Dual X-ray absorptiometry model for characterizing water in the human forearm using multiple frequency bioimpedance analysis
Bibliographic record
Abstract
The purpose of this study was to develop a method for measuring intracellular (ICW) and extracellular water (ECW) in the human forearm using multiple frequency bioimpedance analysis (MFBIA). The approach was (i) to measure whole-body and forearm fat-free mass using dual X-ray absorptiometry (DXA); (ii) to use these measurements to estimate the fat-free mass (FFM) resistivity in both the forearm and in the whole body; and (iii) to use the ratio of these FFM resistivities to estimate the resistivity in the ICW and ECW compartments of the forearm. To first demonstrate the accuracy of the DXA software in differentiating lean body mass from fat and bone within a volume of tissue, ex-vivo bovine muscle tissue samples (n = 3) were used to approximate the physical properties of the human forearm. It was found that although the human whole-body software overestimates FFM, it was slightly underestimated by the small animal software. Using this technique, DXA measures of FFM were obtained from human volunteers (n = 11; age = 20 +/- 5 years; height = 170 +/- 12 cm; mass = 64 +/- 16 kg). These measures were used in conjunction with MFBIA measures of impedance of the whole body and of the forearm to determine the resistivities of the ICW and ECW compartments of the forearm, namely 375.8 +/- 25.2 ohms cm and 55.6 +/- 3.7 ohms cm, respectively. These were used in MFBIA equations to calculate the ICW, ECW, and total arm water (TAW) volumes of the human forearm. The calculated TAW and the ECW (+/- SD) volume fraction (667.29 +/- 200.15 mL and 0.169 +/- 0.039 mL, respectively) were in agreement with literature values. MFBIA results were compared with those obtained using nuclear magnetic resonance relaxometry (NMRR). MFBIA was performed on 15 subjects before and after an intense maximal handgrip exercise to estimate changes in water volume in muscle. Following exercise, the total and intracellular water of the forearm increased on average by 8% +/- 3% and 10% +/- 4% (mean +/- SD), respectively. In 5 healthy volunteers, MFBIA and NMRR were performed before and after a similar exercise of the forearm muscle. The changes with exercise of intracellular and total arm water volumes as measured by MFBIA were estimated. The percent increases in total water were found to be 9.4% +/- 4.2% and 9.4% +/- 2.6% and in intracellular water were found to be 10.6% +/- 4.6% and 12.0% +/- 2.8% (mean +/- SD) for NMRR and MFBIA, respectively. The results show that the exercise-induced changes in ICW and TAW determined with the MFBIA model are consistent with those observed with NMRR and radiotracer literature.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".