Bibliographic record
Abstract
RNA metabolism is a vital process through which RNA is produced, transported, regulated, stored, and translated or degraded. Recently, the discoveries of mutations in key RNA binding proteins involved in several human neuronal based diseases have firmly placed the process of RNA metabolism as central to disease etiology. This review first recaps the process of RNA metabolism in the mammalian neuron and describes the roles of RNA granules in this process. Using the recently described alterations in TAR DNA binding protein (TDP-43) and fused in sarcoma/translocated in liposarcoma (FUS/TLS) in amyotrophic lateral sclerosis (ALS) and frontal temporal lobar degeneration (FTLD), we discuss how RNA binding protein abnormalities can affect RNA metabolism. We then discuss two additional RNA based mechanisms distinct from alterations in RNA binding protein function that impact RNA metabolism and result in disease. Cumulatively, these observations provide strong support for the hypothesis that alterations in RNA metabolism can lead to neurodegenerative disease, including ALS. Keywords: ALS, miRNA, FUS/TLS, TDP-43, myotonic dystrophy, spinal muscular atrophy, RNA metabolism, stress granule, transport granule, Neurodegenerative Disease, frontal temporal lobar degeneration, neuronal intermediate filament inclusion diseas, fragile X tremor ataxia syndrome, spinocerebellar ataxia type 8, oculopharyngeal muscular dystrophy
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".