Monitoring Photosensitizer Uptake Using Two Photon Fluorescence Lifetime Imaging Microscopy
Bibliographic record
Abstract
Photodynamic Therapy (PDT) provides an opportunity for treatment of various invasive tumors by the use of a cancer targeting photosensitizing agent and light of specific wavelengths. However, real-time monitoring of drug localization is desirable because the induction of the phototoxic effect relies on interplay between the dosage of localized drug and light. Fluorescence emission in PDT may be used to monitor the uptake process but fluorescence intensity is subject to variability due to scattering and absorption; the addition of fluorescence lifetime may be beneficial to probe site-specific drug-molecular interactions and cell damage. We investigated the fluorescence lifetime changes of Photofrin(®) at various intracellular components in the Mat-LyLu (MLL) cell line. The fluorescence decays were analyzed using a bi-exponential model, followed by segmentation analysis of lifetime parameters. When Photofrin(®) was localized at the cell membrane, the slow lifetime component was found to be significantly shorter (4.3 ± 0.5 ns) compared to those at other locations (cytoplasm: 7.3 ± 0.3 ns; mitochondria: 7.0 ± 0.2 ns, p < 0.05).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".