Immune Stimulation Using a Gut Microbe-Based Immunotherapy Reduces Disease Pathology and Improves Barrier Function in Ulcerative Colitis
Bibliographic record
Abstract
Background: Current ulcerative colitis (UC) treatments are focused on symptom management primarily via immune suppression. Despite the current arsenal of immunosuppressant treatments, the majority of patients with UC still experience disease progression. Importantly, aggressive long-term inhibition of immune function comes with consequent risk, such as serious infections and malignancy. There is thus a recognized need for new, safe and effective treatment strategies for people living with UC that work upstream of managing the symptoms of the disease. The objective of this study was to evaluate a microbial-based treatment, QBECO, that functions to productively activate rather than suppress mucosal immune function as a novel approach to treat UC. Methods: Two established models of experimental colitis, namely chemically-induced DSS colitis and the spontaneous colitis that develops in Muc2 deficient mice, were used to assess whether QBECO treatment could ameliorate gastrointestinal disease. A small exploratory 16-week QBECO open-label trial was subsequently conducted to test the safety and tolerability of this approach and also to determine whether similar improvements in clinical disease and histopathology could be demonstrated in patients with moderate-to-severe UC. Results: QBECO treatment successfully reduced inflammation and promoted mucosal and histological healing in both experimental models and in UC patients. The preclinical models of colitis showed that QBECO ameliorated mucosal pathology, in part by reducing inflammatory cell infiltration, primarily that induced by neutrophils and inflammatory T cells. The most rapid and noticeable change observed in QBECO treated UC patients was a marked reduction in rectal bleeding. Conclusion: Collectively, this work demonstrates for the first time that strategically activating immune function rather than suppressing it, not only does not worsen colitis induced-damage, but may lead to an objective reduction in UC disease pathology.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".