A Novel, Rapid Diagnostic Molecular Method for Sars-Cov-2 Detectionby Nanoprobes
Bibliographic record
Abstract
Early diagnosis of active viral infections is crucial to prevent silent transmission among the population, as we have seen during the present COVID-19 pandemic.Single stranded RNA viruses, including SARS-CoV-2, are currently detected using molecular methods, such as the gold standard qRT-PCR.These methods require expensive equipment and specialized training, which limits its use as point-of-care (POC) diagnostic systems.We have developed a novel, fast and simple diagnostic method, based in the combination of Isothermal Loop Amplification (LAMP) [1], [2] and oligonucleotidefunctionalized gold nanoparticles (AuNPs).If viral RNA is amplified and recognized by de DNA probe, a change in gold nanoparticles aggregation state initiates, leading to a wavelength change in the region of the visible spectrum, evident by naked eye.The developed system has been designed by BioAssays SL to detect SARS-CoV-2.After successful results detecting synthetic targets, we have pre-validated our novel diagnostic system with a 364 sample panel from SARS-CoV-2 positive patients (83 positive samples and 281 negative samples).We analyzed two different primer sets, targeting Gen-E and Gen-N respectively, and we determined that the Gen-N primer set reliably detected SARS-CoV-2 RNA with an RT-qPCR cycle threshold (CT) number of up to 30, with a 98% sensitivity and 100% specificity compared to commercial qRT-PCR diagnostic kits.These results showed that RT-LAMP is sensitive enough to detect viral RNA within 30 min and a detection limit of 150 copies per mL when using these primers.To allow a colorimetric visualization of the results, we coupled LAMP amplification to AuNPs detection [3], [4].DNA-AuNPs (nanoprobes) were able to detect LAMP results in a saline buffer in 15 minutes, leading to aggregation in the absence of RT-LAMP amplification.Then, nanoprobes remained stable in saline buffer when they specifically recognized SARS-CoV-2 RT-LAMP product, and the red colour of the solution was indicative of a positive result.All positive samples amplified by RT-LAMP were successfully detected by nanoprobes.In conclusion, our LAMP detection system coupled to nanoprobe visualization is sensitive enough to detect viral RNA within less than 60 min by the naked eye, with 98% sensitivity and 100% specificity, overcoming sensitivity limitations of previously described nanoprobe systems [5].Due to the potential threat of re-emerging we have also designed a LAMPnanoprobe detection system for Hepatitis C virus and we are currently starting pre-validation phases too.Upon in vivo validation, a marketable diagnostic kit will be developed that can be implemented in the health system as a routine method for assay.The estimated cost for the diagnostic kit is 5€ per assay.The simplicity of this technology allows its ready transfer and optimization for to the detection of other viral diseases by RNA or ssDNA viruses such as measles virus, Zika, Dengue, HIV-1, or even animal viruses, such as Avian Metapneumovirus (aMPV).
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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 | 0.001 |
| 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 | 0.001 |
| 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".