Mapping Brain Metals to Evaluate Therapies for Neurodegenerative Disease
Bibliographic record
Abstract
The brain is rich in metals and has a high metabolic rate, making it acutely vulnerable to the toxic effects of endogenously produced free radicals. The abundant metals, iron and copper, transfer single electrons as they cycle between their reduced (Fe(2+) , Cu(1+) ) and oxidized (Fe(3+) , Cu(2+) ) states making them powerful catalysts of reactive oxygen species (ROS) production. Even redox inert zinc, if present in excess, can trigger ROS production indirectly by altering mitochondrial function. While metal chelators seem to improve the clinical outcome of several neurodegenerative diseases, their mechanisms of action remain obscure and the effects of long-term use are largely unknown. Most chelators are not specific to a single metal and could alter the distribution of multiple metals in the brain, leading to unexpected consequences over the long-term. We show here how X-ray fluorescence will be a valuable tool to examine the effect of chelators on the distribution and amount of metals in the brain.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".