Devil's claw has anti-neuroinflammatory capacity through mitochondrial recovering in microglia cells exposed to rotenone
Bibliographic record
Abstract
Neuropsychiatric diseases, such as bipolar disorder, Alzheimer’s, and Parkinson’s disease, are related to mitochondrial dysfunction and chronic inflammatory activation, affecting thousands of people worldwide. Therefore, new alternative therapies with mitochondrial modulation and anti-inflammatory effects, especially involving natural agents are important to search. Harpagophytum procumbens is a notable candidate. It is an African plant with anti-inflammatory action. The objective of this study was to evaluate the anti-neuroinflammatory effect of H. procumbens ethyl acetate fraction in microglia cells. H. procumbens ethyl acetate fraction per se effect was available in SH-SY5Y and BV-2 cells during 24, 48, and 72 hours by cell viability and nitric oxide levels. Only BV-2 cells were exposed to rotenone to induce inflammation-mediated mitochondrial dysfunction and treated with curve concentration of H. procumbens fraction. Cellular proliferation and oxidative metabolism parameters were analyzed. Per se effect results revealed that significative changes are viewer depending on the time of exposure and cell line type. 0.001-400 µg/mL of H. procumbens treatment decreased nitric oxide and reactive oxygen species levels which have been increased by rotenone exposure, furthermore, cell viability recovered after this treatment exposure. H. procumbens was able to act as anti-neuroinflammatory agent, recovering mitochondrial complex I function, reducing oxidative stress, and decreasing cell proliferation.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".