Intracellular Acidification in Brain Tumors Induced by Topiramate : In-Vivo Detection Using Chemical Exchange Saturation Transfer Magnetic Resonance Imaging
Bibliographic record
Abstract
Glioblastoma Multiforme (GBM) is the most aggressive and malignant form of primary brain tumor. In many tumors, increased intracellular pH (pHi) is a hallmark of aggressiveness. This increased pHi has been shown to be related to cell proliferation and evasion of apoptosis as well as resistance to chemotherapy. As such, monitoring pHi and the tumor pHi response to pharmacologic challenge, may aid in treatment planning and patient management for this deadly cancer. A magnetic resonance imaging (MRI) method called Chemical Exchange Saturation Transfer (CEST) has been used to detect changes in pHi. Our group has recently developed a CEST technique called amine and amide concentration independent detection (AACID), which was shown to be sensitive to pHi changes induced by the anticancer drug, lonidamine (LND). However, LND is not currently approved for use in humans. Our objective was to demonstrate that topiramate (TPM), an antiepileptic drug that is well tolerated in humans, could also induce tumor acidification. The goal this thesis was to quantify the changes in pHi induced by a single dose of TPM in a mouse model of brain tumor. CEST spectra were acquired using a 9.4T MRI scanner, before and 75 minutes after administration of TPM (dose: 120 mg/kg). A significant increase in the AACID CEST effect was observed within brain tumors with no change observed in contralateral tissue. The increase in AACID CEST corresponds to tumor acidification as expected. Therefore TPM induced a rapid measurable metabolic change in tumors that could provide valuable insight into cancer aggressiveness and aid in tumor detection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".