Development of an Aptamer-functionalized Magnetic Particle Sample Purification Protocol for the Plasmonic Polymerase Chain Reaction Diagnosis of Severe Acute Respiratory Syndrome Coronavirus 2
Bibliographic record
Abstract
BackgroundDespite having endured numerous pandemics throughout history and working with 21 st century technology, the world was still unprepared to deal with the emergence of the novel pathogen known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019.Up until the development and distribution of effective vaccines, health officials relied heavily on diagnostics to curtail the spread of disease.Reverse transcription-polymerase chain reaction (RT-PCR) still remains the gold standard for diagnosing SARS-CoV-2 due to its exceptional sensitivity and specificity.The rapid increase in the number of COVID-19 cases as well as the reliance on centralized laboratories noticeably prolonged the duration between patient sample collection and test result communication.In the meantime, asymptomatic individuals could unknowingly infect more people, thus propagating the disease throughout their community even faster.Although at-home rapid antigen tests provided diagnoses in minutes rather than days, their reduced sensitivity and varying success at detecting variants generated false-negative results, especially in patients with lower viral loads. Furthermore, clinical sample preparation techniques for SARS-CoV-2 are sub-optimal.Nasopharyngeal swabs are stored in large volumes of viral transport medium, yet only a fraction of the sample is used for subsequent nucleic acid isolation.This non-specific process co-purifies unwanted genetic material from nasal epithelial cells, thereby diluting the target RNA template, and potential PCR inhibitors.Moreover, only a few microliters of this purified sample are used in the PCR mix.Altogether, from sample collection to RT-PCR analysis, this procedure takes hours to complete.
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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 | 0.001 |
| 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 | 0.001 | 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".