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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".