Cerebral ischemia-reperfusion injury (CIRI) lacks effective treatments. Astaxanthin (AST) ameliorates CIRI, but the underlying mechanisms remain unclear.

This study aimed to examine the effects of sericin on diabetic cognitive impairment (DCI) in rats based on neuroinflammation. SD rats were first fed with a high sugar and high fat diet for 4 weeks, and then injected with 50 mg/kg streptozotocin intraperitoneally to establish a diabetic model. The diabetic rats were randomly divided into 3 groups and treated with distilled water (n = 10), 500 mg/kg (n = 10) or 1000 mg/kg (n = 20) sericin, respectively, by gavage once a day for 8 weeks. Before the end of the trial, 10 rats in the 1000 mg/kg sericin group were injected with 10 μg EX527, a Sirtuin-1 (SIRT1) inhibitor, into the lateral ventricles once every other day for 5 times. Treated with sericin significantly reduced fasting blood glucose and improved DCI in rats. Sericin significantly inhibited neuroinflammation and microglial activation, reduced the expression of NOD-like receptor protein 3 (NLRP3) and thioredoxin-interacting protein (TXNIP) proteins, and reduced cell apoptosis, while increasing the expression of SIRT1 protein in the hippocampus of diabetic rats. After inhibiting SIRT1 with EX527, the above effect of sericin on DCI rats was weakened. These results indicated that sericin may block DCI progression in rats by inhibiting TXNIP/NLRP3 neuroinflammation and neuronal apoptosis through SIRT1.

Traumatic intracerebral hemorrhage (TICH) is a severe neurological emergency whose metabolic mechanisms remain largely unresolved. This study used serum metabolomics analysis to compare the metabolic profiles of TICH patients (n = 10) with non-TICH controls (n = 10). LC-MS/MS analysis identified a total of 3183 metabolites. The relative abundances of benzene and its derivatives, organic acids and their derivatives were significantly increased in the serum of ICH patients, while fatty acid, glycerophospholipid, and sphingomyelin metabolites were generally decreased. Multidimensional statistical methods were used to screen for significantly differentially expressed metabolites. Metabolite regulatory networks revealed the potential roles of amino acid metabolism, lipid metabolism (GP/SP/FA), and aromatic compounds (benzenes) in regulating TICH metabolism. Enrichment analysis revealed that these metabolites were highly enriched in lipid metabolism, amino acid metabolism, and neural signaling pathways, and were significantly associated with inflammatory drug pathways. The differentially expressed metabolites were further mapped to the Human Metabolite Database (HMDB) and subjected to metabolic-disease association analysis, suggesting that these metabolites may be closely related to the pathogenesis of TICH. ROC analysis demonstrated good diagnostic performance of five key metabolites in both discovery and validation cohorts (AUC > 0.70), with hypoxanthine and L-histidine showing the highest accuracy (AUC > 0.93). Correlation analysis indicated that key metabolites were associated with hematoma volume, NIHSS and GCS scores. In vitro experiments further confirmed that, under conditions of neuronal injury, the changes in these metabolites exhibit consistency. This study provides a theoretical basis for the development of potential biomarkers and precise intervention strategies.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, cognition, and behavioral functions, making early and accurate stage classification essential for timely clinical intervention and treatment planning. Conventional MRI-based diagnostic approaches are often limited by noise sensitivity, manual interpretation, and insufficient capability to model complex non-linear neuroimaging relationships across multiple disease stages. To address these limitations, this paper proposes a novel Hybrid Quantum-Classical Spike-Driven Network optimized with the Bobcat Optimization Algorithm(HQSDNet-BOA)for automated multistage AD classification using MRI data. The suggested framework introduces an integrated architecture that combines Square Root Sage-Husa Adaptive Robust Kalman Filtering(SRS-HARKF)for adaptive noise suppression and covariance stabilization, Graph-Enhanced Fuzzy Clustering (GEFC)for structurally consistent brain tissue segmentation, and a Hybrid Structural Graph Attention Network(HSGAN)for learning discriminative local-global anatomical representations. Moreover, Hybrid Quantum-Classical Spike-Driven Network (HQSDNet), which incorporates quantum convolutional learning, spike-driven transformers, and structural attention schemes, is designed to precisely identify nonlinear and spatial-temporal disease patterns. Bobcat Optimization Algorithm (BOA) is used for optimizing network parameters in real-time to ensure convergence efficiency and computational optimization. This study was performed using two datasets: ADNI (5,300 images representing five disease stages) and OASIS-3 (3,712 images representing three cognitive classes). The suggested HQSDNet-BOA yielded improved classification results with 98.8% accuracy, 98.0% precision, 98.5% recall, and 98.25% F1-score when applied to the ADNI dataset and outperformed the existing techniques in terms of computational speed. The obtained results confirm that the suggested framework provides a robust, efficient, and clinically relevant solution for accurate AD stage prediction and neuroimaging-based diagnostic support.