Detection of inflammatory bowel disease by proton magnetic resonance spectroscopy (1H MRS) using an animal model
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to analyze the potential of proton magnetic resonance spectroscopy (1H MRS) in diagnosing early inflammatory bowel disease (IBD). METHODS: Thirty male Sprague Dawley rats were fed 2% carrageenan in their diet for either 1 or 2 weeks. 1H MRS was performed ex-vivo on colonic mucosal samples (n = 123) and the spectra were analyzed by a multivariate method of analysis. The results of the multivariate analysis were correlated with histological analysis performed using H & E stain for the presence of inflammation in the samples from each group. RESULTS: Multivariate analysis classified the samples in their respective groups with an accuracy of 82%. Our region selection algorithm identified four regions in the spectra as being discriminatory. The metabolites assigned to these regions include creatine, phosphatidylcholine, the -CH2HC= group in fatty acyl chain, and the glycerol backbone of lipids. The differences in concentration of these metabolites in each group offer insight into the biochemical changes occurring during IBD and confer diagnostic potential to 1H MRS as a tool to study colonic inflammation in conjunction with biopsy. CONCLUSION: 1H MRS is a sensitive tool to detect early colonic inflammation in an animal model of IBD.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".