Impact of 2’-fluoro nucleobase modifications on CD detection, sensitivity and specificity of short oligonucleotides bound to alginate hydrogels
Bibliographic record
Abstract
Chemical modification of oligonucleotides has aided in the advancement of various therapeutic applications; however, their impact on biosensor performance remains understudied. As demand grows for rapid, sensitive, and portable detection technologies in healthcare, environmental, and forensic fields, leveraging the effects of chemical modifications with spectroscopic detection methods offers an opportunity to improve biosensor performance. Herein, we synthesize label-free oligonucleotide-bound alginate hydrogels as biosensors and use circular dichroism (CD) spectroscopy to detect and confirm DNA and RNA hybridization without amplification or labelling. We particularly focus on the impact of 2’-fluoro modifications on biosensor sensitivity and specificity. Fluorine-modified DNA-based biosensors demonstrate more than a threefold increase in sensitivity compared to unmodified DNA. RNA-based biosensors displayed a similar trend, where 2’-fluoro nucleobase modifications significantly lowered the LOD. Biosensor specificity is evaluated by adding mixtures containing up to four non-complementary strands to the alginate-oligo hydrogels. Fluorine-modified biosensors consistently demonstrated greater specificity with more distinct shifts in CD spectra compared to unmodified DNA. Principal component analysis was applied to differentiated samples with and without a bound complement. Additionally, when thermal melt data was combined with CD spectral data it was possible with a random forest model to predict whether unknown samples demonstrated complement binding, with accuracies of 95 % and 83% for 2’-F modified and unmodified oligos, respectively. Our findings highlight the enhancement in biosensor sensitivity and specificity conferred by 2’-fluoro modifications, demonstrating their potential for improved label-free, amplification-free detection of oligonucleotides in complex environmental and forensic samples.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".