Effect of antifouling dendrimers and Au DENPs on the enhancement of PCR amplification
Bibliographic record
Abstract
The polymerase chain reaction (PCR) has been considered as one of the most fundamental techniques to amplify and analyze specific DNA fragments in the field of molecular biology and clinical medicine. Recently, a variety of nanoparticles (NPs) have been regarded as a novel method to enhance both the quality and yield of PCR technique. Herein, we report the use of generation 5 (G5) poly(amidoamine) (PAMAM) dendrimers and dendrimer-entrapped gold nanoparticles (Au DENPs) modified with polyethylene glycol (PEG) moieties and (or) acetyl groups as a novel class of enhancers to improve the PCR amplification. We set up the nonspecific PCR and two-round PCR as model systems to investigate mechanisms of enhanced PCR. Our results show that dendrimer-based derivatives seem to enhance the PCR specificity. It is worth noting that the modification of antifouling PEG significantly lowered the optimization capability of the corresponding dendrimers, although this inhibition effect can be remarkably compromised by the entrapment of Au NPs. Furthermore, we found that in the presence of Au NPs, the thermal conductivity induced by Au NPs could play the dominant role in the PCR optimization, whereas in the absence of Au NPs, the electrostatic interaction of the dendrimers with PCR components may be the major factor affecting the PCR system. Our results also showed that the optimal concentrations of the materials in the two test systems were very close, which indicates that the developed dendrimer derivatives with good thermal stability may be the efficient PCR additives for enhancing different PCR systems.
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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) | 0.001 | 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 | 0.000 | 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 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".