Bacterial Biomarkers of the Oropharyngeal and Oral Cavity during SARS-CoV-2 Infection
Bibliographic record
Abstract
(1) Background: Individuals with COVID-19 display different forms of disease severity and the upper respiratory tract microbiome has been suggested to play a crucial role in the development of its symptoms. (2) Methods: The present study analyzed the microbial profiles of the oral cavity and oropharynx of 182 COVID-19 patients compared to 75 unaffected individuals. The samples were obtained from gargle screening samples. 16S rRNA amplicon sequencing was applied to analyze the samples. (3) Results: The present study shows that SARS-CoV-2 infection induced significant differences in bacterial community assemblages, with Prevotella and Veillonella as biomarkers for positive-tested people and Streptococcus and Actinomyces for negative-tested people. It also suggests a state of dysbiosis on the part of the infected individuals due to significant differences in the bacterial community in favor of a microbiome richer in opportunistic pathogens. (4) Conclusions: SARS-CoV-2 infection induces dysbiosis in the upper respiratory tract. The identification of these opportunistic pathogenic biomarkers could be a new screening and prevention tool for people with prior dysbiosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".