Comparison of culture and qPCR methods in detection of mycobacteria from drinking waters
Bibliographic record
Abstract
Environmental mycobacteria are common bacteria in man-made water systems and may cause infections and hypersensitivity pneumonitis via exposure to water. We compared a generally used cultivation method and a quantitative polymerase chain reaction (qPCR) method to detect mycobacteria in 3 types of drinking waters: surface water, ozone-treated surface water, and groundwater. There was a correlation between the numbers of mycobacteria obtained by cultivation and qPCR methods, but the ratio of the counts obtained by the 2 methods varied among the types of water. The qPCR counts in the drinking waters produced from surface or groundwater were 5 to 34 times higher than culturable counts. In ozone-treated surface waters, both methods gave similar counts. The ozone-treated drinking waters had the highest concentration of assimilable organic carbon, which may explain the good culturability. In warm tap waters, qPCR gave 43 times higher counts than cultivation, but both qPCR counts and culturable counts were lower than those in the drinking waters collected from the same sites. The TaqMan qPCR method is a rapid and sensitive tool for total quantitation of mycobacteria in different types of clean waters. The raw water source and treatments affect both culturability and total numbers of mycobacteria in drinking waters.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".