Quantification and cultivation of Helicobacter pylori (H. pylori) from various urban water environments: A comprehensive analysis of precondition methods and sample characteristics
Bibliographic record
Abstract
Substantial evidence suggests that all types of water, such as drinking water, wastewater, surface water, and groundwater, can be potential sources of Helicobacter pylori (H. pylori) infection. Thus, it is critical to thoroughly investigate all possible preconditioning methods to enhance the recovery of H. pylori, improve the reproducibility of subsequent detection, and optimize the suitability for various water types and different detection purposes. In this study, we proposed and evaluated five distinct preconditioning methods for treating water samples collected from multiple urban water environments, aiming to maximize the quantitative qPCR readouts and achieve effective selective cultivation. According to the experimental results, when using the qPCR technique to examine WWTP influent, effluent, septic tank, and wetland water samples, the significance of having a preliminary cleaning step becomes more evident as it can profoundly influence qPCR detection results. In contrast, the simple, straightforward membrane filtration method could perform best when isolating and culturing H. pylori from all water samples. Upon examining the cultivation and qPCR results obtained from groundwater samples, the presence of infectious H. pylori (potentially other pathogens) in aquifers must represent a pressing environmental emergency demanding immediate attention. Furthermore, we believe groundwater can be used as a medium to reflect the H. pylori prevalence in a highly populated community due to its straightforward analytical matrix, consistent detection performance, and minimal interferences from human activities, temperature, precipitation, and other environmental fluctuations.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".