Characterization of tissue autofluorescence in Barrett's esophagus by confocal fluorescence microscopy
Bibliographic record
Abstract
High grade dysplasia and early cancer in Barrett's esophagus can be distinguished in vivo by endoscopic autofluorescence point spectroscopy and imaging from non-dysplastic Barrett's mucosa. We used confocal fluorescence microscopy for ex vivo comparison of autofluorescence in non-dysplastic and dysplastic Barrett's esophagus. Unstained frozen sections were obtained from snap-frozen Barrett's esophagus biopsy samples and scanned with confocal fluorescence microscopy (458 nm excitation; 505-550 nm [green] and > 560 nm [red] emission). Digital micrographs were taken from areas with homogenous and specific histopathology. Visual inspection and statistical analysis were used to evaluate the image datasets. Dysplastic and non-dysplastic Barrett's esophagus epithelia fluoresced mainly in the green spectrum and the main sources of autofluorescence were the cytoplasm and lamina propria. High-grade dysplasia was differentiated from non-dysplastic Barrett's esophagus by microstructural tissue changes. However, there were no specific changes in either the locations or average intensities of intrinsic green and red autofluorescence at the epithelial level that could differentiate between dysplastic and non-dysplastic Barrett's esophagus epithelia, ex vivo. Detectable differences in autofluorescence between BE and dysplasia/cancer in vivo are probably not caused by specific changes in epithelial fluorophores but are likely due to other inherent changes (e.g. mucosal thickening and increased microvascularity) attenuating autofluorescence from the collagen-rich submucosa. Furthermore, confocal fluorescence microscopy provides 'histology-like' imaging of Barrett's tissues and may offer a unique opportunity to exploit microstructural tissue changes occurring during neoplastic transformation for in vivo detection of high-grade dysplasia in Barrett's patients using newly developed confocal fluorescence microendoscopy devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".