An accelerator based system for <i>in vivo</i> neutron activation analysis measurements of manganese in human hand bones
Bibliographic record
Abstract
Manganese (Mn) is an essential nutrient for growth and development. Unfortunately, overexposure can lead to neurological damage, which is manifested as a movement disorder marked by tremors. Preclinical symptoms have been found in populations occupationally exposed to the element, and it is suggested that in late stages of the disorder, removing the Mn exposure will not prevent symptoms from progressing. Hence, it is desirable to have a means of monitoring Mn body burden. In vivo neutron activation analysis (IVNAA) is a technique which allows the concentration of some elements to be determined within sites of the body without invasive procedures. Data in the literature suggests that the Mn concentration in bone is greater than other tissues, and that it may be a long term storage site following exposure. Therefore, using the McMaster KN-accelerator to produce neutrons through the 7Li(p,n)7Be reaction, the feasibility of IVNAA for measuring Mn levels in the human hand bone was investigated. Mn is activated through the 55Mn(n,gamma)56Mn reaction, and the 847 keV gamma rays emitted when 56Mn decays are measured outside the body using NaI(Tl) detectors. An optimal incident proton energy of 2.00 MeV was determined from indium foil and microdosimetry measurements. Hand phantom data suggest a minimum detectable limit of approximately 1.8 ppm could be achieved with a reasonably low dose of 50 mSv to the hand (normal manganese levels in the human hand are approximately 1 ppm). It is recommended the technique be developed further to make human in vivo measurements.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".