5-aminolevulinic acid for quantitative seek-and-treat of high-grade dysplasia in Barrett’s esophagus cellular models
Bibliographic record
Abstract
High-grade dysplasia (HGD) in Barrett’s esophagus (BE) poses increased risk for developing esophageal adenocarcinoma. To date, early detection and treatment of HGD regions are still challenging due to the sampling error from tissue biopsy and relocation error during the treatment after histopathological analysis. In this study, CP-A (metaplasia) and CP-B (HGD) cell lines were used to investigate the “seek-and-treat” potential using 5-aminolevulinic acid-induced protoporphyrin IX (PpIX). The photodynamic therapy photosensitizer then provides both a phototoxic effect and additional image contrast for automatic detection and real-time laser treatment. Complementary to our studies on automatic classification, this work focused on characterizing subcellular irradiation and the potential phototoxicity on both metaplasia and HGD. The treatment results showed that the HGD cells are less viable than metaplastic cells due to more PpIX production at earlier times. Also, due to mitochondrial localization of PpIX, a better killing effect was achieved by involving mitochondria or whole cells compared with just nucleus irradiation in the detected region. With the additional toxicity given by PpIX and potential morphological/textural differences for pattern recognition, this cellular platform serves as a platform to further investigate real-time “seek-and-treat” strategies in three-dimensional models for improving early detection and treatment of BE.
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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 | 0.000 | 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 | 0.001 |
| 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".