Detection of cystic fibrosis transmembrane conductance regulator ΔF508 gene mutation using a paper-based nucleic acid hybridization assay and a smartphone camera
Bibliographic record
Abstract
Diagnostic technology that makes use of paper platforms in conjunction with the ubiquitous availability of digital cameras in cellular telephones and personal assistive devices offers opportunities for development of bioassays that are cost effective and widely distributed. Assays that operate effectively in aqueous solution require further development for implementation in paper substrates, overcoming issues associated with surface interactions on a matrix that offers a large surface-to-volume ratio and constraints on convective mixing. This report presents and compares two related methods for determination of oligonucleotides that serve as indicators of cystic fibrosis, differentiating between the normal wild-type sequence, and a mutant-type sequence that has a 3-base replacement. The transduction strategy operates by selective hybridization of oligonucleotide probes that are conjugated to fluorescent quantum dots, where hybridization of target sequences causes a molecular fluorophore to approach the quantum dot and become emissive through fluorescence resonance energy transfer. Detection can rely on hybridization of a target that is labelled with Cy3 fluorophore, or in the presence of an unlabelled target when a sandwich assay format is implemented with a labelled reporter oligonucleotide. Selectivity to determine the presence of mismatched sequences involves appropriate selection of nucleotide sequences to set melt temperatures, in conjunction with control of stringency conditions using formamide as a chaotrope. It was determined that both direct and sandwich assays on paper substrates are able to distinguish between wild-type and mutant-type 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".