Molecular survey of occurrence and quantity of <i>Legionella</i> spp., <i>Mycobacterium</i> spp., <i>Pseudomonas aeruginosa</i> and amoeba hosts in municipal drinking water storage tank sediments
Bibliographic record
Abstract
AIMS: To examine the occurrence and quantity of potential pathogens and an indicator of microbial contamination in the sediments of municipal drinking water storage tanks (MDWSTs), given the absence of such data across the United States. METHODS AND RESULTS: Sediment samples (87 MDWST) from eighteen locations across ten states of the United States were collected and assayed by qPCR for a range of potential enteric and opportunistic microbial pathogens and a sewage-associated Bacteroides marker. Potential opportunistic pathogens dominated, with the highest detection of occurrence (per cent positive detection; average cell equivalence (CE)) being Mycobacterium spp. (88·9%; 6·7 ± 8·5 × 10(4) CE g(-1) ), followed by Legionella spp. (66·7%; 5·2 ± 5·9 × 10(3) CE g(-1) ), Pseudomonas aeruginosa (22·2%; 250 ± 880 CE g(-1) ) and Acanthamoeba spp. (38·9%; 53 ± 70 CE g(-1) ), with no detected Naegleria fowleri. Most enteric pathogens (Campylobacter jejuni, Escherichia coli 0157:H7, Salmonella enterica, Cryptosporidium parvum and Giardia duodenalis) were not detected, except for a trace signal for Campylobacter spp. There was significant correlation between the qPCR signals of Legionella spp. and Acanthamoeba spp. (R(2) = 0·61, n = 87, P = 0·0001). Diverse Legionella spp. including Leg. pneumophila, Leg. pneumophila sg1 and Leg. anisa were identified, each of which might cause legionellosis. CONCLUSIONS: These results imply that potential opportunistic pathogens are common within MDWST sediments and could act as a source of microbial contamination, but need downstream growth to be of potential concern. SIGNIFICANCE AND IMPACT OF THE STUDY: The results imply that opportunistic pathogen risks may need to be managed by regular tank cleaning or other management practices.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".