Characterization of Microbial Communities on Caps in Controlled Human Exposures
Bibliographic record
Abstract
Background: While a few small studies suggest that endotoxin from gram-negative bacteria on ambient pollution particles may increase systemic inflammation, the contribution of microbes on pollution particles to their inflammatory and cardiovascular effects is poorly understood. Methods/ Results: We conducted a blinded, randomized, cross-over trial of controlled human exposures to concentrated ambient particles (CAPs). Fifty-five healthy adult volunteers were exposed for 130 min to fine, coarse, or ultrafine CAPs; HEPA-filtered air or medical air with = 2-week washout between exposures. CAPs were sampled immediately prior to testing (calibration samples), and during each exposure. We conducted a pilot study to characterize the bacterial and fungal communities on CAPs at the phylum and genus level with the ultimate goal of assessing their health effects. Metagenomic DNA was extracted from CAPs on nine pre-exposure calibration filters, and 16S, 18S, and ITS sequencing was performed. Scaling to 2,000 reads, bacterial and fungal relative abundance was characterized at the phylum and genus level. The five most abundant bacterial phyla were Proteobacteria (59%) (i.e, gram negatives, which varied by CAP sample with a relative abundance ranging from 50% to 71% with 16 distinct genera identified), Actinobacteria (15%), Firmicules (10%), Bacteroidetes (8%), and Cyanobacteria/Chloroplast (5%). For fungi, by ITS sequencing the most abundant phyla were Basidiomycota (overall relative abundance: 83%; range: 73-95%), followed by Ascomycota (16%), Polyporales, Glomeromycota, and Zygomycota. Twenty-three fungal genera were identified, including Alternaria and Penicillium in low abundance. Further classification will be performed. Conclusion: Metagenomic characterization of the variability of bacterial and fungal communities on CAPs opens up opportunities to assess the contribution of microbes on ambient pollution particles to their inflammatory and physiologic effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".