Hydrogen Attenuates Cognitive Impairment in Rat Models of Vascular Dementia by Inhibiting Oxidative Stress and NLRP3 Inflammasome Activation.

Vascular dementia (VaD) is the second most common form of dementia worldwide. Oxidative stress and neuroinflammation are important factors contributing to cognitive dysfunction in patients with VaD. The antioxidant and anti-inflammatory properties of hydrogen are increasingly being utilized in neurological disorders, but conventional hydrogen delivery has the disadvantage of inefficiency. Therefore, magnesium silicide nanosheets (MSNs) are used to release hydrogen in vivo in larger quantities and for longer periods of time to explore the appropriate dosage and regimen. In this study, it is observed that hydrogen improved learning and working memory in VaD rats in the Morris water maze and Y-maze, which elicits improved cognitive function. Nissl staining of neurons shows that hydrogen treatment significantly improves edema in neuronal cells. The expression and activation of reactive oxygen species (ROS), Thioredoxin-interacting protein (TXNIP), NOD-like receptor protein 3 (NLRP3), caspase-1, and IL-1ß in the hippocampus are measured via ELISA, Western blotting, real-time qPCR, and immunofluorescence. The results show that oxidative stress indicators and inflammasome-related factors are significantly decreased after 7dMSN treatment. Therefore, it is concluded that hydrogen can ameliorate neurological damage and cognitive dysfunction in VaD rats by inhibiting ROS/NLRP3/IL-1ß-related oxidative stress and inflammation.

Photodynamic and nitric oxide therapy-based synergistic antimicrobial nanoplatform: an advanced root canal irrigation system for endodontic bacterial infections.

BACKGROUND: The main issues faced during the treatment of apical periodontitis are the management of bacterial infection and the facilitation of the repair of alveolar bone defects to shorten disease duration. Conventional root canal irrigants are limited in their efficacy and are associated with several side effects. This study introduces a synergistic therapy based on nitric oxide (NO) and antimicrobial photodynamic therapy (aPDT) for the treatment of apical periodontitis. RESULTS: This research developed a multifunctional nanoparticle, CGP, utilizing guanidinylated poly (ethylene glycol)-poly (ε-Caprolactone) polymer as a carrier, internally loaded with the photosensitizer chlorin e6. During root canal irrigation, the guanidino groups on the surface of CGP enabled effective biofilm penetration. These groups undergo oxidation by hydrogen peroxide in the aPDT process, triggering the release of NO without hindering the production of singlet oxygen. The generated NO significantly enhanced the antimicrobial capability and biofilm eradication efficacy of aPDT. Furthermore, CGP not only outperforms conventional aPDT in eradicating biofilms but also effectively promotes the repair of alveolar bone defects post-eradication. Importantly, our findings reveal that CGP exhibits significantly higher biosafety compared to sodium hypochlorite, alongside superior therapeutic efficacy in a rat model of apical periodontitis. CONCLUSIONS: This study demonstrates that CGP, an effective root irrigation system based on aPDT and NO, has a promising application in root canal therapy.

Designing AI-Aided Analysis and Prediction Models for Nonprecious Metal Electrocatalyst-Based Proton-Exchange Membrane Fuel Cells.

Traditionally, a larger number of experiments are needed to optimize the performance of the membrane electrode assembly (MEA) in proton-exchange membrane fuel cells (PEMFCs) since it involves complex electrochemical, thermodynamic, and hydrodynamic processes. Herein, we introduce artificial intelligence (AI)-aided models for the first time to determine key parameters for nonprecious metal electrocatalyst-based PEMFCs, thus avoiding unnecessary experiments during MEA development. Among 16 competing algorithms widely applied in the AI field, decision tree and XGBoost showed good accuracy (86.7 % and 91.4 %) in determining key factors for high-performance MEA. Artificial neural network (ANN) shows the best accuracy (R2=0.9621) in terms of predictions of the maximum power density and a decent reproducibility (R2>0.99) on uncharted I-V polarization curves with 26 input features. Hence, machine learning is shown to be an excellent method for improving the efficiency of MEA design and experiments.