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Vital pulp therapy and root canal therapy (RCT) are the dominant treatment for irreversible pulpitis. While the success rate of these procedures is favorable, they have some limitations. For instance, RCT leads to removing significant dentin in the coronal third of the tooth that increases root-fracture risk, which forces tooth removal. The ideal therapeutic goal is dental pulp regeneration, which is not achievable with RCT. Specialized proresolving mediators (SPMs) are well known for inflammatory resolution. The resolution of inflammation and tissue restoration or regeneration is a dynamic and continuous process. SPMs not only have potent immune-modulating functions but also effectively promote tissue homeostasis and regeneration. Resolvins have been shown to promote dental pulp regeneration. The purpose of this study was to explore further the cellular target of Resolvin E1 (RvE1) therapy in dental pulp regeneration and the impact of RvE1 in infected pulps. We investigated the actions of RvE1 on experimentally exposed pulps with or without microbial infection in an Axin2 Cre-Dox ; Ai14 genetically defined mouse model. Our results showed RvE1 promoted Axin2-tdTomato+ cell expansion and odontoblastic differentiation after direct pulp capping in the mouse, which we used to mimic reversible pulpitis cases in the clinic. In cultured mouse dental pulp stem cells (mDPSCs), RvE1 facilitated Axin2-tdTomato+ cell proliferation and odontoblastic differentiation and also rescued impaired functions after lipopolysaccharide stimulation. In infected pulps exposed to the oral environment for 24 h, RvE1 suppressed inflammatory infiltration, reduced bacterial invasion in root canals, and prevented the development of apical periodontitis, while its proregenerative impact was limited. Collectively, topical treatment with RvE1 facilitated dental pulp regenerative properties by promoting Axin2-expressing cell proliferation and differentiation. It also modulated the resolution of inflammation, reduced infection severity, and prevented apical periodontitis, presenting RvE1 as a novel therapeutic for treating endodontic diseases. [ABSTRACT FROM AUTHOR]

In recent years, the effective hydrogen release from hydrogen storage materials has attracted extensive attention. In this work, CoCl2·6H2O (Cobalt chloride hexahydrate, CCH), a low cost and easily available catalyst, was successfully used to catalyze the hydrolysis of NaBH4-NH3BH3 composite (xSB-AB, x is the molar ratio of SB to AB). The results show that the synergistic effect produced by ball milling is beneficial to improve the hydrogen release performance of xSB-AB hydrolysis. This work not only advances the understanding of the synergistic effect of SB and AB, but also provides a basis for using low-cost catalysts to improve the hydrolysis performance of xSB-AB. [ABSTRACT FROM AUTHOR]

NaBH4-NH3BH3 composite (xSB-AB, x is the molar ratio of SB to AB) has better hydrolysis performance than its monomer, but the hydrogen generation rate (HGR) and hydrogen yield (HY) are still not ideal at room temperature. In this work, a low cost and easily available commercial porous spherical Co3O4 was successfully used to catalyze the hydrolysis of xSB-AB composite. It was found that Co3O4 showed good catalytic performance for the hydrolysis of xSB-AB, and the HY and hydrogen release efficiency (HRE) of 4SB-AB/10 wt%Co3O4 at 40 C° reached 2,279.71 mL·g-1 and 89.13%, respectively. [ABSTRACT FROM AUTHOR]

Designing the protection system of high voltage power systems involves elucidating the properties of gas breakdown. This work presents a simplified mathematical approach based on grey and fuzzy methods to estimate the probability of gas breakdown, which differs from other grey-fuzzy methods. With the help of the data normalization process and a linear fuzzy membership function in determining the proper value of the distinguishing coefficient, the proposed approach outperforms other grey-fuzzy-based methods, the curve fitting method, and the neural network method in terms of estimation accuracy. The average relative error of test cases 1 and 2 with the normalization processing is 6.43%, which is 2.53 lower than that of the neural network method, 4.35% lower than that of the curve fitting method, 17.64 lower than that of the 5-interval max-max fuzzy-grey method, 1.51% lower than that of the 5-interval min-min fuzzy-grey method, 13.87% lower than that of the 3-interval max-max method, and 2.72% lower than that of the 3-interval min-min method.

The corrosion resistance of slag‐free self‐shielded metal‐cord welding overlay with different ferrotitanium additions in alkaline solution (pH=14) was investigated. The results show that the self‐corrosion potential Ecorr is the highest and the self‐corrosion current density Icorr is the minimum when the addition of ferrotitanium is 6 wt. %. titanium carbide corrodes before other carbides in alkaline solution due to the absence of iron. A ferrotitanium addition of 6 wt. % can provide the best corrosion resistance to the fabricated welding overlays, as an increased ferrotitanium addition will result in a decrease in chromium carbide and an increase in titanium carbide, of which titanium carbide is the most prone to corrosion, followed by chromium carbide, and finally the austenitic matrix. [ABSTRACT FROM AUTHOR]

The formation of octonary DyErGdHoLuScTbY and senary DyGdHoLaTbY and ErGdHoLaTbY high-entropy alloys (HEAs) with the hexagonal close-packed (HCP) structure was reported in this study. Experiments using scanning electron microscopy and x-ray diffraction confirmed the single HCP solid solution in the as-cast state for these three HEAs if the presence of minor rare-earth oxides due to contamination from processing is ignored. The measured compressive yield stress values for these HEAs at room temperature are 245, 205, and 360 MPa for the ErGdHoLaTbY, DyGdHoLaTbY, and DyErGdHoLuScTbY HEAs, respectively. The corresponding solid solution strengthening contributions for these HEAs were estimated using a simple elastic model, and the resulting contributions were 28 MPa, 27 MPa, and 42 MPa for the three aforementioned HEAs. [ABSTRACT FROM AUTHOR]

Epilepsy is a serious neurological condition caused by a sudden abnormality of brain neurons. An accurate epilepsy detection based on electroencephalogram (EEG) signals can provide vital information for diagnosis and treatment. In this study, we propose a lightweight automatic epilepsy detection system with artificial neural network based on our as-fabricated neuromorphic chip. The proposed system utilizes a neural network model to achieve high-accuracy detection without the need for epilepsy-related prior knowledge. The model uses a filter module and a convolutional neural network to preprocess the raw EEG signal and uses a long short-term memory recurrent neural network and a fully connected network as the classifier. In the examination, the classification accuracy of the normal cases and seizures approaches 99.10%, and the accuracy of the normal cases, and interictal and seizure cases can reach 94.46%. This design provides possible epilepsy detection in wearable or portable devices. [ABSTRACT FROM AUTHOR]