Protein misfolding: understanding biology to classify and treat synucleinopathies
Bibliographic record
Abstract
Protein misfolding and aggregation is a major pathological hallmark in a variety of human conditions, including cancer, diabetes, and neurodegeneration. However, we still do not fully understand the role of protein accumulation in disease. Interestingly, recent breakthroughs in artificial intelligence (AI) are having a tremendous impact on our ability to predict three-dimensional protein structures and understand the molecular rules governing protein folding/misfolding. This progress will enable us to understand how intrinsic and extrinsic factors trigger protein misfolding, thereby changing protein function. These changes, in some cases, are related to normal biological responses and, in other cases, associated with pathological alterations, such as those found in many neurodegenerative disorders. Here, we provide a brief historical perspective of how findings in the field of prion diseases and prion biology have enabled tremendous advances that are now forming the basis for our understanding of disease processes and discuss how this knowledge is now emerging as central for our ability to classify, diagnose, and treat devastating neurodegenerative disorders such as Parkinson's and Alzheimer's diseases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".