Combination of Near Infrared Light-Activated Photodynamic Therapy Mediated by Indocyanine Green with Etoposide to Treat Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Indocyanine green (ICG) has been reported as a potential near-infrared (NIR) photosensitizer for photodynamic therapy (PDT) of cancer. However the application of ICG-mediated PDT is both intrinsically and physiologically limited. Here we report a combination of ICG-PDT with a chemotherapy drug etoposide (VP-16), aiming to enhance the anticancer efficacy, to circumvent limitations of PDT using ICG, and to reduce side effects of VP-16. We found in controlled in vitro cell-based assays that this combination is effective in killing non-small-cell lung cancer cells (NSCLC, A549 cell line). We also found that the combination of ICG-PDT and VP-16 exhibits strong synergy in killing non-small-cell lung cancer cells partially through inducing more DNA double-strand breaks (DSBs), while it has a much weaker synergy in killing human normal cells (GM05757). Furthermore, by studying the treatment sequence dependence and the cytotoxicity of laser-irradiated mixtures of ICG and VP-16, we found that the observed synergy involves direct/indirect reactions between ICG and VP-16. We further propose that there exists an electron transfer reaction between ICG and VP-16 under irradiation. This study therefore shows the anticancer efficacy of ICG-PDT combined with VP-16. These findings suggest that ICG-mediated PDT may be applied in combination with the chemotherapy drug VP-16 to treat some cancers, especially the non-small-cell lung cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".