Emerging Developments in Targeting Proteotoxicity in Neurodegenerative Diseases
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
The most common neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease, frontotemporal lobar degeneration, and the motor neuron diseases, with AD affecting approximately 6% of people aged 65 years and older, and PD affecting approximately 1% of people aged over 60 years. Specific proteins are associated with these neurodegenerative diseases, as determined by both immunohistochemical studies on post-mortem tissue and genetic screening, where protein misfolding and aggregation are key hallmarks. Many of these proteins are shown to misfold and aggregate into soluble non-native oligomers and large insoluble protein deposits (fibrils and plaques), both of which may exert a toxic gain of function. Proteotoxicity has been examined intensively in cell culture and in in vivo models, and clinical trials of methods to attenuate proteotoxicity are relatively new. Therapies to enhance cellular protein quality control mechanisms such as upregulation of chaperones and clearance/degradation pathways, as well as immunotherapies against toxic protein conformations, are being actively pursued. In this article, we summarize the common pathophysiology of neurodegenerative disease, and review therapies in early-phase clinical trials that target the proteotoxic component of several neurodegenerative 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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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