Jia-Wei-Kai-Xin-San Treatment Alleviated Mild Cognitive Impairment through Anti-Inflammatory and Antiapoptotic Mechanisms in SAMP8 Mice
Bibliographic record
Abstract
Background. Alleviating mild cognitive impairment (MCI) is crucial to delay the progression of Alzheimer’s disease (AD). Jia-Wei-Kai-Xin-San (JWKXS) is applied for treating AD with MCI. However, the mechanism of JWKXS in the treatment of MCI is unclear. Thus, this study aimed to investigate the effect and mechanism of JWKXS in SAMP8 mice models of MCI. Methods. MCI models were established to examine learning and memory ability and explore the pathomechanisms in brain of SAMP8 mice at 4, 6, and 8 months. The mice were treated for 8 weeks and the effects of JWKXS on MCI were characterized through Morris water maze and HE/Nissl’s/immunohistochemical staining. Its mechanism was predicted by the combination of UPLC-Q-TOF/MS and system pharmacology analysis, further verified with SAMP8 mice, BV2 microglial cells, and PC12 cells. Results. It was found that 4-month-old SAMP8 mice exhibited MCI. Two months of JWKXS treatment improved the learning and memory ability, alleviated the hippocampal tissue and neuron damage. Through network pharmacology, four key signaling pathways were found to be involved in treatment of MCI by JWKXS, including TLR4/NF-κB pathway, NLRP3 inflammasome activation, and intrinsic and extrinsic apoptosis. In vitro and in vivo experiments demonstrated that JWKXS attenuated neuroinflammation by inhibiting microglia activation, suppressing TLR4/NF-κB and NLRP3 inflammasome pathways, and blocking the extrinsic and intrinsic apoptotic pathways leading to neuronal apoptosis suppression in the hippocampus. Conclusion. JWKXS treatment improved the learning and memory ability and conferred neuroprotective effects against MCI by inducing anti-inflammation and antiapoptosis. Limitations. The small sample size and short duration of the intervention limit in-depth investigation of the mechanisms. Future Prospects. This provides a direction for further clarification of the anti-AD mechanism, and provides certain data support for the formulation to move toward clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".