The function of RNA-binding proteins at the synapse: implications for neurodegeneration
Bibliographic record
Abstract
The loss of synapses is a central event in neurodegenerative diseases. Synaptic proteins are often associated with disease neuropathology, but their role in synaptic loss is not fully understood. Of the many processes involved in sustaining the integrity of synapses, local protein translation can directly impact synaptic formation, communication, and maintenance. RNA-binding proteins and their association with RNA granules serve to regulate mRNA transportation and translation at synapses and in turn regulate the synapse. Genetic mutations in RNA-binding proteins FUS and TDP-43 have been linked with causing neurodegenerative diseases: amyotrophic lateral sclerosis and frontotemporal dementia. The observation that mutations in FUS and TDP-43 coincide with changes in RNA granules provides evidence that dysfunction of RNA metabolism may underlie the mechanism of synaptic loss in these diseases. However, we do not know how mutations in RNA-binding proteins would affect RNA granule dynamics and local translation, or if these alterations would cause neurodegeneration. Further investigation into this area will lead to important insights into how disruption of RNA metabolism and local translation at synapses can cause neurodegenerative 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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".