Selective Loss of trans-Acting Instability Determinants of Neurofilament mRNA in Amyotrophic Lateral Sclerosis Spinal Cord
Bibliographic record
Abstract
Neurofilament (NF) aggregates in motor neurons are a key neuropathological feature of amyotrophic lateral sclerosis (ALS). We have previously observed an alteration in the stoichiometry of NF subunit steady state mRNA levels in ALS spinal motor neurons using in situ hybridization and proposed that this led to aggregate formation. We have now examined the levels of NF mRNA in whole tissue homogenates of spinal cord using the RNase protection assay and real time reverse transcriptase-PCR and observed significant elevations of NF mRNA level in ALS. Compared with age-matched control, we observed a greater stability of heterogeneously expressed NFL mRNA in the presence of ALS spinal cord homogenates. Heat denaturing or protease K digestion of the control homogenates increased the stability of the NFL mRNA to levels observed in ALS homogenate. Increased NFL mRNA stability was also induced by increasing the percentage of ALS homogenate in an admixture of control and ALS homogenates. These observations suggest the presence of trans-acting NFL mRNA-destabilizing elements in control but not in ALS spinal cord homogenates. This was confirmed in gel retardation assays. We also observed that the destabilizing elements interact with the 3'-untranslated region of NFL mRNA. These findings suggest that the trans-acting NFL-destabilizing elements are selectively suppressed in ALS homogenates, resulting in an increased stability and level of NFL mRNA.
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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 | 0.000 | 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) | 0.001 | 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 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".