Bibliographic record
Abstract
Abstract Huntington's disease ( HD ) is one of many neurodegenerative diseases with reported alterations in brain iron homeostasis that may contribute to neuropathogenesis. Iron accumulation in the specific brain areas of neurodegeneration in HD has been proposed based on observations in post‐mortem tissue and magnetic resonance imaging studies. Altered magnetic resonance imaging signal within specific brain regions undergoing neurodegeneration has been consistently reported and interpreted as altered levels of brain iron. Biochemical studies using various techniques to measure iron species in human samples, mouse tissue, or in vitro has generated equivocal data to support such an association. Whether elevated brain iron occurs in HD , plays a significant contributing role in HD pathogenesis, or is a secondary effect remains currently unclear. image Huntington's disease (HD) is one of many neurodegenerative diseases reported to have alterations in brain iron homeostasis. Whether elevated brain iron occurs in HD, and whether elevated iron levels play a significant contributory role in HD pathogenesis remains currently unclear. In this review, we discuss potential mechanisms by which mutant huntingtin‐mediated increases in intracellular calcium and elevated intracellular iron may interact to result in elevated levels of reactive oxygen species leading to lipid peroxidation, DNA damage, and neuronal cell death.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".