Distinct Chemical Determinants are Essential for Achieving Ligands for Superior Optical Detection of Specific Amyloid‐β Deposits in Alzheimer's Disease
Bibliographic record
Abstract
Aggregated forms of different proteins are common hallmarks for several neurodegenerative diseases, including Alzheimer's disease, and ligands that selectively detect specific protein aggregates are vital. Herein, we investigate the molecular requirements of thiophene-vinyl-benzothiazole based ligands to detect a specific type of Aβ deposits found in individuals with dominantly inherited Alzheimer's disease caused by the Arctic APP E693G mutation. The staining of these Aβ deposits was alternated when switching the terminal heterocyclic moiety attached to the thiophene-vinyl-benzothiazole scaffold. The most prevalent staining was observed for ligands having a terminal 3-methyl-1H-indazole moiety or a terminal 1,2-dimethoxybenzene moiety, verifying that specific molecular interactions between these ligands and the aggregates were necessary. The synthesis of additional thiophene-vinyl-benzothiazole ligands aided in pinpointing additional crucial chemical determinants, such as positioning of nitrogen atoms and methyl substituents, for achieving optimal staining of Aβ aggregates. When combining the optimized thiophene-vinyl-benzothiazole based ligands with a conventional ligand, CN-PiB, distinct staining patterns were observed for sporadic Alzheimer's disease versus dominantly inherited Alzheimer's disease caused by the Arctic APP E693G mutation. Our findings provide chemical insights for developing novel ligands that allow for a more precise assignment of Aβ deposits, and might also aid in creating novel agents for clinical imaging of distinct Aβ aggregates in AD.
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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) | 0.000 | 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) | 0.001 | 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".