Alterations of Plasma Microbiome: A Potentially New Perspective to the Dysbiosis in Systemic Lupus Erythematosus?
Bibliographic record
Abstract
The human microbiome, which consists of the microbial communities inhabiting the human body, has sparked growing excitement in both basic research and clinical practice.1,2 Gut microbiota, in particular, has been considered a major environmental factor in modulating immune responses in autoimmune diseases (ADs).3,4 Systemic lupus erythematosus (SLE) is a prototype AD characterized by dysregulation of both innate and adaptive immune responses, autoantibody production, multiorgan involvement, and upregulation of interferon-stimulated genes.5,6,7 The etiology of SLE remains unclear but is partially attributed to a combination of genetic and environmental factors. As a critical environmental factor, dysbiosis is linked to SLE immunopathogenesis.3,4,8,9,10,11 Patients with SLE displayed decreased richness and diversity of gut microbiota compared to controls.8,9,10,11 Further, a significantly lower ratio of Firmicutes to Bacteroidetes in patients with SLE was reported in several studies.3 The implication of gut dysbiosis in SLE pathogenesis was further corroborated by the findings that cecal microbiota transfer from SLE-prone mice induced autoimmune phenotypes in germ-free congenic C57BL/6 mice.12 With the introduction of metagenomics and metabolomics, a more complicated interaction between host and gut microbiome has been revealed in SLE.8,9 We recently identified 2 autoantigen cross-reacting peptides from SLE-enriched species in the gut with the ability to promote the production of inflammatory cytokines.8 Of interest, the gut microbiome also helps reveal novel relationships among complex human diseases. A recent study found that SLE and chronic myeloid leukemia shared some common gut microbiome features, suggesting that different complex diseases may be mechanistically correlated by sharing certain common gut microbiome features.13 In addition to alterations in gut microbiota, oral and skin dysbiosis have been described … Address correspondence to Dr. X. Zhang, Department of Rheumatology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Clinical Immunology Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China. Email: zxpumch2003{at}sina.com.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".