Withaferin-A Treatment Alleviates TAR DNA-Binding Protein-43 Pathology and Improves Cognitive Function in a Mouse Model of FTLD
Bibliographic record
Abstract
Withaferin-A, an active withanolide derived from the medicinal herbal plant Withania somnifera induces autophagy, reduces TDP-43 proteinopathy, and improves cognitive function in transgenic mice expressing mutant TDP-43 modelling FTLD. TDP-43 is a nuclear DNA/RNA-binding protein with cellular functions in RNA transcription and splicing. Abnormal cytoplasmic aggregates of TDP-43 occur in several neurodegenerative diseases including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD), and limbic-predominant age-related TDP-43 encephalopathy (LATE). To date, no effective treatment is available for TDP-43 proteinopathies. Here, we tested the effects of withaferin-A (WFA), an active withanolide extracted from the medicinal herbal plant Withania somnifera, in a transgenic mouse model of FTLD expressing a genomic fragment encoding mutant TDP-43G348C. WFA treatment ameliorated the cognitive performance of the TDP-43G348C mice, and it reduced NF-κB activity and neuroinflammation in the brain. WFA alleviated TDP-43 pathology while it boosted the levels of the autophagic marker LC3BII in the brain. These data suggest that WFA and perhaps other autophagy inducers should be considered as potential therapy for neurodegenerative diseases with TDP-43 pathology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".