Modulation of PPIX synthesis and accumulation in various normal and glioma cell lines by modification of the cellular signaling and temperature
Bibliographic record
Abstract
Effective therapies for malignant gliomas are still elusive and limited survival improvements are provided only by Temozolomide or fluorescence guided resection. The efficacy of photodynamic therapy (PDT) in this indication is limited by the higher sensitivity of normal brain structures compared to glioma necessitating a modulation of its sensitivity. We evaluate the influence of hypothermia and the tyrosine kinase inhibitor Erlotinib on cell's ability to synthesize PPIX following the administration of ALA which was not previously investigated. We demonstrate that both hypothermia and Erlotinib are favorable in PPIX selectivity as only glioma cell lines demonstrate an increased PPIX synthesis, whereas the neuronal and astrocytic synthesis is remaining unaffected. The results are encouraging to consider hypothermia and Erlotinib as adjuvant therapies to increase the PDT therapeutic index between GBM and normal intracranial tissues, as well as to improve contrast in fluorescence guided resection.
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) | 0.000 | 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".