Dual-ligand fluorescence microscopy enables chronological and spatial histological assignment of distinct amyloid-β deposits
Bibliographic record
Abstract
Different types of deposits comprised of amyloid-β (Aβ) peptides are one of the pathological hallmarks of Alzheimer's disease (AD) and novel methods that enable identification of a diversity of Aβ deposits during the AD continuum are essential for understanding the role of these aggregates during the pathogenesis. Herein, different combinations of five fluorescent thiophene-based ligands were used for detection of Aβ deposits in brain tissue sections from transgenic mouse models with aggregated Aβ pathology, as well as brain tissue sections from patients affected by sporadic or dominantly inherited AD. When analyzing the sections with fluorescence microscopy, distinct ligand staining patterns related to the transgenic mouse model or to the age of the mice were observed. Likewise, specific staining patterns of different Aβ deposits were revealed for sporadic versus dominantly inherited AD, as well as for distinct brain regions in sporadic AD. Thus, by using dual-staining protocols with multiple combinations of fluorescent ligands, a chronological and spatial histological designation of different Aβ deposits could be achieved. This study demonstrates the potential of our approach for resolving the role and presence of distinct Aβ aggregates during the AD continuum and pinpoints the necessity of using multiple ligands to obtain an accurate assignment of different Aβ deposits in the neuropathological evaluation of AD, as well as when evaluating therapeutic strategies targeting Aβ aggregates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".