Correlative Microscopy to Localize and Characterize Iron Deposition in Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: , may play a role in the development and progression of neurodegeneration in Alzheimer's disease (AD) through the production of oxidative stress. OBJECTIVE: To localize and characterize iron deposition and oxidation state in AD, we analyzed human hippocampal autopsy samples from four subjects with advanced AD that have been previously characterized with correlative MRI-histology. METHODS: We perform scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and electron energy loss spectroscopy (EELS) in the higher resolution transmission electron microscope on the surface and cross-sections of specific iron-rich regions of interest. RESULTS: Specific previously analyzed regions were visualized using SEM and confirmed to be iron-rich deposits using EDS. Subsequent analysis using focused ion beam cross-sectioning and SEM characterized the iron deposition throughout the 3-D volumes, confirming the presence of iron throughout the deposits, and in two out of four specimens demonstrating colocalization with zinc. Analysis of traditional histology slides showed the analyzed deposits overlapped both with amyloid and tau deposition. Following higher resolution analysis of a single iron deposit using scanning transmission electron microscope (STEM), we demonstrated the potential of monochromated STEM-EELS to discern the relative oxidation state of iron within a deposit. CONCLUSION: These findings suggest that iron is present in the AD hippocampus and can be visualized and characterized using combined MRI and EM techniques. An altered relative oxidation state may suggest a direct link between iron and oxidative stress in AD. These methods thus could potentially measure an altered relative oxidation state that could suggest a direct link between iron and oxidative stress in AD. Furthermore, we have demonstrated the ability to analyze metal deposition alongside commonly used histological markers of AD pathology, paving the way for future insights into the molecular interactions between Aβ, tau, iron, and other putative metals, such as zinc.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".