Table_1_Exploring the role of gut microbiota dysbiosis in gout pathogenesis: a systematic review.DOCX
Bibliographic record
Abstract
Objectives<p>Gut dysbiosis is believed to be one of the several mechanisms that are involved in the pathogenesis of gout. This systematic review aimed to summarize the role of gut dysbiosis in gout disease and uncover the underlying mechanisms.</p>Methods<p>A comprehensive search was conducted on PubMed, Web of Science, and Scopus databases up to October 2021. Animal studies and human observational studies, including case-control, cross-sectional, and cohort studies assessing the association between gut microbiota composition and gout were included. The quality of included studies has been evaluated using the Newcastle–Ottawa Quality Assessment scale (NOS) and the SYRCLE's risk of bias tool.</p>Results<p>Initially, we found 274 studies among which 15 studies were included in this systematic review. Of them, 10 studies were conducted on humans and 5 studies were conducted on animals. Increased abundance of Alistipes and decreased abundance of Enterobacteriaceae alters purine metabolism, thereby aggravating gout condition. Moreover, a higher abundance of Phascolarctobacterium and Bacteroides in gout modulates enzymatic activity in purine metabolism. Butyrate-producing bacteria such as Faecalibacterium, prausnitzii, Oscillibacter, Butyricicoccus, and Bifidobacterium have higher abundance in healthy controls compared to gout patients, suggesting the anti-inflammatory and anti-microbial role of short-chain fatty acids (SCFAs). Lipopolysaccharides (LPS)-releasing bacteria, such as Enterobacteriaceae, Prevotella, and Bacteroides, are also involved in the pathogenesis of gout disease by stimulating the innate immune system.</p>Conclusion<p>Exploring the role of gut dysbiosis in gout and the underlying mechanisms can help develop microbiota-modulating therapies for gout.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.038 |
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; both teacher heads agree on what is shown here.
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".