An analysis of gut dysbiosis in obesity, diabetes, and chronic gut conditions
Bibliographic record
Abstract
Introduction: Gut dysbiosis is an imbalance in the microbial communities of the intestine and has been associated with numerous chronic diseases. Objectives: We aimed to compare gut dysbiosis within and across various disease states (Crohn's disease [CD], colorectal cancer [CRC], irritable bowel syndrome [IBS], and type 2 diabetes mellitus [T2DM], and obesity). Materials and Methods: Assessing comparative studies which examined levels of bacterial phyla in cases and controls. PubMed and Web of Science were searched to identify relevant studies, in which human fecal samples were used to analyze microbial flora. Results: Twenty-one studies were included, which met inclusion and exclusion criteria. Three studies were included assessing IBS, which found a decrease in Bacteroidetes in the IBS population, but inconsistent findings for other phyla. Six studies were included assessing obesity, and no consistent patterns emerged. Five studies were included examining T2DM, which found a consistent decrease in the Firmicutes/Bacteroidetes ratio in cases as compared to controls. No patterns were found for other phyla. Three studies were included examining CD, and five examining CRC. Conclusions: No consistent patterns were found for either of these diseases. While some patterns were found in bacterial phyla distribution, there were few commonalities, even in same-system disorders. However, uncovering underlying dysbiosis patterns shows great promise in furthering the understanding of disease pathogenesis and the potential for new therapeutic and diagnostic interventions. Further systematic reviews and well-controlled studies are warranted.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".