Detection and quantification of the human‐specific HF183 <i>Bacteroides</i> 16S rRNA genetic marker with real‐time PCR for assessment of human faecal pollution in freshwater
Bibliographic record
Abstract
The human-specific HF183 Bacteriodes 16S rRNA genetic marker can be used to detect human faecal pollution in water environments. However, there is currently no method to quantify the prevalence of this marker in environmental samples. We developed a real-time polymerase chain reaction (PCR) assay using SYBR Green I detection to quantify this marker in faecal and environmental samples. To decrease the amplicon length to a suitable size for real-time PCR detection, a new reverse primer was designed and validated on human and animal faecal samples. The use of the newly developed reverse primer in combination with the human-specific HF183 primer did not decrease the specificity of the real-time PCR assay but a melting curve analysis must always be included. This new assay was more sensitive than conventional PCR and highly reproducible with a coefficient of variation of less than 1% within an assay and 3% between assays. As the Bacteroides species that carries this human-specific marker has never been isolated, a bacteria real-time assay was used to determine the detection efficiency. The estimated detection efficiency in freshwater ranged from 78% to 91% of the true value with an average detection efficiency of 83+/-4% of the true value. Using a simple filtration method, the limit of quantification was 4.7+/-0.3x10(5) human-specific Bacteroides markers per litre of freshwater. The aerobic incubation of the human-specific Bacteroides marker in freshwater for up to 24 days at 4 and 12 degrees C, and up to 8 days at 28 degrees C, indicated that the marker persisted up to the end of the incubation period for all incubation temperatures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".