Acute Viral Infection Accelerates Neurodegeneration in a Mouse Model of ALS
Bibliographic record
Abstract
Summary While several viral infections have been associated with amyotrophic lateral sclerosis (ALS), the mechanism(s) through which they promote disease has remained almost entirely elusive. This study investigated the impact of common, acute viral infections prior to disease onset on ALS progression in the SOD1 G93A mouse model. A single sublethal infection prior to onset of ALS clinical signs was associated with markedly accelerated ALS disease progression characterized by rapid loss of hindlimb function. Prior infection resulted in gliosis in the lumbar spine and upregulation of transcriptional pathways involved in inflammatory responses, metabolic dysregulation, and muscular dysfunction. Therapeutic suppression of gliosis with an anti-inflammatory small molecule, or administration of a direct-acting antiviral, was associated with significantly improved ALS clinical signs, akin to what was observed in uninfected animals. This study provides causal and mechanistic evidence that the immune response elicited by acute viral infections may be an important etiological factor that alters ALS disease trajectory, and provides insight into novel therapeutic and preventative strategies for ALS. One Sentence Summary Acute viral infection with influenza A virus and SARS-CoV-2 accelerates the progression of ALS in SOD1 G93A mice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".