Modulation of experimental autoimmune encephalomyelitis through colonisation of the gut with Escherichia coli
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multiple sclerosis (MS) is a neuro-inflammatory autoimmune disease of the central nervous system (CNS) that affects young adults. It is characterised by the development of demyelinating lesions and inflammation within the CNS. Although the causes of MS are still elusive, recent work using patient samples and experimental animal models has demonstrated a strong relationship between the gut microbiota and its contribution to CNS inflammation and MS. While there is no cure for MS, alteration of the gut microbiota composition through the use of probiotics is a very promising treatment. However, while most recent works have focused on the use of probiotics to modify pre-existing disease, little is known about its role in protecting from the establishment of MS. In this study, we determined whether colonisation with the probiotic bacterium Escherichia coli strain Nissle 1917 (EcN) could be used as a prophylactic strategy to prevent or alter the development of experimental autoimmune encephalomyelitis (EAE), a preclinical model of MS. We found that double gavage (two doses) of EcN before induction of EAE delayed disease onset and decreased disease severity. We also found that EcN-treated mice had decreased amounts of perivascular cuffing, CD4 + T cell infiltration into the CNS, together with significantly decreased absolute numbers of Th1 cells, and reduced activation of microglia. Although further studies are necessary to comprehend the exact protective mechanisms induced, our study supports a promising use of EcN as a probiotic for the prevention of MS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it