Effect of buffer composition on PNA–RNA hybridization studied in the microfluidic microarray chip
Bibliographic record
Abstract
Herein, we report that peptide nucleic acid sequences (PNAs) have been used as the probe species for detection of RNA and that a microfluidic microarray (MMA) chip is used as the platform for detection of hybridizations between immobilized PNA probes and RNA targets. The RNA targets used are derived from influenza A sequences. This paper discusses the optimization of two probe technologies used for RNA detection and investigates how the composition of the probe buffer and the content of the hybridization solution can influence the overall results. Our data show that the PNA probe is a better choice than the DNA probe when there is low salt in the probe buffer composition. Furthermore, we show that the absence of salt (NaCl) in the hybridization buffer does not hinder the detection of RNA sequences. The results provide evidence that PNA probes are superior to DNA probes in term of sensitivity and adaptability, as PNA immobilization and PNA–RNA hybridization are less affected by salt content in the reaction buffers unlike DNA probes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".