PCR-Free DNA Detection Using a Magnetic Bead-Supported Polymeric Transducer and Microelectromagnetic Traps
Bibliographic record
Abstract
A fluorescent polymeric hybridization transducer supported on magnetic microbeads was investigated for the rapid, ultrasensitive, and sequence-specific detection of DNA. We show that the polymer derivative can be used to detect target DNA directly on magnetic particles by preparing "target-ready" microbeads grafted with the polymer and suitable DNA probes. A detection limit of approximately 200 target copies in a probed volume of 150 muL (1.4 copies/muL) was obtained for a DNA sequence specific to Candida albicans This detection scheme does not require the release of the hybridized target DNA prior to its detection or the labeling or amplification of the nucleic acids. Furthermore, we show that the fluorescence from these biosensing magnetic beads can be read while magnetically confined in a small volume by a microelectromagnetic trap, which offers the possibility of performing both the preconcentration and detection steps simultaneously on the same support. The combination of the fluorescent polymer biosensor with magnetic particle-assisted DNA preconcentration extends the application of this ultrasensitive biosensor to biological samples with complex matrixes and to integrated lab-on-a-chip platforms, where it could be used for fast multitarget DNA detection in point-of-care diagnostics and field analysis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".