Quantitation of Tissue Amyloid via Fluorescence Spectroscopy Using Controlled Concentrations of Thioflavin-S
Bibliographic record
Abstract
Amyloids are misfolded proteins that aggregate into fibrillar structures, the accumulation of which is associated with the pathogenesis of many neurodegenerative diseases, such as Alzheimer's disease (AD). Early, sensitive detection of these misfolded aggregates is of great interest to the field, as amyloid deposition begins well before the presentation of clinical symptoms. Thioflavin-S (ThS) is a fluorescent probe commonly used to detect amyloid pathology. Protocols for ThS staining vary, but they often use high staining concentrations followed by differentiation, which causes varying levels of non-specific staining and potentially leaves more subtle amyloid deposition unidentified. In this study, we developed an optimized ThS staining protocol for the sensitive detection of β-amyloids in the widely used 5xFAD Alzheimer's mouse model. Controlled dye concentrations together with fluorescence spectroscopy and advanced analytical methods enabled not only the visualization of plaque pathology, but also the detection of subtle and widespread protein misfolding throughout the 5xFAD white matter and greater parenchyma. Together, these findings demonstrate the efficacy of a controlled ThS staining protocol and highlight the potential use of ThS for the detection of protein misfolding that precedes clinical manifestation of disease.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".