Activated Paper Surfaces for the Rapid Hybridization of DNA through Capillary Transport
Bibliographic record
Abstract
The development of low-cost, accurate, and equipment-free diagnostic tests is crucial to many clinical, laboratory, and field applications, including forensics and medical diagnostics. Cellulose fiber-based paper is an inexpensive, biodegradable, and renewable resource, the use of which as a biomolecule detection matrix and support confers several advantages compared to traditional materials such as glass. In this context, a new, facile method for the preparation of surface functionalized papers bearing single-stranded probe DNA (ssDNA) for rapid target hybridization via capillary transport is presented. Optimized reaction conditions were developed that allowed the direct, one-step activation of standard laboratory filters by the inexpensive and readily available bifunctional linking reagent, 1,4-phenylenediisothiocyanate. Such papers were thus amenable to subsequent coupling of amine-labeled ssDNA under standard conditions widely used for glass-based supports. The intrinsic wicking ability of the paper matrix facilitated rapid sample elution through arrays of probe DNA, leading to significant, detectable hybridization in the time required for the sample liquid to transit the vertical length of the strip (less than 2 min). The broad applicability of these paper test strips as rapid and specific diagnostics in "real-life" situations was exemplified by the discrimination of amplicons generated from canine and human mitochondrial and genomic DNA in mock 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".