Evidence for Environmental–Human Microbiota Transfer at a Manufacturing Facility with Novel Work-related Respiratory Disease
Bibliographic record
Abstract
Abstract Rationale Workers’ exposure to metalworking fluid (MWF) has been associated with respiratory disease. Objectives As part of a public health investigation of a manufacturing facility, we performed a cross-sectional study using paired environmental and human sampling to evaluate the cross-pollination of microbes between the environment and the host and possible effects on lung pathology present among workers. Methods Workplace environmental microbiota were evaluated in air and MWF samples. Human microbiota were evaluated in lung tissue samples from workers with respiratory symptoms found to have lymphocytic bronchiolitis and alveolar ductitis with B-cell follicles and emphysema, in lung tissue samples from control subjects, and in skin, nasal, and oral samples from 302 workers from different areas of the facility. In vitro effects of MWF exposure on murine B cells were assessed. Measurements and Main Results An increased similarity of microbial composition was found between MWF samples and lung tissue samples of case workers compared with control subjects. Among workers in different locations within the facility, those that worked in the machine shop area had skin, nasal, and oral microbiota more closely related to the microbiota present in the MWF samples. Lung samples from four index cases and skin and nasal samples from workers in the machine shop area were enriched with Pseudomonas, the dominant taxa in MWF. Exposure to used MWF stimulated murine B-cell proliferation in vitro, a hallmark cell subtype found in the pathology of index cases. Conclusions Evaluation of a manufacturing facility with a cluster of workers with respiratory disease supports cross-pollination of microbes from MWF to humans and suggests the potential for exposure to these microbes to be a health hazard.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".