Denosumab Regulates Gut Microbiota Composition and Cytokines in Dinitrobenzene Sulfonic Acid (DNBS)-Experimental Colitis
Bibliographic record
Abstract
The pro-inflammatory mediator receptor activator of nuclear factor-kappa B ligand (RANKL) plays a significant role in the development of rheumatoid arthritis; however, its role in inflammatory bowel disease is unknown. Genome-wide association meta-analysis for Crohn's disease (CD) identified a variant near the TNFSF11 gene that encodes RANKL and CD risk allele increased expression of RANKL in specific cell lines. This study aims to elucidate if the RANKL inhibitor denosumab can reduce the severity of experimental colitis and modify the gut microbiota composition using murine dinitrobenzenesulfonic acid (DNBS)-experimental model of colitis mimicking CD. In colitic conditions, denosumab treatment significantly decreased the pro-inflammatory cytokines IL-6, IL-1β, and TNF-α within the colonic mucosa. Moreover, colitis was accompanied by disruption of gut microbiota, and preventative treatment with denosumab modulated this disruption. Denosumab treatment also modified the alpha- and beta diversity of colonic mucosa and fecal microbiota. These results provide a rationale for considering denosumab as a future potential therapy in CD; however, more detailed experimental and clinical studies are warranted.
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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 | 0.000 |
| 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".