Longitudinal Measurements of Intra- and Extracellular pH Gradient in a Rat Model of Glioma
Bibliographic record
Abstract
This study presents the first longitudinal measurement of the intracellular/extracellular pH gradient in a rat glioma model using noninvasive magnetic resonance imaging. The acid–base balance in the brain is tightly controlled by endogenous buffers. Tumors often express a positive pH gradient (pHi – pHe) compared with normal tissue that expresses a negative gradient. Alkaline pHi in tumor cells increases activity of several enzymes that drive cellular proliferation. In contrast, acidic pHe is established because of increased lactic acid production and subsequent active transport of protons out of the cell. pHi was mapped using chemical exchange saturation transfer, whereas regional pHe was determined using hyperpolarized 13C bicarbonate magnetic resonance spectroscopic imaging. pHi and pHe were measured at days 8, 12, and 15 postimplantation of C6 glioma cells into rat brains. Measurements were made in tumors and compared to brain tissue without tumor. Overall, average pH gradient in the tumor changed from −0.02 ± 0.12 to 0.10 ± 0.21 and then 0.19 ± 0.16. Conversely, the pH gradient of contralateral brain tissue changed from −0.45 ± 0.16 to −0.25 ± 0.21 and then −0.34 ± 0.25 (average pH ± 1 SD) Spatial heterogeneity of tumor pH gradient was apparent at later time points and may be useful to predict local areas of treatment resistance. Overall, the intracellular/extracellular pH gradients in this rat glioma model were noninvasively measured to a precision of ∼0.1 pH units at 3 time points. Because most therapeutic agents are weak acids or bases, a priori knowledge of the pH gradient may help guide choice of therapeutic agent for precision medicine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".