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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".