A History of <i>In Vivo</i> Neutron Activation Analysis in Measurement of Aluminum in Human Subjects
Bibliographic record
Abstract
Aluminum, as an abundant metal, has gained widespread use in human life, entering the body predominantly as an additive to various foods and drinking water. Other major sources of exposure to aluminum include medical, cosmetic, and occupational routes. As a common environmental toxin, with well-known roles in several medical conditions such as dialysis encephalopathy, aluminum is considered a potential candidate in the causality of Alzheimer's disease. Aluminum mostly accumulates in the bone, which makes bone an indicator of the body burden of aluminum and an ideal organ as a proxy for the brain. Most of the techniques developed for measuring aluminum include bone biopsy, which requires invasive measures, causing inconvenience for the patients. There has been a considerable effort in developing non-invasive approaches, which allow for monitoring aluminum levels for medical and occupational purposes in larger populations. In vivo neutron activation analysis, a method based on nuclear activation of isotopes of elements in the body and their subsequent detection, has proven to be an invaluable tool for this purpose. There are definite challenges in developing in vivo non-invasive techniques capable of detecting low levels of aluminum in healthy individuals and aluminum-exposed populations. The following review examines the method of in vivo neutron activation analysis in the context of aluminum measurement in humans focusing on different neutron sources, interference from other activation products, and the improvements made in minimum detectable limits and patient dose over the past few decades.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".