[P3–424]: CORRELATIVE MRI/OPTICAL/ELECTRON MICROSCOPY EVALUATION OF METAL DISTRIBUTION AND OXIDATIVE STATE IN THE ALZHEIMER's HIPPOCAMPUS
Bibliographic record
Abstract
Metal ions, in particular Fe2+, are a possible source of oxidative stress in neurodegenerative disorders. Iron may be a key part of the microglial-driven inflammatory component of AD. In this study, we demonstrate a technique correlating specimen MRI and traditional optical histology with scanning electron microscopy (SEM) to locate, extract, and characterize the distribution and oxidation state of iron deposits in human AD. Four formalin-fixed hippocampal specimens were obtained from patients with advanced AD. Specimens underwent 0.1mm 7.0T MRI examination, followed by paraffin embedding and staining with DAB-enhanced iron. SEM images were obtained and aligned relative to optical micrographs/MRI by fiducial marks. Energy dispersive X-ray spectroscopy (EDS) was applied to locate iron deposits in regions of interest. Focused ion beam microscopy was used to remove ∼10x10x1μm sections and thin to electron transparency. Electron energy loss spectra (EELS) were collected in the scanning transmission electron microscope (STEM) with a 100nm pixel size to map elements in the thin lamella. Fe rich regions were located by EDS in all four patient samples with very good spatial agreement to DAB enhanced Perl's iron stain in optical histology (Figure 1). Zinc X-ray peaks were also generated near iron deposits in two of the four specimens. STEM-EELS mapping demonstrated that Zn and Fe have different distributions at the micron to sub-micron scale, with Zn being more localized and Fe more disperse (Figure 2). The shape of the Fe spectra suggest a mixed 2+/3+ valence (Figure 3).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".