Alterations in oral microbiomes in SARS-CoV-2 Omicron Variant Infected and Recovery Patients
Bibliographic record
Abstract
Objective Our study aimed to investigate the oral microbiome of patients infected with the Omicron variant (PIOV) and the changes in oral microbiota during the recovery of infection, compared to those infected with the original strain (PIOS) and provide a theoretical foundation for early diagnosis and disease prognosis of PIOV from the perspective of microecology. Design We collected 963 samples of tongue-coating prospectively, including 349 samples of PIOV, 242 samples of recovered patients from PIOV (RP), 300 samples of healthy controls (HC), and 72 samples of PIOS. We randomly selected tongue-coating samples from PIOV and HC at a ratio of 2:1, respectively, as the discovery cohort and validation cohort. Results Oral microbial diversity was significantly increased in PIOV. Compared to HC, conditional pathogenic bacteria were increased in PIOV. The classifier based on 6 optimal oral microbial markers had high diagnostic efficiency in both cohorts. Oral microbiota numbers were changed as the disease recovered. Conclusion For the first time, our study characterizes the oral microbiota of PIOV and RP, successfully establishes and validates the noninvasive diagnostic model of PIOV, and outlines the correlation between the OTUs of microbiota and clinical indicators.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".