Changes in tissue water content measured with multiple-frequency bioimpedance and metabolism measured with <sup>31</sup>P-MRS during progressive forearm exercise
Bibliographic record
Abstract
Multiple-frequency bioimpedance analysis (MFBIA) has been used to determine the cellular water composition in the human body. It is noninvasive and has demonstrated good correlations with other invasive measures of tissue water. However, the ability of this method to study transient changes in tissue water in specific muscle groups has not been explored. In this study, MFBIA was used to assess changes in forearm intracellular water (ICW), extracellular water (ECW), and total water (TW) in seven healthy volunteers during and after a progressive wrist flexion exercise protocol. In an identical trial, (31)P magnetic resonance spectroscopy ((31)P-MRS) was used to assess changes in intracellular pH and phosphocreatine (PCr). At the completion of exercise, forearm ICW increased 12.6% (SD 0.07, P = 0.003), TW increased 10.1% (SD 0.06, P = 0.005), and no significant changes were recorded for ECW. A significant correlation was found between the changes in intracellular pH and changes in ICW during exercise (r = -0.84, P = 0.018). With the use of regression analysis, average changes in P(i), PCr, and pH were found to predict changes in ICW (R(2) = 0.98, P = 0.005). In conclusion, MFBIA was sensitive enough to measure transient changes in the exercising forearm muscle. The changes seen were consistent with the hypothesis that intracellular acidification and PCr hydrolysis are important mediators of cellular osmolality and therefore may be responsible for the increased volume of water in the intracellular space that is often recorded after short-term high-intensity exercise.
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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 | 0.001 | 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".