Plastic Polymers for Efficient DNA Microarray Hybridization: Application to Microbiological Diagnostics
Bibliographic record
Abstract
Fabrication of microarray devices using traditional glass slides is not easily adaptable to integration into microfluidic systems. There is thus a need for the development of polymeric materials showing a high hybridization signal-to-background ratio, enabling sensitive detection of microbial pathogens. We have developed such plastic supports suitable for highly sensitive DNA microarray hybridizations. The proof of concept of this microarray technology was done through the detection of four human respiratory viruses that were amplified and labeled with a fluorescent dye via a sensitive reverse transcriptase PCR (RT-PCR) assay. The performance of the microarray hybridization with plastic supports made of PMMA [poly(methylmethacrylate)]-VSUVT or Zeonor 1060R was compared to that with high-quality glass slide microarrays by using both passive and microfluidic hybridization systems. Specific hybridization signal-to-background ratios comparable to that obtained with high-quality commercial glass slides were achieved with both polymeric substrates. Microarray hybridizations demonstrated an analytical sensitivity equivalent to approximately 100 viral genome copies per RT-PCR, which is at least 100-fold higher than the sensitivities of previously reported DNA hybridizations on plastic supports. Testing of these plastic polymers using a microfluidic microarray hybridization platform also showed results that were comparable to those with glass supports. In conclusion, PMMA-VSUVT and Zeonor 1060R are both suitable for highly sensitive microarray hybridizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 0.002 |
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".