Modeling in vivo recovery of intracellular pH in muscle to provide a novel index of proton handling: Application to the diagnosis of mitochondrial myopathy
Bibliographic record
Abstract
Post-exercise recovery of intracellular pH (pH(i)) assessed using phosphorus magnetic resonance spectroscopy has not been previously evaluated in its entirety due to its complex time-course and missing data points resulting from a transient loss of inorganic phosphate signal. By considering the transition from exercise to recovery as a step function input, pH(i) recovery was modeled based on the creatine-kinase equilibrium, and the entire pH(i) recovery was characterized by calculating the time required for pH(i) recovery (t(pHrec)). Applying this methodology, normal subjects showed a strong linear correlation between phosphocreatine (PCr) half-time and t(pHrec) (r = 0.90, P < 0.001). In mitochondrial myopathy (MM) patients with weakness in the limb examined, 9/10 had faster pH(i) recovery relative to PCr recovery; wide normal ranges from a control group which included deconditioned subjects resulted in 7 of those 10 patients having otherwise normal recovery indices. Therefore, modeling pH(i) recovery allows characterization of the entire pH(i) recovery and detects altered proton handling in MM patients, including those with otherwise normal recovery indices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".