Generalization of the Prion Hypothesis to Other Neurodegenerative Diseases: An Imperfect Fit
Bibliographic record
Abstract
Protein misfolding diseases have been classically understood as diffuse errors in protein folding, with misfolded protein arising autonomously throughout a tissue due to a pathologic stressor. The field of prion science has provided an alternative mechanism whereby a seed of pathologically misfolded protein, arising exogenously or through a rare endogenous structural fluctuation, yields a template to catalyze misfolding of the native protein. The misfolded protein may then spread intercellularly to communicate the misfold to adjacent areas and ultimately infect a whole tissue. Mounting evidence implicates a prion-like process in the propagation of several neurodegenerative diseases, including Alzheimer's, Parkinson's, Huntington's, amyotrophic lateral sclerosis, and the tauopathies. However, the parallels between the events observed in these conditions and those in prion disease are often incomplete. The aim of this review was to examine the current state of knowledge concerning the mechanisms of protein misfolding and aggregation for neurodegeneration-associated proteins. In addition, possible methods of intercellular spread are described that focus on the hypothesis that released microvesicles function as misfolded protein delivery vehicles, and the therapeutic options enabled by viewing these diseases from the prion perspective.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".