Bioaerosols Play a Major Role in the Nasopharyngeal Microbiota Content in Agricultural Environment
Bibliographic record
Abstract
Background: Bioaerosols are a major concern for public health and sampling for exposure assessment purposes is challenging. The nasopharyngeal region could be a potent carrier of long-term bioaerosol exposure agents. This study aimed to evaluate the correlation between nasopharyngeal bacterial flora of swine workers and the swine barns bioaerosol biodiversity. Methods: Air samples from eight swine barns as well as nasopharyngeal swabs from pig workers (n = 25) and from a non-exposed control group (n = 29) were sequenced using 16S rRNA gene high-throughput sequencing. Wastewater treatment plants were used as the industrial, low-dust, non-agricultural environment control to validate the microbial link between the bioaerosol content (air) and the nasopharynxes of workers. Results: A multivariate analysis showed air samples and nasopharyngeal flora of pig workers cluster together, compared to the non-exposed control group. The significance was confirmed with the PERMANOVA statistical test (p-value of 0.0001). Unlike the farm environment, nasopharynx samples from wastewater workers did not cluster with air samples from wastewater treatment plants. The difference in the microbial community of nasopharynx of swine workers and a control group suggest that swine workers are carriers of germs found in bioaerosols. Conclusion: Nasopharynx sampling and microbiota could be used as a proxy of air sampling for exposure assessment studies or for the determination of exposure markers in highly contaminated agricultural environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".