Mass Spectrometric detection of SARS-CoV-2 virus in scrapings of the epithelium of the nasopharynx of infected patients via Nucleocapsid N protein
Bibliographic record
Abstract
ABSTRACT Detection of viral RNA by PCR is currently the main diagnostic tool for COVID-19 [1]. The PCR-based test, however, shows limited sensitivity, especially at early and late stages of the disease development [2,3], and is relatively time consuming. Fast and reliable complementary methods for detecting the viral infection would be of help in the current pandemia conditions. Mass-spectrometry is one of such possibilities. We have developed a mass-spectrometry based method for the detection of the SARS CoV-2 virus in nasopharynx epithelial swabs, based on the detection of the viral nucleocapsid N protein. The N protein of the SARS-COV-2 virus, the most abundant protein in the virion, is the best candidate for mass-spectrometric detection of the infection, and MS-based detection of several peptides from the SARS-COoV-2 nucleoprotein has been reported earlier by the Sinz group [4]. Our approach shows confident identification of the N protein in patient samples even with the lowest viral loads and a much simpler preparation procedure. Our main protocol consists of virus inactivation by heating and adding of isopropanol, and tryptic digestion of the proteins sedimented from the swabs followed by MS analysis. A set of unique peptides, produced as a result of proteolysis of the nucleocapsid phosphoprotein of SARS-CoV-2, is detected. The obtained results can further be used to create fast parallel mass-spectrometric approaches for the detection of the virus in the nasopharyngeal mucosa, saliva, sputum and other physiological fluids.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".