Hawaiian Volcanic Ash, an Airborne Fomite for Nontuberculous Mycobacteria
Bibliographic record
Abstract
Abstract Nontuberculous mycobacteria (NTM) are environmentally acquired opportunistic pathogens that can cause chronic lung disease. Within the U.S., Hawai'i shows the highest prevalence rates of NTM lung infections. Here, we investigated a potential role for active volcanism at the Kīlauea Volcano located on Hawai'i Island in promoting NTM growth and diversity. We recovered NTM that are known to cause lung disease from plumbing biofilms and soils collected from the Kīlauea environment. We also discovered viable Mycobacterium avium, Mycobacterium abscessus, and Mycobacterium intracellulare subsp. chimaera on volcanic ash collected during the 2018 Kīlauea eruption. Analysis of soil samples showed that NTM prevalence is positively associated with bulk content of phosphorus, sulfur, and total organic carbon. In growth assays, we showed that phosphorus utilization is essential for proliferation of Kīlauea‐derived NTM, and demonstrate that NTM cultured with volcanic ash adhere to ash surfaces and remain viable. Ambient dust collected on O'ahu concurrent with the 2018 eruption contained abundant fresh volcanic glass, suggestive of inter‐island ash transport. Phylogenomic analyses using whole genome sequencing revealed that Kīlauea‐derived NTM are genetically similar to respiratory isolates identified on other Hawaiian Islands. Consequently, we posit that volcanic eruptions could redistribute environmental microorganisms over large scales. While additional studies are needed to confirm a direct role of ash in NTM dispersal, our results suggest that volcanic particulates harbor and can redistribute NTM and should therefore be studied as a fomite for these burgeoning, environmentally acquired respiratory infections.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".