Gold–Oligonucleotide Nanoconstructs Engineered to Detect Conserved Enteroviral Nucleic Acid Sequences
Bibliographic record
Abstract
Enteroviruses are ubiquitous mammalian pathogens that can produce mild to life-threatening disease. We developed a multimodal, rapid, accurate and economical point-of-care biosensor that can detect nucleic acid sequences conserved amongst 96% of all known enteroviruses. The biosensor harnesses the physicochemical properties of gold nanoparticles and oligonucleotides to provide colourimetric, spectroscopic and lateral flow-based identification of an exclusive enteroviral nucleic acid sequence (23 bases), which was identified through in silico screening. Oligonucleotides were designed to demonstrate specific complementarity towards the target enteroviral nucleic acid to produce aggregated gold–oligonucleotide nanoconstructs. The conserved target enteroviral nucleic acid sequence (≥1 × 10−7 M, ≥1.4 × 10−14 g/mL) initiates gold–oligonucleotide nanoconstruct disaggregation and a signal transduction mechanism, producing a colourimetric and spectroscopic blueshift (544 nm (purple) > 524 nm (red)). Furthermore, lateral-flow assays that utilise gold–oligonucleotide nanoconstructs were unaffected by contaminating human genomic DNA, demonstrated rapid detection of conserved target enteroviral nucleic acid sequence (<60 s), and could be interpreted with a bespoke software and hardware electronic interface. We anticipate that our methodology will translate in silico screening of nucleic acid databases to a tangible enteroviral desktop detector, which could be readily translated to related organisms. This will pave the way forward in the clinical evaluation of disease and complement existing strategies to overcome antimicrobial resistance.
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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 | 0.000 | 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".