Relationship between clinical features and intestinal microbiota in Chinese patients with ulcerative colitis
Bibliographic record
Abstract
BACKGROUND: Dysbacteriosis may be a crucial environmental factor for ulcerative colitis (UC). Further study is required on microbiota alterations in the gastrointestinal tract of patients with UC for better clinical management and treatment. AIM: To analyze the relationship between different clinical features and the intestinal microbiota, including bacteria and fungi, in Chinese patients with UC. METHODS: Eligible inpatients were enrolled from January 1, 2018 to June 30, 2019, and stool and mucosa samples were collected. UC was diagnosed by endoscopy, pathology, Mayo Score, and Montreal classification. Gene amplicon sequencing of 16S rRNA gene and fungal internal transcribed spacer gene was used to detect the intestinal microbiota composition. Alpha diversity, principal component analysis, similarity analysis, and Metastats analysis were employed to evaluate differences among groups. RESULTS: showed the opposite trend. More intermicrobial correlations in UC in remission than in active patients were observed. The bacteria-fungi correlations became single and uneven in patients with UC. CONCLUSION: The intestinal bacteria flora of patients with UC differs significantly in terms of various sample types and disease activities. The intermicrobial correlations change in patients with UC compared with non-IBD controls.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".