Mycobacterium tuberculosis and SARS-CoV-2 co-infections: The knowns and unknowns
Bibliographic record
Abstract
(Mtb) and SARS-CoV-2 co-infections are not fully understood. Both pathogens modulate host responses and induce immunopathology with extensive lung damage. With a quarter of the world's population harboring latent TB, exploring the relationship between SARS-CoV-2 infection and its effect on the transition of Mtb from latent to active form is paramount to control this pathogen. The effects of active Mtb infection on establishment and severity of COVID-19 are also unknown, despite the ability of TB to orchestrate profound long-lasting immunopathologies in the lungs. Absence of mechanistic studies and co-infection models hinder the development of effective interventions to reduce the health impacts of SARS-CoV-2 and Mtb co-infection. Here, we highlight dysregulated immune responses induced by SARS-CoV-2 and Mtb, their potential interplay, and implications for co-infection in the lungs. As both pathogens master immunomodulation, we discuss relevant converging and diverging immune-related pathways underlying SARS-CoV-2 and Mtb co-infections.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".