The respiratory microbiome and nontuberculous mycobacteria: an emerging concern in human health
Bibliographic record
Abstract
, which has seen a reduction in disease rates in developed countries, the incidence and prevalence of NTM disease is increasing. NTM are difficult to treat with standard antimicrobial regimens and may contain both virulence and antibiotic-resistance genes with potential for pathogenicity. With the advent of molecular techniques, it has been elucidated that these organisms do not reside in isolation and are rather part of a complex milieu of microorganisms within the host lung microbiome. Over the last decade, studies have highlighted the impact of the microbiome on host immunity, metabolism and cell-cell communication. This recognition of a broader community raises the possibility that the microbiome may disrupt the balance between infection and disease. Additionally, NTM disease progression and antimicrobial therapy may affect the healthy steady state of the host and function of the microbiome, contributing to further dysbiosis and clinical deterioration. There have been limited studies assessing how NTM may influence the relationship between microbiome and host. In this review, we highlight available studies about NTM and the microbiome, postulate on virulence mechanisms by which these microorganisms communicate and discuss implications for treatment.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".