Molecular Typing of Legionella pneumophila Isolates in the Province of Quebec from 2005 to 2015
Bibliographic record
Abstract
Legionella is found in natural and man-made aquatic environments, such as cooling towers and hot water plumbing infrastructures. Legionella pneumophila serogroup 1 (Lp1) is the most common etiological agent causing waterborne disease in the United States and Canada. This study reports the molecular characterization of Lp strains during a 10 year period. We conducted sequence-based typing (SBT) analysis on a large set of Lp isolates (n = 284) to investigate the province of Quebec sequence types (STs) distribution in order to identify dominant clusters. From 2005 to 2015, 181 clinical Lp isolates were typed by SBT (141 sporadic cases and 40 outbreak related cases). From the same period of time, 103 environmental isolates were also typed. Amongst the 108 sporadic cases of Lp1 typed, ST-62 was the most frequent (16.6%), followed by ST-213 (10.2%), ST-1 (8.3%) and ST-37 (8.3%). Amongst other serogroups (SG), ST-1327 (SG5) (27.3%) and ST-378 (SG10) (12.2%) were the most frequent. From the environmental isolates, ST-1 represent the more frequent SBT type (26.5%). Unweighted pair group method with arithmetic mean (UPGMA) dendrogram from the 108 sporadic cases of SG1 contains 4 major clusters (A to D) of related STs. Cluster B contains the majority of the strains (n = 61) and the three most frequent STs in our database (ST-62, ST-213 and ST-1). During the study period, we observed an important increase in the incidence rate in Quebec. All the community associated outbreaks, potentially or confirmed to be associated with a cooling tower were caused by Lp1 strains, by opposition to hospital associated outbreaks that were caused by serogroups of Lp other than SG1. The recent major Quebec City outbreak caused by ST-62, and the fact that this genotype is the most common in the province supports whole genome sequencing characterization of this particular sequence type in order to understand its evolution and associated virulence factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".