Is Gut Microbial LPS a Potential Trigger of Juvenile Idiopathic Arthritis?
Bibliographic record
Abstract
Juvenile idiopathic arthritis (JIA) is one of the most common chronic inflammatory disorders in children, with hallmarks of joint inflammation, synovial hyperplasia, and leukocyte infiltration1. As in other autoimmune diseases, the exact etiopathogenesis, and pathogenic drivers of JIA, remain incompletely understood. In rheumatoid arthritis (RA) and ankylosing spondylitis (AS), several studies have shown that disease susceptibility is strongly associated with sets of immune-response genes, which include specific MHC alleles. In contrast, JIA does not seem to have such a strong association, and there is less occurrence of familial aggregations, and only limited disease concordance in homozygotic twins2,3,4,5. All of these observations suggest a key role in JIA pathogenesis for noninherited factors. Indeed, a number of reports have implicated maternal factors, such as breast-feeding, and whether the mother is a smoker6. Other environmental factors have been cited, such as bacterial or viral infection, antibiotic usage7, and most recently the influence of shifts in the gut microbiome8. The development of state-of-the-art 16S rRNA high-throughput sequencing technology for microbial profiling has opened the door for the characterization of the phylogenetic distribution of taxa in the gut bacterial communities in an individual. With this culture-independent approach, an increasing number of reports have documented decreases in gut microbiome diversity and other forms of dysbiosis in inflammatory and autoimmune diseases that include … Address correspondence to Dr. G.J. Silverman, Laboratory of B cell Immunobiology, Department of Medicine, New York University School of Medicine, Room 804, 450 E. 29th St., New York, New York 10016, USA. E-mail: Gregg.silverman{at}nyumc.org
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".