Multimodal imaging to study the diffusion of a photosensitizer into tumoral tissue
Bibliographic record
Abstract
Photodynamic therapy (PDT) used for photodiagnostic and cancer treatment is based on interaction between a photosensitizer and light leading to tumoral tissue destruction. To perform the therapy, specific localisation and dose-ranging of photosensitive molecules should be optimised. The aim of this work was to follow the diffusion of a new photosensitizer according to time (30 min to 24 hours) into a tumor after an intratumoral injection. Multiphoton technology (infrared excitation) enables to image specimens in a non invasive mode with high resolution at deep penetration. The spectral imaging mode gives information about localization of fluorescent molecules and strongly depends on their concentration. Lifetime imaging mode gives access to their fluorescence decay which is independent on their concentration but sensible to their physicochemical environment. Second Harmonic Generation localizes the collagen content of specimens. Macrofluorescence imaging (visible excitation) enabled us to visualize the whole tumor and showed a progressive concentration of the photosensitizer in the tumoral tissue. Fluorescence detection methods (Spectral, FLIM, SHG) combined to a macroscopic observation represent major advanced in optical photodiagnostic. Based on these techniques, this work enabled us to determine the delay following injection for which the photosensitizer's concentration in tumoral tissue was optimised.
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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".