Exploration of noninvasive mapping of brain pH with <sup>31</sup>P magnetic resonance spectroscopy
Bibliographic record
Abstract
Previous study of brain “potential of hydrogen” (pH) was conducted in single-voxel31P magnetic resonance (MR) spectroscopy. In this study, we explored the feasibility of mapping brain pH by using multivoxel31P MR spectroscopy. Firstly, phantom studies were carried out with a GE 3T MR system using 2D PRESSCSI sequence. TR was 1000 msec and TE 144 msec with 128 scan averages. Then two healthy volunteers were studied with same parameters. Data were processed offline using the SAGE/IDL program. Multivoxel spectra were analyzed and brain pH values were calculated with a standard equation. Peaks of metabolite were not homogeneous in phantom studies at this moment. There was noise for multivoxel31P spectra in volunteer studies. However, phosphomonoester peak, inorganic phosphate peak, phosphodiester peak, and phosphocreatine peak can be identified. Preliminary brain pH map was generated in the volunteers. In our opinion, it is feasible to map brain pH with improved multivoxel31P MR spectroscopy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".