Protective effects of Withania somnifera extract in SOD1G93A mouse model of amyotrophic lateral sclerosis
Bibliographic record
Abstract
Withania somnifera (WS; commonly known as Ashwagandha or Indian ginseng) is a medicinal plant whose extracts have been in use for centuries in various regions of the world as a rejuvenator. There is now a growing body of evidence documenting neuroprotective functions of the plant extracts or its purified compounds in several models of neurodegenerative diseases including amyotrophic lateral sclerosis (ALS). Based on the extract's beneficial effect in a mouse model of ALS with TDP-43 proteinopathy , the current study was designed to test its efficacy in another model of familial ALS. Our results show that administration of WS extracts by gavage to mice expressing G93A mutant form of superoxide dismutase (SOD1) resulted in increased longevity, improved motor performance and increased number of motor neurons in lumbar spinal cord. The WS treatment caused substantial reduction in levels of misfolded SOD1whereas it enhanced expression of cellular chaperons in spinal cord of SOD1 G93A mice. WS markedly reduced glial activation and prevented phosphorylation of nuclear factor kappaB (NF-κB). The overall immunomodulatory effect of WS was further evidenced by changes in expression of multiple cytokines/chemokines. WS also served as an autophagy inducer which may be beneficial at early stages of the disease. These results suggest that WS extracts might constitute promising therapeutics for treatment of ALS with involvement of misfolded SOD1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".