Partitioning and probe-based quantitative PCR assays for the wastewater monitoring of <i>Mycobacterium tuberculosis</i> complex, <i>M. tuberculosis</i> , and <i>M. bovis</i>
Bibliographic record
Abstract
ABSTRACT Three new probe-based quantitative PCR assays were designed based on existing PCR assays to quantitate Mycobacterium tuberculosis complex (MTBC) species, M. tuberculosis (MTB), and M. bovis (MB) in wastewater targeting genomic regions rv0577, RD9, and the deletion of RD4, respectively. The assays were validated for specificity using four Mycobacterial species, including two MTBC species and two non-tuberculosis Mycobacteria species, and endogenous wastewater samples from Ottawa, Ontario, Canada, Mumbai, India, and a remote Northern Indigenous community in Nunangat with known ongoing tuberculosis cases or outbreaks. The three assays demonstrate high sensitivity and are suitable for use in wastewater. Partitioning experiments performed on endogenous MTBC and MTB in collected wastewaters from Mumbai, India with known tuberculosis outbreaks show that the targeted genomic regions of rv0577 (MTBC) and RD9 (MTB) used to quantitate human tuberculosis infection predominately partition to solids fraction of wastewaters. The partitioning results of this study, in combination with the presented probe-based PCR assays, provide guidance on how to best enrich wastewaters and rapidly and economically quantify tuberculosis with high specificity and sensitivity in wastewaters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".