Regional brain iron correlates with transcriptional and cellular signatures in Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: The link between overload brain iron and transcriptional/cellular signatures in Alzheimer's disease (AD) remains inconclusive. METHODS: Iron deposition in 41 cortical and subcortical regions of 30 AD patients and 26 healthy controls (HCs) was measured using quantitative susceptibility mapping (QSM). The expression of 15,633 genes was estimated in the same regions using transcriptomic data from the Allen Human Brain Atlas (AHBA). Partial least square (PLS) regression was used to identify the association between the healthy brain gene transcription and aberrant regional QSM signal in AD. The biological processes and cell types associated with the linked genes were evaluated. RESULTS: Gene ontological analyses showed that the first PLS component (PLS1) genes were enriched for biological processes relating to the "protein phosphorylation" and "metal ion transport". Additionally, these genes were expressed in microglia (MG) and glutamatergic neurons (GLUs). DISCUSSION: Our findings provide mechanistic insights from transcriptional and cellular signatures into regional iron accumulation measured by QSM in AD. HIGHLIGHTS: Spatial patterns of iron deposition changes in AD correlate with cortical spatial expression genes in healthy subjects. The identified gene transcription profile underlies aberrant iron accumulation in AD was enriched for biological processes relating to "protein phosphorylation" and "metal ion transport". The related genes were predominantly expressed in MG and GLUs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".