Lower Bifidobacteria counts in both duodenal mucosa-associated and fecal microbiota in irritable bowel syndrome patients
Bibliographic record
Abstract
AIM: To determine the composition of both fecal and duodenal mucosa-associated microbiota in irritable bowel syndrome (IBS) patients and healthy subjects using molecular-based techniques. METHODS: Fecal and duodenal mucosa brush samples were obtained from 41 IBS patients and 26 healthy subjects. Fecal samples were analyzed for the composition of the total microbiota using fluorescent in situ hybridization (FISH) and both fecal and duodenal brush samples were analyzed for the composition of bifidobacteria using real-time polymerase chain reaction. RESULTS: The FISH analysis of fecal samples revealed a 2-fold decrease in the level of bifidobacteria (4.2 +/- 1.3 vs 8.3 +/- 1.9, P < 0.01) in IBS patients compared to healthy subjects, whereas no major differences in other bacterial groups were observed. At the species level, Bifidobacterium catenulatum levels were significantly lower (6 +/- 0.6 vs 19 +/- 2.5, P < 0.001) in the IBS patients in both fecal and duodenal brush samples than in healthy subjects. CONCLUSION: Decreased bifidobacteria levels in both fecal and duodenal brush samples of IBS patients compared to healthy subjects indicate a role for microbiotic composition in IBS pathophysiology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".