Wastewater Monitoring of Mycobacterium Tuberculosis Complex, Mycobacterium Tuberculosis, and Mycobacterium bovis
Bibliographic record
Abstract
Tuberculosis (TB) remains the leading cause of death among infectious diseases, with 1.25 million fatalities reported globally in 2023. The World Health Organization estimates that 10.8 million individuals developed TB in the same year, with Southeast Asia and Africa accounting for the majority of cases. Despite low TB incidence rates overall in Canada, Indigenous populations in Canada, particularly the Inuit of Nunangat, continue to experience disproportionately high incidences of TB, outnumbering all other population groups in the country. Systemic inequalities, including overcrowded housing, food insecurity, and limited access to healthcare, present persistent challenges to equitable TB prevention and management. Current strategies for TB control in remote regions like Inuit Nunangat rely heavily on community-wide screenings and contact tracing. However, logistical barriers, including geographic isolation and insufficient health infrastructure, hinder the effectiveness of these interventions. Additionally, historical trauma associated with TB testing and treatment has led to medical hesitancy among Indigenous communities, further complicating public health monitoring. Wastewater and environmental monitoring (WEM) offers a non-invasive, anonymous alternative for TB monitoring, enabling the early detection of outbreaks without the need for individual clinical diagnoses. Although the detection of Mycobacterium tuberculosis species (MTBC) in wastewater has been demonstrated, current methodologies such as intercalating dye-based polymerase chain reaction (PCR) employ clinical assays that lack the sensitivity and specificity required for wastewater and environmental applications. Moreover, effective WEM implementation, requires an understanding of the partitioning behavior of TB markers in wastewaters to optimize nucleic acid extraction to improve detection and quantification methods, which has not yet been established. This study focuses on developing and validating probe-based quantitative PCR assays for the detection and quantification of MTBC species, including Mycobacterium tuberculosis and Mycobacterium bovis, in wastewater. By addressing the sensitivity and specificity challenges in current methodologies, and incorporating partitioning insights, this research aims to provide a foundation for implementing WEM as a disease monitoring system for TB in Northern Indigenous communities, ultimately advancing equitable public health solutions for underserviced, priority populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".