo hydrogels. Chemometric models are then used to assess biosensor specificity. Principal component analysis (PCA) is performed on the spectra collected from 156 samples and successfully differentiated samples with and without a bound complement. DNA-based biosensors can also be distinguished from RNA-based biosensors. Additionally, random forest classification models are computed to classify unknown samples based on complement binding, achieving prediction accuracies greater than 92 %. Our findings demonstrate the feasibility of label-free, amplification-free detection of short oligonucleotides in aqueous solutions by measuring hybridization within our biosensor with CD spectroscopy, supporting potential applications to more complex environmental matrices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".