Protection by genistein on cortical neurons against oxidative stress injury via inhibition of NF-kappaB, JNK and ERK signaling pathway
Bibliographic record
Abstract
CONTEXT: Genistein, one of the isoflavones derived from soybean seeds, has been reported to exert multiple bioactivities. However, the mechanism of its action on the central nervous system is not fully understood. OBJECTIVE: To investigate the cytoprotection of genistein and its molecular mechanism against H2O2-induced cell death in primary rat cortical neurons. MATERIALS AND METHODS: Genistein (0.01, 0.1, and 1 μM) were added into the primary rat neurons 24 h before and co-cultured with 500 μM H2O2 for 1 h. Neuronal injury was assessed by MTT, lactate dehydrogenase (LDH) assay, and Hoechst33258 staining. Intracellular reactive oxygen species (ROS) generation induced by H2O2 was determined. Neuronal apoptosis was evaluated by Bcl-2/Bax ratio as well as by caspase-9 and caspase-3 activities. The protein levels and phosphorylation of NF-κB/p65, IκB, JNK, and ERK were detected by western blots. RESULTS: Genistein pretreatment attenuated H2O2-mediated neuronal viability loss, nuclear condensation, and ROS generation in a concentration-dependent manner. Genistein exerted anti-apoptotic effects by reversing the apoptotic factors Bcl-2 and Bax ratio, along with the suppression of caspase-9 and caspase-3 activities. In addition, genistein down-regulated the expression of NF-κB/p65, and suppressed the phosphorylation of p65 and IκB. Genistein also inhibited H2O2-induced activation of the MAPK-signaling pathway including JNK and ERK. DISCUSSION AND CONCLUSION: The results indicated that genistein effectively protects cortical neurons against oxidative stress at least partly via inactivation of NF-κB as well as MAPK-signaling pathways, and suggested the possibility of this antioxidant for the prevention and treatment of stroke.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".