Identification of SARS-CoV-2 biomarkers in saliva by transcriptomic and proteomics analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The detection of SARS-CoV-2 biomarkers by real time PCR (rRT-PCR) has shown that the sensitivity of the test is negatively affected by low viral loads and the severity of the disease. This limitation can be overcome by the use of more sensitive approaches such as mass spectrometry (MS), which has not been explored for the detection of SARS-CoV-2 proteins in saliva. Thus, this study aimed at assessing the translational applicability of mass spectrometry-based proteomics approaches to identify viral proteins in saliva from people diagnosed with COVID-19 within fourteen days after the initial diagnosis, and to compare its performance with rRT-PCR. After ethics approval, saliva samples were self-collected by 42 COVID-19 positive and 16 healthy individuals. Samples from people positive for COVID-19 were collected on average on the sixth day (± 4 days) after initial diagnosis. Viable viral particles in saliva were heat-inactivated followed by the extraction of total proteins and viral RNA. Proteins were digested and then subjected to tandem MS analysis (LC-QTOF-MS/MS) using a data-dependent MS/MS acquisition qualitative shotgun proteomics approach. The acquired spectra were queried against a combined SARS-CoV-2 and human database. The qualitative detection of SARS-CoV-2 specific RNA was done by rRT-PCR. SARS-CoV-2 proteins were identified in all COVID-19 samples (100%), while viral RNA was detected in only 24 out of 42 COVID-19 samples (57.1%). Seven out of 18 SARS-CoV-2 proteins were identified in saliva from COVID-19 positive individuals, from which the most frequent were replicase polyproteins 1ab (100%) and 1a (91.3%), and nucleocapsid (45.2%). Neither viral proteins nor RNA were detected in healthy individuals. Our mass spectrometry approach appears to be more sensitive than rRT-PCR for the detection of SARS-CoV-2 biomarkers in saliva collected from COVID-19 positive individuals up to 14 days after the initial diagnostic test. Based on the novel data presented here, our MS technology can be used as an effective diagnostic test of COVID-19 for initial diagnosis or follow-up of symptomatic cases, especially in patients with reduced viral load.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it