A moderately elevated soy protein diet mitigates inflammatory changes in gut and in bone turnover during chronic TNBS-induced inflammatory bowel disease
Bibliographic record
Abstract
Inflammatory bowel disease is a condition that leads to gut pathologies such as abnormal lymphatic architecture, as well as to systemic comorbidities such as bone loss. Furthermore, current therapies are limited to low efficacy and incur side effects. Dietary interventions have been explored minimally, but may provide a treatment for improving gut outcomes and comorbidities. Indeed, plant-based soy protein has been shown to exert anti-inflammatory effects. Here, we tested the impact of a moderately elevated soy protein diet in a chronic, 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis model on gut and bone inflammatory-mediated pathophysiological adaptations. Colitis was induced by intrarectal administration of TNBS. Gut histopathology was scored, and lymphatic structural changes and the local inflammatory state were assessed via immunofluorescence. In addition, the effects of gut inflammation on bone turnover and osteocyte proteins were determined via histomorphometry and immunohistochemistry, respectively. The moderately elevated soy protein diet produced improvements in both colonic and bone tissues. In TNBS animals given the soy protein intervention, colon histological scores were reduced and the abnormal lymphatic architecture resolved. There were also improvements in bone formation and reduced bone resorption. In addition, TNBS increased inflammatory cytokines such as tumor necrosis factor-α and receptor activator of nuclear factor κ-B ligand in the gut and bone, but this was resolved in both tissues with the dietary soy protein intervention. The moderately elevated soy protein diet mitigated gut and bone inflammation in a chronic, TNBS-induced colitis model, demonstrating the potential for soy protein as a potential anti-inflammatory dietary intervention for inflammatory bowel 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 | 0.000 |
| 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) | 0.001 | 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".