A Distinct Plasma Microbiome But Not Gut Microbiome in Patients With Systemic Lupus Erythematosus Compared to Healthy Individuals
Bibliographic record
Abstract
OBJECTIVE: Blood microbiome has been analyzed in cancer patients using machine learning. We aimed to study whether the plasma microbiome represents the microbial community in the gut among patients with systemic lupus erythematosus (SLE) and healthy controls (HCs). METHODS: Paired plasma and stool samples from female patients with SLE and female HCs were assessed for microbiome composition by microbial 16S ribosomal RNA sequencing. RESULTS: Decreased microbial alpha diversity in stool compared to plasma and distinct plasma and gut beta diversity were found in both HCs and patients with SLE. No difference in gut microbial diversity was found; however, plasma alpha diversity was decreased in patients with SLE compared to HCs. The predominant bacteria differed between plasma and stool in both groups. Although the predominant plasma and stool genus bacteria were similar in patients with SLE and HCs, some were clearly different. CONCLUSION: Compared to the gut, the plasma microbiome contained distinct community and greater heterogeneity, indicating that the predominant circulating microbiome may originate from sites (eg, oral or skin) other than the gastrointestinal tract. The decreased plasma but not gut alpha diversity in patients with SLE compared to HCs implies an altered plasma microbiome in SLE, which may be important for systemic immune perturbations and SLE disease pathogenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".