Mycobacterial infection influences bone biomarker levels in patients with Crohn’s disease
Bibliographic record
Abstract
Patients with Crohn’s disease (CD) have higher risk for osteoporosis following decreased level of osteocalcin. We hypothesize that active inflammation following Mycobacterium avium subsp. paratuberculosis (MAP) infection results in elevation of undercarboxylated osteocalcin (ucOC) and downregulation of active osteocalcin in CD patients and cow-disease model (Johne’s disease). In this study, we measured ucOC, active osteocalcin, and calcium levels in sera from 42 cattle (21 infected with MAP and 21 healthy cattle), 18 CD patients, and 20 controls. The level of ucOC in MAP+ bovine samples was higher than that in MAP− controls (318 ± 57.2 nmol/mL vs. 289 ± 95.8 nmol/mL, P > 0.05). Consequently, mean calcium level in bovine MAP+ was significantly higher than that in bovine-MAP− samples (9.98 ± 0.998 mg/dL vs. 7.65 ± 2.12 mg/dL, P < 0.05). Also, the level of ucOC was higher in CD-MAP+ than in CD-MAP− (561 ± 23.7 nmol/mL vs. 285 ± 19.6 nmol/mL, P < 0.05). Interestingly, the mean osteocalcin level in MAP+ bovine was lower than that in MAP− bovine (797 ± 162 pg/mL vs. 1190 ± 43 pg/mL) and it was lower in CD-MAP+ than in CD-MAP− infection (1.89 ± 0.184 ng/mL vs. 2.19 ± 0.763 ng/mL) (P < 0.05). The correlation between MAP infection and elevation of sera ucOC, reduction of active osteocalcin and increased calcium supports MAP infection role in CD and complications with osteoporosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".