IR spectral imaging of secreted mucus: a promising new tool for the histopathological recognition of human colonic adenocarcinomas
Bibliographic record
Abstract
AIMS: During colonic carcinogenesis, mucin-type glycoproteins are known to undergo quantitative and qualitative alterations. The aim of this study was to determine the value of infrared (IR) spectral histology for the histopathological recognition of colonic adenocarcinomas based on mucin-associated IR spectral markers. METHODS AND RESULTS: Paraffin-embedded tissue sections of normal human colon and adenocarcinomas were analysed directly by IR-microspectroscopy (IR-MSP), without prior chemical dewaxing. IR-MSP imaging combined with multivariate analysis permitted the construction of IR colour-coded images of the tissue sections providing spatially resolved biochemical information. This allowed localization of mucin-rich areas and provided label-free spectral-based staining of secreted mucus related to the biochemical heterogeneity of its mucin content. IR images of secreted mucus display the same spectral clusters in both normal and adenocarcinomatous colonic tissues, but with significant differences in surface percentages. Such differences allow a distinction between these two tissue types. Spectral variations associated with changes of mucin secondary structure were the most accurate mucus spectral marker for discriminating between normal colon and adenocarcinomas in the sample set. CONCLUSIONS: IR-MSP imaging provides a new type of histology, independent of visual morphology, presenting tremendous possibilities for discovery and clinical monitoring of cancer markers.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".