<scp>RNA</scp> Binding Protein Dysfunction Links Smoldering/Slowly Expanding Lesions to Neurodegeneration in Multiple Sclerosis
Bibliographic record
Abstract
Objective Despite the advances in treatments for multiple sclerosis (MS), unremitting neurodegeneration continues to drive disability and disease progression. Smoldering/slowly expanding lesions (SELs) and dysfunction of the RNA binding protein (RBP) heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) are pathologic hallmarks of MS cortex and intricately tied to disability and neurodegeneration, respectively. We hypothesized that neuronal hnRNP A1 dysfunction contributes to neurodegeneration and is exacerbated by smoldering/SELs in progressive MS. Methods Neuronal hnRNP A1 pathology (nucleocytoplasmic mislocalization of hnRNP A1) was examined in healthy control and MS brains using immunohistochemistry. MS cases were stratified by severity of hnRNP A1 pathology to examine the link between RBP dysfunction, demyelination, and neurodegeneration. Results We found that smoldering/SELs were only present within a subset of MS tissues characterized by elevated neuronal hnRNP A1 pathology (MS‐A1high) in adjacent cortical gray matter. In contrast to healthy controls and MS with low hnRNP A1 pathology (MS‐A1low), MS‐A1high showed elevated markers of neurodegeneration, including neuronal loss and injury, brain atrophy, axonal loss, and axon degeneration. Additionally, we discovered a subpopulation of morphologically intact neurons lacking expression of NeuN, a neuron‐specific RBP, in cortical projection neurons in MS‐A1high cases. Interpretation hnRNP A1 dysfunction contributes to neurodegeneration and may be exacerbated by smoldering/SELs in progressive MS. The discovery of NeuN‐negative neurons suggests that some cortical neurons may only be injured and not lost. By characterizing RBP pathology in MS cortex, this study has important implications for understanding the pathogenic mechanisms driving neurodegeneration, the substrate of disability and disease progression. ANN NEUROL 2025;97:313–328
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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 | 0.000 |
| 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".