Cross-talk between amyloidogenic proteins in type-2 diabetes and Parkinson’s disease
Bibliographic record
Abstract
In type-2 diabetes (T2D) and Parkinson's disease (PD), polypeptide assembly into amyloid fibers plays central roles: in PD, α-synuclein (aS) forms amyloids and in T2D, amylin [islet amyloid polypeptide (IAPP)] forms amyloids. Using a combination of biophysical methods in vitro we have investigated whether aS, IAPP, and unprocessed IAPP, pro-IAPP, polypeptides can cross-react. Whereas IAPP forms amyloids within minutes, aS takes many hours to assemble into amyloids and pro-IAPP aggregates even slower under the same conditions. We discovered that preformed amyloids of pro-IAPP inhibit, whereas IAPP amyloids promote, aS amyloid formation. Amyloids of aS promote pro-IAPP amyloid formation, whereas they inhibit IAPP amyloid formation. In contrast, mixing of IAPP and aS monomers results in coaggregation that is faster than either protein alone; moreover, pro-IAPP can incorporate aS monomers into its amyloid fibers. From this intricate network of cross-reactivity, it is clear that the presence of IAPP can accelerate aS amyloid formation. This observation may explain why T2D patients are susceptible to developing PD.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".