Therapeutic effectiveness of orally administered transgenic low‐alkaloid tobacco expressing human interleukin‐10 in a mouse model of colitis
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) represents a spectrum of diseases in which inflammation leads to acute and chronic gut injury. It is a growing health issue for which no cure exists. The pathogenesis is multifactorial with links to infectious and environmental events that trigger disease in genetically predisposed individuals. Treatment of the two major forms of IBD, Crohn's disease and ulcerative colitis, involves the reduction of inflammation with toxic immunosuppressive drugs or blocking of the pro-inflammatory effects of tumour necrosis factor-alpha (TNF-alpha) with antibodies. Here, we show that the oral administration of transgenic low-alkaloid tobacco expressing the contra-inflammatory cytokine human interleukin-10 (hIL-10) reduces the severity of colitis by down-regulating TNF-alpha expression directly at the sites of inflammation in IBD-susceptible IL-10(-/-) mice. hIL-10 expressed in plants is biologically active and displays resistance to gastrointestinal degradation. Dietary supplementation with plant tissue delivering up to 9 microg of hIL-10 daily for 4 weeks was well tolerated by treated mice. Gut histology was significantly improved relative to controls (P = 0.002), and was correlated with a decrease in small bowel TNF-alpha mRNA levels and an increase in IL-2 and IL-1beta mRNA levels. Transgenic plants expressing IL-10 to directly attenuate TNF-alpha expression at sites of inflammation in the gut may become a useful new approach in the luminal therapy of IBD.
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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) | 0.000 | 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 | 0.001 | 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".