A versatile fluorometric <i>in situ</i> hybridization method for the quantitation of hairpin conformations in DNA self-assembled monolayers
Bibliographic record
Abstract
As the performance of hairpin DNA (hpDNA)-based biosensors is highly dependent on the yield of stem-loop (hairpin) conformations, we report herein a versatile fluorometric in situ hybridization protocol for examining hpDNA self-assembled monolayers (SAMs) on popularly used biochip substrates. Specifically, the ratio of fluorescence (FL) intensities of hpDNA SAMs (in an array format) before and after hybridization was adopted as the key parameter for performing such a determination. Upon confirming the existence of mixed and tunable DNA conformations in binary deposition solutions and efficient hybridization of the hairpin strands with the target DNA via gel electrophoresis assays, we tested the fluorometric protocol for determining the coverages of hpDNA in hpDNA/ssDNA SAMs prepared on gold; its accuracy was validated by Exonuclease I (Exo I)-assisted electrochemical quantitation. To further confirm its versatility, this FL protocol was adopted for quantifying hairpin conformations formed on glass and polycarbonate (PC) substrates. The molar ratios of surface-tethered hairpin conformations on the three different substrates were all found to be proportional to but less than those in the binary deposition solutions, and were dependent on the substrate morphology. The findings reported herein are beneficial for the construction of highly efficient DNA hairpin-based sensing surfaces, which essentially facilitates the creation of hpDNA-based biosensors with optimal detection performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".