Immobilization of DNA on Magnetic Microparticles for Mercury Enrichment and Detection with Flow Cytometry
Bibliographic record
Abstract
Mercury detection in water has attracted a lot of research interest due to its highly toxic nature and adverse environmental impact. In particular, the recent discovery of specific binding of Hg(II) to thymine-rich (T-rich) DNA resulting in T-Hg(II)-T base pairs has led to the development of a number of sensors with different signaling mechanisms. However, the majority of such sensors were non-immobilized. Immobilization, on the other hand, allows active mercury adsorption, signal amplification, and sensor regeneration. In this work, we immobilized a thymine-rich DNA on a magnetic microparticle (MMP) surface through biotin-streptavidin interactions. In the presence of Hg(II), the DNA changes from a random coil structure into a hairpin, upon which SYBR Green I binds to emit green fluorescence. Detection was carried out by using flow cytometry where the fluorescence intensity increased ≈9-fold in the presence of mercury and the binding of mercury reached equilibrium in less than 2 min. The sensor showed a unique sample-volume-dependent fluorescence signal change where a higher fluorescence was obtained with a larger sample volume, suggesting that the particles can actively adsorb Hg(II). Detection limits of 5 nM (1 ppb) and 14 nM (2.8 ppb) were achieved in pure buffer and in mercury-spiked Lake Ontario water samples, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".