Walking the tightrope: proteostasis and neurodegenerative disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A characteristic of many neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS), is the aggregation of specific proteins into protein inclusions and/or plaques in degenerating brains. While much of the aggregated protein consists of disease specific proteins, such as amyloid-β, α-synuclein, or superoxide dismutase1 (SOD1), many other proteins are known to aggregate in these disorders. Although the role of protein aggregates in the pathogenesis of neurodegenerative diseases remains unknown, the ubiquitous association of misfolded and aggregated proteins indicates that significant dysfunction in protein homeostasis (proteostasis) occurs in these diseases. Proteostasis is the concept that the integrity of the proteome is in fine balance and requires proteins in a specific conformation, concentration, and location to be functional. In this review, we discuss the role of specific mechanisms, both inside and outside cells, which maintain proteostasis, including molecular chaperones, protein degradation pathways, and the active formation of inclusions, in neurodegenerative diseases associated with protein aggregation. A characteristic of many neurodegenerative diseases is the aggregation of specific proteins, which alone provides strong evidence that protein homeostasis is disrupted in these disease states. Proteostasis is the maintenance of the proteome in the correct conformation, concentration, and location by functional pathways such as molecular chaperones and protein degradation machinery. Here, we discuss the potential roles of quality control pathways, both inside and outside cells, in the loss of proteostasis during aging and disease.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it