Detection of Species-Specific <i>Helicobacter</i> Ribosomal DNA in Intestinal Biopsy Samples from a Population-Based Cohort of Patients with Ulcerative Colitis
Bibliographic record
Abstract
The inflammatory bowel diseases are considered an abnormal host immune response to an environmental stimulus. Evidence suggests a role for intestinal bacteria in initiating and/or providing an ongoing stimulus for inflammation in inflammatory bowel disease. Helicobacter pylori is the major cause of active chronic gastritis and peptic ulcers in humans and has been linked to gastric carcinoma and lymphoma. Studies in various animal models, particularly mice, have identified enterohepatic Helicobacter species that are capable of causing hepatitis and enterocolitis. We hypothesize that Helicobacter species may have a role in maintaining inflammation in humans with inflammatory bowel disease. In order to investigate this, biopsy specimens were obtained from patients with and without inflammatory bowel disease. DNA was extracted from the tissues and subjected to PCR with primers designed to detect the ribosomal DNA of members of the Helicobacter species. DNA from six biopsy samples from 60 inflammatory bowel disease patients tested positive. This included 5 of 33 ulcerative colitis patients that were positive compared to 0 of 29 age-matched controls (P < 0.04). Sequencing of the bands produced by PCR amplification revealed >or=99% homology with H. pylori. These results indicate that a member of the Helicobacter species may be involved in some cases of ulcerative colitis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".