Increased expression of mitochondrial benzodiazepine receptors following low-level light treatment facilitates enhanced protoporphyrin IX production in glioma-derived cells in vitro
Bibliographic record
Abstract
This study investigates whether low level light treatment (LLLT) can enhance the expression of Peripheral-type mitochondrial benzodiazepine receptors (PBRs) on the glioma-derived tumour cell line, CNS-1, and by doing so promote the synthesis of protoporphyrin IX (PpIX) and increase the photodynamic therapy (PDT)-induced cell kill using 5-aminolevulinic acid (ALA). The endogenous photosensitizer, (PpIX) and related metabolites including coproporphyrin III are known to traffic via the PBRs on the outer mitochondrial membrane on their passage into or out of the mitochondria. Astrocyte-derived cells within the brain express PBRs, while neurons express the central-type of benzodiazepine receptor. CNS-1 cells were exposed to a range of differing low-level light protocols immediately prior to PDT. LLLT involved using broad-spectrum light or monochromatic laser light specific to 635 or 905 nm wavelength. Cells (5&mgr;105) were exposed to a range of LLLT doses (0, 1 or 5 J/cm2) using a fixed intensity of 10 mW/cm2 and subsequently harvested for cell viability, immunofluorescence or western blot analysis of PBR expression. The amount of PpIX within the cells was determined using chemical extraction techniques. Results confirm the induction of PBR following LLLT is dependent on the dose and wavelength of light used. Broadspectrum light provided the greatest cell kill following PDT, although LLLT with 635 nm or 905 nm also increased cell kill as compared to PDT alone. All LLLT regimens increased PBR expression compared to controls with corresponding increases in PpIX production. These data suggest that by selectively increasing PBR expression in tumour cells, LLLT may facilitate enhanced cell kill using ALA-PDT without damaging surrounding normal brain.
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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".