Testing the Interaction between NOD-2 Status and Serological Response to<i>Mycobacterium paratuberculosis</i>in Cases of Inflammatory Bowel Disease
Bibliographic record
Abstract
In a population-based case-control study we have previously shown that 14% of healthy Manitobans carry one or two mutations in the NOD-2 locus, a gene highly associated with Crohn's disease (CD). The NOD-2 protein is the receptor responsible for recognition of bacterial peptidoglycans, and it is plausible that NOD-2 is involved in the recognition of mycobacteria. Thirty-seven percent of Manitobans with CD had >or=1 NOD-2 mutation, leading to a threefold increased risk of CD for single-mutant carriers and a 30-fold increased risk for double-mutant carriers. In the same population groups, we assessed the seroprevalence for Mycobacterium paratuberculosis and found it to be 35%, with no differences between CD, ulcerative colitis (UC), and controls. Because of high rates of CD and UC in Manitoba, we assessed whether there was an interaction between carrying a NOD-2 mutation and M. paratuberculosis seropositivity. An enzyme-linked immunosorbent assay for serum antibodies to M. paratuberculosis in cattle was adapted for human use. DNA was purified from whole blood. Subjects were genotyped for three NOD-2 variants, G908R, Cins1007fs, and R702W. Multivariate logistic regression analysis showed that NOD-2 gene mutations significantly associated with CD, but M. paratuberculosis serology did not. Furthermore, there was no interaction between NOD-2 mutation status and M. paratuberculosis serology status. For those with the NOD-2 mutation, the likelihood of CD subjects having positive M. paratuberculosis serology was similar to that of controls (odds ratio, 1.31; 95% confidence interval, 0.55-3.11). No interaction could be proven for UC or by combining CD and UC compared to controls. In conclusion, we could not find an interaction between the NOD-2 genotype and M. paratuberculosis serology in relationship to CD or UC.
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.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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 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".