Epithelial Gaps in a Rodent Model of Inflammatory Bowel Disease: A Quantitative Validation Study
Bibliographic record
Abstract
OBJECTIVES: Confocal laser endomicroscopy (CLE) is a non-invasive imaging modality of the gastrointestinal tract. Epithelial gaps in the small intestine of patients and rodents have been demonstrated using CLE. The goal of this study was to quantitatively validate the findings of epithelial gap density observed with CLE against confocal microscopy (CM) and light microscopy. METHODS: Two strains of mice (control 129 Sv/Ev and interleukin 10 knockout (IL-10(-/-))) underwent CLE of the terminal ileum. Adjacent ileal tissues were examined using CM and light microscopy. The total number of gaps and cells in the villi were manually counted from the three-dimensional reconstruction of cross-sectional CLE and CM images. The histology specimens were reviewed for epithelial gap and cell counts by a pathologist blinded to the study groups. The inter- and intra-observer variability for cell and gap counts were determined. RESULTS: For CLE, the gap densities (mean±s.d.) in the ileum for control and IL-10(-/-) mice were: 9.5±1.3 gaps per 1,000 cells and 20.6±2.1 gaps per 1,000 cells counted (P<0.001), respectively. For CM, the ileal gap densities were 7.3±1.3 gaps per 1,000 cells and 22.8±6.2 gaps per 1,000 cells (P=0.03), respectively. For light microscopy, the ileal gap densities were 29.2±5.9 gaps per 1,000 cells and 51.5±6.4 gaps per 1,000 cells for the two strains. CONCLUSION: CLE can be used to quantitatively assess epithelial cells and gaps with accuracy comparable to CM and light microscopy. In a mouse model of inflammatory bowel disease, the epithelial gap density in the terminal ileum is significantly increased when examined using all three modalities.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".