Sputum Production and Salivary Microbiome in COVID-19 Patients Reveals Oral-Lung Axis
Bibliographic record
Abstract
Abstract Objective SARS-CoV-2 is a severe respiratory disease that primarily targets the lungs and was the leading cause of death worldwide during the pandemic. Investigating the intricate interplay between the oral microbiome and inflammatory cytokines during the acute phase of infection is crucial for understanding host immune responses. This study aimed to explore the relationship between the oral microbiome and cytokines in COVID-19 patients, specifically examining those with and without sputum production. Methods Saliva and blood samples from 50 COVID-19 patients were subjected to 16S ribosomal RNA gene sequencing to analyze the oral microbiome. Additionally, 65 saliva and serum cytokines were assessed using Luminex multiplex analysis. The Mann-Whitney test compared cytokine levels between individuals with and without sputum production. Results Our study revealed significant differences in the membership (Jaccard dissimilarity: p=0.016) and abundance (PhILR dissimilarity: p=0.048; metagenomeSeq) of salivary microbial communities between COVID-19 patients with and without sputum production. Seven bacterial genera, including Prevotella, Streptococcus, Actinomyces, Atopobium, Filifactor, Leptotrichia, and Selenomonas, were present in statistically higher proportions of patients with sputum production (p<0.05, Fisher’s exact test). Eight bacterial genera, including Prevotella, Megasphaera, Stomatobaculum, Leptotrichia, Veillonella, Actinomyces, Atopobium, and Corynebacteria were significantly more abundant in the sputum-producing group, while Lachnoacaerobaculum was notably more prevalent in the non-sputum-producing group (p<0.05, ANCOM-BC).We observed a significant positive correlation between salivary IFN-gamma (Interferon-gamma) and Eotaxin2/CCL24 (chemokine ligand 24) with sputum production. Conversely, negative correlations were noted in serum MCP3/CCL7 (monocyte-chemotactic protein 3/Chemokine ligand 7), MIG/CXCL9 (Monokine induced by gamma/Chemokine ligand 9), IL1 beta (interleukin 1 beta), and SCF (stem cell factor) with sputum production (p<0.05, Mann-Whitney test). Conclusion Substantial distinctions in salivary microbial communities were evident between COVID-19 patients with and without sputum production, emphasizing the notable impact of sputum production on the oral microbiome and cytokine levels during the acute phase of infection.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".