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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".