Intracellular Acidification in a Rat C6 Glioma Model following Cariporide Injection Investigated by CEST-MRI
Bibliographic record
Abstract
Acidification of cancerous tissue induced pharmacologically may slow tumor growth and can be detected using magnetic resonance imaging. Numerous studies have shown that pharmacologically inhibiting specific transporters, such as the Na+/H+ exchanger 1 (NHE1), can alter glycolitic metabolism and affect tumor acidosis. The sodium proton exchanger inhibitor Cariporide can acidify U87MG gliomas in mice. This study aimed to determine whether Cariporide could acidify C6 glioma tumors in rats with an intact immune system. C6 glioma cells were implanted in the right brain hemisphere of ten rats. Chemical exchange saturation transfer (CEST) MRI (9.4T) was acquired on days 7–8 and 14–15 after implantation to measure in vivo tissue intracellular pH (pHi) within the tumors and on the contralateral side. pHi was basic relative to contralateral tissue at both time points assessed using the amine and amide concentration-independent detection (AACID) value. On day 14–15, measurements were made before and up to 160 min after Cariporide injection (N = 6). Twenty minutes after drug injection, the average AACID value in the tumor significantly increased by ∼6.4% compared to pre-injection, corresponding to 0.31 ± 0.20 lower pHi, while in contralateral tissue, AACID value increased significantly by ∼4.3% compared to pre-injection, corresponding to 0.22 ± 0.19 lower pHi. Control rats without tumors showed no changes following injection of Cariporide dissolved in 10% or 1% DMSO and diluted in PBS. This study demonstrates the sensitivity of CEST-based pH-weighted imaging for monitoring the response of tumors to pharmacologically induced acidification.
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 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 | 0.000 | 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 | 0.000 |
| 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".