Bibliographic record
Abstract
Mycobacteria are responsible for several human and animal diseases. NOD2 is a pattern recognition receptor that has an important role in mycobacterial recognition. However, the mechanisms by which mutations in NOD2 alter the course of mycobacterial infection remain unclear. Herein, we aimed to review the totality of studies directly addressing the relationship between NOD2 and mycobacteria as a foundation for moving the field forward. NOD2 was linked to mycobacterial infection at 3 levels: (1) genetic, through association with mycobacterial diseases of humans; (2) chemical, through the distinct NOD2 ligand in the mycobacterial cell wall; and (3) immunologic, through heightened NOD2 signaling caused by the unique modification of the NOD2 ligand. The immune response to mycobacteria is shaped by NOD2 signaling, responsible for NF-κB and MAPK activation, and the production of various immune effectors like cytokines and nitric oxide, with some evidence linking this to bacteriologic control. Absence of NOD2 during mycobacterial infection of mice can be detrimental, but the mechanism remains unknown. Conversely, the success of immunization with mycobacteria has been linked to NOD2 signaling and NOD2 has been targeted as an avenue of immunotherapy for diseases even beyond mycobacteria. The mycobacteria-NOD2 interaction remains an important area of study, which may shed light on immune mechanisms in disease.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".