Rapid and sensitive detection of viral nucleic acids using silicon microchips
Bibliographic record
Abstract
Clinical laboratory-based nucleic acid amplification tests (NAT) play an important role in diagnosing viral infections. However, laboratory infrastructure requirements and their failure to diagnose at the point-of-need (PON) limit their clinical utility in both resource-rich and -limited clinical settings. The development of fast and sensitive PON viral NAT may overcome these limitations. The scalability of silicon microchip manufacturing combined with advances in silicon microfluidics present an opportunity for development of rapid and sensitive PON NAT on silicon microchips. In the present study, we present rapid and sensitive NAT for a number of RNA and DNA viruses on the same silicon microchip platform. We first developed sensitive (4 copies per reaction) one-step RT-qPCR and qPCR assays detecting HCV, HIV, Zika, HPV 16, and HPV 18 on a benchtop real-time PCR instrument. A silicon microchip was designed with an etched 1.3 μL meandering microreactor, integrated aluminum heaters, thermal insulation trenches and microfluidic channels; this chip was used in all on-chip experiments. Melting curve analysis confirmed precise and localized heating of the microreactor. Following minimal optimization of reaction conditions, the bench-scale assays were successfully transferred to 1.3 μL silicon microreactors with reaction times of 25 min with no reduction in sensitivity, reproducibility, or reaction efficiencies. Taken together, these results demonstrate that rapid and sensitive detection of multiple viruses on the same silicon microchip platform is feasible. Further development of this technology, coupled with silicon microchip-based nucleic acid extraction solutions, could potentially shift viral nucleic acid detection and diagnosis from centralized clinical laboratories to the PON.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".