Oral Microbiota Composition in Children and Adults During Spanish COVID-19 Lockdown: Impact of Home Self-Confinement and SARS-CoV-2 infection
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic changed society’s habits and customs due to the social restrictions and health measures imposed during the first half of 2020. This study analyzes the composition of the oral microbiota in relation to age, household cohabitation, SARS-CoV-2 infection, and COVID-19 severity among children and adults under home confinement in Barcelona, Spain. Methods A prospective study conducted involving children and adults confined during the COVID-19 pandemic in the Barcelona Metropolitan Area between April and June 2020 included multiple cases of several participants living within the same family household. Saliva samples were collected from all participants, and microbiota composition was characterized through 16S rRNA gene sequencing. Results A total of 142 adults and 265 children living in 121 family households were included in the study. All 142 adults had a prior confirmed SARS-CoV-2 infection, and 20 (14.08%) of them had a history of severe COVID-19. SARS-CoV2 infection was detected in 58/265 (21.89%) of children; all of them were asymptomatic. Oral microbiota composition and diversity did not differ by SARS-CoV-2 infection status in children. In contrast, adults with severe COVID-19 exhibited lower microbiota diversity and distinct microbiota composition compared to those with mild disease symptoms. Age-related differences in oral microbiota composition were marked in the younger children groups. Additionally, cohabiting individuals shared more Amplicon Sequence Variants (ASVs) than non-cohabitants. Conclusions Age and cohabitation strongly influenced oral microbial composition. Our study demonstrates that oral microbiota composition in adults varies according to COVID-19 severity, whereas such microbial shifts are not observed in asymptomatic pediatric populations, regardless of infection status.
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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 | 0.001 |
| 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".