A Multimodal Spectroscopic Imaging Method To Characterize the Metal and Macromolecular Content of Proteinaceous Aggregates (“Amyloid Plaques”)
Bibliographic record
Abstract
Alzheimer's disease (AD) is a major international health and economic concern. A key pathological feature of AD is so-called "amyloid-β-plaques", or "Aβ-plaques", which are deposits of aggregated protein, enriched with the Aβ fragment of amyloid precursor protein. Despite their name, the deposits are not pure Aβ and have a heterogeneous, chemically complex composition that can include multiple proteins, lipids, and metal ions (Fe, Cu, or Zn). Despite extensive research, it is still uncertain whether Aβ-plaques are a cause or a consequence of AD pathology. Further characterization of the elemental and biochemical composition within and surrounding Aβ-plaques, and knowledge of how composition varies with disease state or progression, may provide important insight into the relationship between Aβ-plaques and AD pathology. With this aim in mind, herein we demonstrate a multimodal spectroscopic imaging workflow to better characterize the complex composition of Aβ-plaques. Our approach incorporates several spectroscopic imaging techniques, such as Fourier transform infrared spectroscopic imaging (FTIR), Raman microscopy, and X-ray fluorescence microscopy (XFM). While FTIR, Raman, and XFM have been used previously, mostly in isolation, to study Aβ-plaques, application of all three techniques, in combination with histology and fluorescence microscopy, has not been reported previously. We demonstrate that a multimodal workflow, incorporating all three methods on adjacent or serial tissue sections, can reveal substantial complementary information about the biochemical and elemental composition of Aβ-plaques. Information revealed by the method includes the relative content and distribution of aggregated protein, total lipid, lipid esters, cholesterol, and metals (Fe, Cu, or Zn).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".