Autofluorescence spectroscopy for multimodal tissues characterization in colitis-associated cancer murine model
Bibliographic record
Abstract
The purpose of this research project is to assess mice colon wall, using three optical modalities (conventional endoscopy, confocal endomicroscopy and optical spectroscopy) and endoluminal MRI. The study is done in the context of inflammatory bowel disease and colorectal cancer that represent 13% of new cases of cancer, every year in western countries.An optical spectroscopic bench (autofluorescence and reflectance) was developed with a flexible fiber probe. This latter has been combined with a mini multi-purpose rigid endoscope and a confocal endomicroscope. The optical modalities were first used in vivo on SWISS mice. Then, a specific optical a phantom (containing two layers of distinct fluorophores) was developed in order to evaluate our two-channel spectroscopic probe as a basic depth-sensitive measurement tool.The preliminary results show the feasibility to combine such modalities in the same in vivo protocol. Conventional endoscopy is useful to depict inflammation along colon wall. Confocal endomicroscopy provides high-contrasted images of microvascularization. Measured optical spectra both depend on biochemical tissue content and layered structure of the medium. The light collected from one channel is not similar to the other, in terms of intensity and spectroscopic profile as the interaction with the medium observed volume is different. A comparative analysis of the spectra based on our in vitro model exhibits a strong correlation between simple index extracted from spectral data and two main phantom characteristics (fluorophore concentrations and superficial layer thickness). This work suggests that this technique could contribute to assess tissues alterations through autofluorescence spectroscopic measurement under endoscopy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".