Enhancing intensity of tumor-specific fluorescence by MEK inhibition in brain tumor models
Bibliographic record
Abstract
Fluorescence guided surgery using 5-aminolevulinic acid (5-ALA FGS), a technique widely used to resect brain tumors, relies on fluorescence signals from Protoporphyrin IX (PpIX) accumulated in cancer cells to identify and resect tumor tissue. Despite its clinical success, a major issue of 5-ALA FGS is the insufficient accumulation of PpIX in tumors, posing challenges to the surgeons to delineate tumor ends. Our previous research showed that MEK inhibition increases PpIX accumulation in cancer cells but not in normal cells. In this study, we aimed to screen different MEK inhibitors for their efficacy in promoting 5-ALA mediated PpIX accumulation in human brain cancer cell lines in vitro. Furthermore, we sought to develop an animal model of brain cancer to evaluate the efficacy of screened MEK inhibitors in enhancing tumor visualization in vivo. By conducting in vitro fluorescence measurements, we found that MEK inhibitors Trametinib and Selumetinib were most effective in promoting PpIX accumulation in cancer cell lines. For its consistent robust effect in vitro, Selumetinib was selected for evaluation in in vivo experiments. We successfully developed a mice brain tumor model in Balb/c mice by allografting mammary cancer 4T1 cells into the brain's right hemisphere. Preliminary 2-Photon imaging results show a substantial increase in PpIX fluorescence in mice treated with Selumetinib and 5-ALA compared to mice with vehicle (DMSO/saline) and 5-ALA treatment. These results indicate the potential use of MEK inhibitor treatment to promote PpIX fluorescence in brain tumors for improved 5-ALA FGS in clinical settings.
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) | 0.000 | 0.000 |
| 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".