Molecular Biology and DNA Microarray Technology for Microbial Quality Monitoring of Water
Bibliographic record
Abstract
Public concern over polluted water is a major environmental issue worldwide. Microbial contamination of water arguably represents the most significant risk to human health on a global scale. An important challenge in modern water microbial quality monitoring is the rapid, specific, and sensitive detection of microbial indicators and waterborne pathogens. Presently, microbial tests are based essentially on time-consuming culture methods. Rapid microbiological analyses and detection of rare events in water systems are important challenges in water safety assessment since culture methods present serious limitations from both quantitative and qualitative points of view. To circumvent lengthy culture methods, newer enzymatic, immunological, and genetic methods are being developed as an alternative. DNA microarray technology is a new and promising tool that allows the detection of several hundred or even thousands DNA sequences simultaneously. Recent advances in sample processing and DNA microarray technologies provide new perspectives to assess microbial water quality. The aims of this review are to (1) summarize what is currently known about microbial indicators, (2) describe the most important waterborne pathogens, (3) present molecular methods used to monitor the presence of pathogens in water, and (4) show the potential of DNA microarrays in water quality monitoring.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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".