Effect of silver and titanium dioxide nanoparticles on PCR efficiency
Bibliographic record
Abstract
Interaction between nanomaterials and biological systems and the role nanomaterials play in biochemical reactions is of great interest nowadays due to the unique properties of nanomaterials and their potential applications in Biomedical Engineering. Recently, nanoparticle PCR research has attracted more attention. However, research has been focused on gold nanoparticles exclusively and there is no work about the effect of other nanoparticles reported yet. In this work, the effect of silver nanoparticles (AgNPs) and titanium dioxide nanoparticles (TiO2NPs) is evaluated using a real-time PCR machine. It is found that AgNPs start to cause PCR inhibition at 30 μg/mL. TiO2NPs exhibit much stronger inhibitory effect. They start to cause PCR inhibition at 0.8 μg/mL. When different amounts of AgNPs and TiO2NPs are added to PCR solution, some interesting phenomenon is observed. The effect of the combination of AgNPs and TiO2NPs is not simply the linear combination of the effect of individual AgNPs and TiO2NPs. However, by choosing the combination of AgNPs and TiO2NPs, PCR inhibition can be minimized. The observations suggest that a complex interaction mechanism exists between AgNPs and TiO2NPs.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".