Combined Effects of Focused Ultrasound and Photodynamic Treatment for Malignant Brain Tumors Using C6 Glioma Rat Model
Bibliographic record
Abstract
PURPOSE: Glioblastoma (GBM) is an intractable disease for which various treatments have been attempted, but with little effect. This study aimed to measure the effect of photodynamic therapy (PDT) and sonodynamic therapy (SDT), which are currently being used to treat brain tumors, as well as sono-photodynamic therapy (SPDT), which is the combination of these two. MATERIALS AND METHODS: Four groups of Sprague-Dawley rats were injected with C6 glioma cells in a cortical region and treated with PDT, SDT, and SPDT. Gd-MRI was monitored weekly and 18F-FDG-PET the day before and 1 week after the treatment. The acoustic power used during sonication was 5.5 W/cm² using a 0.5-MHz single-element transducer. The 633-nm laser was illuminated at 100 J/cm². Oxidative stress and apoptosis markers were evaluated 3 days after treatment using immunohistochemistry (IHC): 4-HNE, 8-OhdG, and Caspase-3. RESULTS: <0.001), were observed in the SPDT group compared to other groups in IHC. CONCLUSION: Our findings show that light with sensitizers can inhibit GBM growth, but not ultrasound. Although SPDT did not show the combined effect in MRI, high oxidative stress was observed in IHC. Further studies are needed to investigate the safety parameters to apply ultrasound in GBM.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".