Gut microbiome of treatment-naïve MS patients of different ethnicities early in disease course
Bibliographic record
Abstract
Although the intestinal microbiome has been increasingly implicated in autoimmune diseases, much is unknown about its roles in Multiple Sclerosis (MS). Our aim was to compare the microbiome between treatment-naïve MS subjects early in their disease course and controls, and between Caucasian (CA), Hispanic (HA), and African American (AA) MS subjects. From fecal samples, we performed 16S rRNA V4 sequencing and analysis from 45 MS subjects (15 CA, 16 HA, 14 AA) and 44 matched healthy controls, and whole metagenomic shotgun sequencing from 24 MS subjects (all newly diagnosed, treatment-naïve, and steroid-free) and 24 controls. In all three ethnic groups, there was an increased relative abundance of the same single genus, Clostridium, compared to ethnicity-matched controls. Analysis of microbiota networks showed significant changes in the network characteristics between combined MS cohorts and controls, suggesting global differences not restricted to individual taxa. Metagenomic analysis revealed significant enrichment of individual species within Clostridia as well as particular functional pathways in the MS subjects. The increased relative abundance of Clostridia in all three early MS cohorts compared to controls provides candidate taxa for further study as biomarkers or as etiologic agents in MS.
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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 | 0.001 | 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".