Quantitative detection of grey and white matter amyloid pathology using a combination of K114 and CRANAD-3 fluorescence
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease that exacts a huge toll on the patient, the healthcare system and society in general. Abundance and morphology of protein aggregates such as amyloid β plaques and tau tangles, along with cortical atrophy and gliosis are used as measures to assess the changes in the brain induced by the disease. Not all of these parameters have a direct correlation with cognitive decline. Studies have shown that only particular protein conformers can be the main drivers of disease progression, and conventional approaches are unable to distinguish different conformations of disease-relevant proteins. METHODS AND RESULTS: Using the fluorescent amyloid probes K114 and CRANAD-3 and spectral confocal microscopy, we examined formalin-fixed paraffin-embedded brain samples from different control and AD cases. Based on the emission spectra of the probes used in this study, we found that certain spectral signatures can be correlated with different aggregates formed by different proteins. The combination of spectral imaging and advanced image analysis tools allowed us to detect variability of protein deposits across the samples. CONCLUSION: Our proposed method offers a quicker and easier neuropathological assessment of tissue samples, as well as introducing an additional parameter by which protein aggregates can be discriminated.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".