Successful treatment of experimental colitis by a nanoparticle gene delivery system that localizes expression of interleukin-10 (IL-10) to the colon.
Bibliographic record
Abstract
IL-10 is a potent anti-inflammatory cytokine, critical in maintaining intestinal immune homeostasis. Defective IL-10 expression or function plays a pathogenic role in inflammatory bowel disease (IBD). Recent genome-wide association studies (GWAS) identified a strong association of an IL-10 gene polymorphism with ulcerative colitis (UC), but not with Crohn's disease (CD). This finding, together with recent elucidation of cellular and molecular mechanisms of IL-10 in the development of colitis, have led to a resurgence of interest in IL-10 as a therapeutic agent for UC. Despite encouraging early clinical results in treating IBD patients with subcutaneously injected recombinant IL-10, development was halted due to its failure to induce remission in subjects with CD and the occurrence of systemic dose-related adverse effects. This limited efficacy could be a result of the less prominent pathogenic role of IL-10 in CD, as evidenced by GWAS. Furthermore, injectable IL-10 as a treatment for IBD is hampered by its poor bioavailability in the intestine and short half-life in the bloodstream. Localized delivery of IL-10 to the colon could improve its therapeutic efficacy, while minimizing systemic exposure and related toxicity. To translate this strategy into a clinical treatment, we developed a chitosan-based nanoparticle DNA delivery system to target production of IL-10 protein to colonic epithelium.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".