Mitochondrial Multiplex Real-Time PCR as a Source Tracking Method in Fecal-Contaminated Effluents
Bibliographic record
Abstract
Multiplex real-time PCR amplifying fecal mitochondrial DNA (mtDNA) combined with rapid, crude DNA preparations are promising additions to surface water source tracking methods. Amplification of eukaryotic mitochondrial DNA identifies the fecal source directly and can be used in conjunction with other intestinal microbial methods to characterize effluents. Species-specific primers and dual-labeled probes for human, swine, and bovine NADH dehydrogenase subunit 5 (ND5) genes were created for multiplex real-time PCR in feces and effluent slurries. The linear range of the multiplex assay was 10(2)-10(7) mtDNA copies for human, bovine, and swine effluent in combination (equal volumes). PCR amplification efficiencies for bovine, human, and swine mtDNA when assayed in combination were 93, 107, and 92% respectively. Linear regression correlation coefficients (r2) were 0.99 for all standard curves except for human mtDNA in combination (r2 = 0.95). Multiplex amplification of bovine, human, and swine mtDNA (ND5) exhibited no cross-reactions between the effluents from three species of interest. Also, no cross-reactions were observed with effluents of other vertebrates: sheep, goat, horse, dog, cat, Canada goose, broiler, layer, turkey, and tilapia. Performed as a blind test, the PCR operator was able to correctly identify all but two effluent challenge samples (10/12 or 83% correct) with no false positives (22/22 or 100% correct). The multiplex assay had a tendency to detect the species of highest mtDNA concentration only. Better detection of all three species in a combination of human, bovine, and swine effluents was accomplished by running each real-time PCR primer/ probe set singly. Real-time PCR detection limit was calculated as 2.0 x 10(6) mitochondrial copies or 0.2 g of human feces per 100 mL effluent. Some carry-over mtDNA PCR signal from consumed beef, but not pork, was found in feces of human volunteers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".