Effect of Disinfectant Exposure on <i>Legionella pneumophila</i> Associated with Simulated Drinking Water Biofilms: Release, Inactivation, and Infectivity
Bibliographic record
Abstract
Legionella pneumophila, the most commonly identified causative agent in drinking water associated with disease outbreaks, can be harbored by and released from drinking water biofilms. In this study, the release of biofilm-associated L. pneumophila under simulated drinking water flow containing a disinfectant residual was examined. Meanwhile, the inactivation and infectivity (to amoebae) of the released L. pneumophila were studied. To simulate drinking water system conditions, biofilms were prepared under either disinfectant exposure (predisinfected biofilms) or disinfectant-free (untreated biofilms) conditions, respectively. For experiments with water flow containing a disinfectant to release the biofilm-associated L. pneumophila from these two types of biofilms, the L. pneumophila release kinetics values from predisinfected and untreated biofilms under flow condition were not statistically different (one-way ANOVA, p > 0.05). However, inactivation of the L. pneumophila released from predisinfected biofilms was 1-2 times higher and amoeba infectivity was 2-29 times lower than that from untreated biofilms. The higher disinfectant resistance of L. pneumophila released from untreated biofilms was presumably influenced by the detachment of a larger amount of biofilm material (determined by 16S rRNA qPCR) surrounding the released L. pneumophila. This study highlights the interaction among disinfectant residual, biofilms, and L. pneumophila, which provides guidelines to assess and control pathogen risk.
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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) | 0.001 | 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) | 0.001 | 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".