Influence of Rose Bengal Dimerization on Photosensitization
Bibliographic record
Abstract
Abstract Protein crosslinking photosensitized by rose Bengal (RB2−) has multiple medical applications and understanding the photosensitization mechanism can improve treatment effectiveness. To this end, we investigated the photochemical efficiencies of monomeric RB2− (RBM2−) and dimeric RB2− (RBD2−) and the optimal pH for anaerobic RB2− photosensitization in cornea. Absorption spectra and dynamic light scattering (DLS) measurements were used to estimate the fractions of RBM2− and RBD2−. RB2− self‐photosensitized bleaching was used to evaluate the photoactivity of RBM2− and RBD2−. The pH dependence of anaerobic RB2− photosensitization was evaluated in ex vivo rabbit corneas. The 549 nm/515 nm absorption ratio indicated that concentrations > 0.10 mm RB contained RBD2−. Results from DLS gave estimated mean diameters for RBM2− and RBD2− of 0.70 ± 0.02 nm and 1.75 ± 0.13 nm, respectively, and indicated that 1 mm RB2− contained equal fractions of RBM2− and RBD2−. Quantum yields for RB2− bleaching were not influenced by RBD2− in RB2− solutions although accounting for RB2− concentration effects on the reaction kinetics demonstrated that RBD2− is not a photosensitizer. Optimal anaerobic photosensitization occurred at pH 8.5 for solutions containing 200 mm Arg. These results suggest potential approaches to optimizing RBM2−‐photosensitized protein crosslinking in tissues.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".