<i>In vivo</i> assessment of magnesium status in human body using accelerator-based neutron activation measurement of hands: A pilot study
Bibliographic record
Abstract
Magnesium (Mg) is an element essential for many enzymatic reactions in the human body. Various human and animal studies suggest that changes in Mg status are linked to diseases such as cardiac arrhythmia, coronary heart disease, hypertension, premenstrual syndrome, and diabetes mellitus. Thus, knowledge of Mg levels in the human body is needed. A direct measurement of human blood serum, which contains only 0.3% of the total body Mg, is generally used to infer information about the status of Mg in the body. However, in many clinical situations, Mg stored in large levels, for example in bones, muscles, and soft tissues, needs to be monitored either to evaluate the efficacy of a treatment or to study the progression of diseases associated with the deficiency of total body Mg. This work presents a feasibility study of a noninvasive, in vivo neutron activation analysis (IVNAA) technique using the 26Mg (n, gamma) 27Mg reaction to measure Mg levels in human hands. The technique employs the McMaster University high beam current Tandetron accelerator hand irradiation facility and an array of eight NaI (T1) detectors arranged in a 4 pi geometry for delayed counting of the 0.844 and 1.014 MeV gamma rays emitted when 27Mg decays in the irradiated hand. Mg determination in humans using IVNAA of hands has been demonstrated to be feasible, with effective doses as low as one-quarter of those delivered in chest x rays. The overall experimental uncertainty in the measurements is estimated to be approximately 5% (1 sigma). The results are found to be in the range of the in vitro measurements reported for other cortical bones collected from different sites of the human skeleton, which confirms that this technique mainly provides a measure of the amount of Mg in hand bones. The average concentration of Mg determined in human hands is 10.96 +/- 1.25 (+/- 1 SD) mg Mg/g Ca. The coefficient of variation (11%) observed in this study is comparable with or lower than several studies using in vitro measurements reported in the literature and therefore allows for a quantitative intersubject comparison, even if to a limited extent. The features of the developed technique such as its simplicity, rapidity, accuracy, robustness, noninvasive nature, and very effective use of radiation doses, present the technique as a viable diagnostic tool available for trial in a clinical environment.
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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 | 0.000 |
| 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 | 0.000 |
| 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".