Molecular mechanisms associated with ALA-PDT of brain tumor cells
Bibliographic record
Abstract
Previous studies have shown that low-dose PDT using 5-aminolevulinic acid (ALA)-induced photoporphyrin IX (PpIX) can induce apoptosis in tumor cells without causing necrosis. In this study we investigated the molecular mechanisms associated with apoptosis after ALA-PDT treatment in two brain glioma cell lines: human U87, and rat CNS-1cells. We used high energy light at a short time (acute PDT) and low energy light at a long time of exposure (metronomic PDT) to treat both cell lines. The cells were treated with 0.25 mM ALA at 5 joules for energy. We found that CNS-1 cells were more resistant to ALA-PDT than U87 cells when treated by both acute and metronomic PDT. To screen possible apoptosis mechanisms associated with acute and metronomic PDT, microarray analysis of gene expression was performed on RNA from glioblastoma cells treated with either acute or metronomic ALA-PDT. Within the set of genes that were negatively or positively regulated by both treatments are tumor necrosis factor receptors. The expression of TNF receptors was investigated further by RT-PCR and western blotting. The apoptosis mechanism of the cell death occurred through different pathways including BCL-2 and TNF receptors, and in part caused by cleaving caspase 3. Interestingly, metronomic ALA-PDT inhibited the expression of LTβR and the transcription factor NFκB. This inhibition was ALA concentration dependent at low concentrations.
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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) | 0.001 | 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".