Effect of Rapamycin on the Radio-Sensitivity of Cultured Tumor Cells Following Boron Neutron Capture Reaction
Bibliographic record
Abstract
Background: Mammalian target of rapamycin (mTOR) signaling pathway has been implicated in multiple mechanisms of resistance to anticancer drugs and poor treatment outcomes in various human cancers. Meanwhile, clinical boron neutron capture therapy (BNCT) has been carried out for patients with malignant gliomas, melanomas, inoperable head and neck tumors and oral cancers. This study aimed to evaluate the effect of mTOR inhibition on radio-sensitivity of cultured tumor cells in BNCT, employing p-boronophenylalanine- 10 B (BPA) as a 10 B-carrier. Methods: Cultured SAS cells had been incubated for 48 h at RPMI medium with mTOR inhibitor, rapamycin at the dose of 1 µM, and then continuously incubated for 2 more hours at RPMI medium containing both BPA at the 10 B concentration of 10 ppm and rapamycin (1 µM). Subsequently, the SAS cells received reactor neutron beams, and then surviving fraction and micronucleus frequency were determined. Results: SAS cells incubated with rapamycin showed resistance to γ-rays compared with no treatment with rapamycin. The efficiency of delivery of 10 B from BPA into cultured SAS cells was reduced through combining with rapamycin, leading to reduced sensitivity following boron neutron capture reaction. Conclusions: Since many tumors are characterized by deregulated PI3K/AKT/mTOR pathway, rapamycin is thought to inhibit the pathway and tumor growth. However, it was revealed that rapamycin can also inhibit the transport of 10 B for BNCT into tumor cells. When BNCT is combined with mTOR inhibitor, the efficiency as cancer treatment can be reduced by repression of distributing 10 B in tumor cells, warranting precaution when the two strategies are combined. World J Oncol. 2020;11(4):158-164 doi: https://doi.org/10.14740/wjon1296
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".