Intestinal Dysbiosis and Potential Consequences of Microbiome-altering Antibiotic Use in the Pathogenesis of Human Rheumatic Disease
Bibliographic record
Abstract
The etiopathogenesis of chronic, rheumatic autoimmune disorders has been the focus of intense research since their initial literary description.Despite much scientific iteration, the current hypothesis postulates that the natural history of most such diseases is complex and multifactorial.Ultimately, it is thought that both genetic factors (including epigenetic) and environmental influences determine the fate of predisposed subjects, who then develop clinically evident disease.The modern notion that autoimmune arthritides are the result of polygenic interactions dates back to the mid-1970s when it was shown that HLA-B27 conferred a high risk for classic ankylosing spondylitis (AS) 1 , followed by the description of the shared epitope hypothesis in 1987, which posited that HLA class II alleles, particularly DR-β1, conferred higher risk for the development of rheumatoid arthritis (RA) 2 .This, along with the advent of the Human Genome Project and the upsurge of genome-wide association studies, had led to an overly optimistic and, arguably, reductionist understanding of the etiology of inflammatory arthropathies.The last 3 decades have seen an ever-expanding use of high-throughput DNA sequencing by multiple groups in a quest to ascribe risk-alleles to the various autoimmune and rheumatic syndromes.This effort has resulted in, among other advances, the identification of many HLA and non-HLA risk alleles associated with disease and the emergence of a functional genomics approach to discovery and validation of immune pathways and molecular mechanisms implicated in the pathogenesis of these disorders 3,4 .Although the influence of heritability for many of these arthritides is considerable, current genetic discoveries can only explain up to 20% of the variance in RA and in juvenile idiopathic arthritis (JIA) 3,4 .These include many HLA alleles (particularly HLA-A2, HLA-DR5, and HLA-DR8), and a handful of non-HLA loci (i.e., PTPN22, MI, SLC11A6, and WISP3).Importantly, mendelian patterns of inheritance
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".