In situ dosing of monochloramine in a hospital hot water system results in drastic microbial communities changes
Bibliographic record
Abstract
Understanding changes in microbial composition under selective pressures is crucial to assess the emergence of resistant taxa and the survival of drinking water-associated pathogens. This study evaluated the impact of in situ monochloramine disinfection in a hospital hot water system on bacterial (16S rRNA gene amplicon sequencing, 112 samples) and eukaryotic communities (18S rRNA gene amplicon sequencing, 103 samples), and on general microbial measurements (180 samples), including adenosine triphosphate (ATP) and flow cytometry counts. After the onset of treatment, ATP decreased by 1.2- and 3.5-fold, and total cell counts (TCC) dropped by 1- and 2-log at distal and system sites, respectively. During the dosage interruption (27-day), TCC rebounded to pre-treatment levels, but viability percentage decreased, indicating that cells were predominantly damaged. Low-use sites (e.g., showerheads) showed elevated ATP (>15 pg/mL) and TCC (10 5 –10 6 cells/L). Monochloramine drastically altered bacterial and eukaryotic communities. Alpha-diversity showed increased amplicon sequence variant richness during treatment, driven by new, low-abundant taxa, while Beta-diversity revealed distinct shifts in community composition over time, with tight or looser clusters corresponding to each treatment phase. Post-treatment, temporal and spatial heterogeneity was evident across distal sites, while elevated temperatures, consistent flow, and higher monochloramine concentrations in the hot water system resulted in more uniform communities at system sites. Additionally, the persistence of potential pathogenic strains belonging to Legionella and Mycobacterium genera highlights the value of comprehensive risk assessments. These findings emphasize the need to understand microbial shifts under disinfection stress and their public health implications, offering new insights into how treatment interventions shape microbial ecology and pathogen dynamics.
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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 | 0.000 | 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".