The respiratory microbiome and nontuberculous mycobacteria: an emerging concern in human health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it