ALA-PpIX mediated photodynamic therapy of malignant gliomas augmented by hypothermia
Bibliographic record
Abstract
BACKGROUND: Malignant gliomas are highly invasive, difficult to treat, and account for 2% of cancer deaths worldwide. Glioblastoma Multiforme (GBM) comprises the most common and aggressive intracranial tumor. The study hypothesis is to investigate the modification of Photodynamic Therapy (PDT) efficacy by mild hypothermia leads to increased glioma cell kill while protecting normal neuronal structures. METHODS: Photosensitizer accumulation and PDT efficacy in vitro were quantified in various glioma cell lines, primary rat neurons, and astrocytes. In vivo studies were carried out in healthy brain and RG2 glioma of naïve Fischer rats. Hypothermia was induced at 1 hour pre- to 2 hours post-PDT, with ALA-PpIX accumulation and PDT treatments effects on tumor and normal brain PDT quantified using optical spectroscopy, histology, immunohistochemistry, MRI, and survival studies, respectively. FINDINGS: In vitro studies demonstrated significantly improved post-PDT survival in primary rat neuronal cells. Rat in vivo studies confirmed a neuroprotective effect to hypothermia following PpIX mediated PDT by T2 mapping at day 10, reflecting edema/inflammation volume reduction. Mild hypothermia increased PpIX fluorescence in tumors five-fold, and the median post-PDT rat survival time (8.5 days normothermia; 14 days hypothermia). Histology and immunohistochemistry show close to complete cellular protection in normal brain structures under hypothermia. CONCLUSIONS: The benefits of hypothermia on both normal neuronal tissue as well as increased PpIX fluorescence and RG2 induced rat survival strongly suggest a role for hypothermia in photonics-based surgical techniques, and that a hypothermic intervention could lead to considerable patient outcome improvements.
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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".