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Abstract Pulmonary artery hypertension (PAH) is a chronic vascular disease defined by the elevation of pulmonary vascular resistance and mean pulmonary artery pressure, which arises due to pulmonary vascular remodelling. Prior research has already established a link between the autonomic nervous system (ANS) and PAH. Therefore, the rebalancing of the ANS offers a promising approach for the treatment of PAH. The process of rebalancing involves two key aspects: inhibiting an overactive sympathetic nervous system and fortifying the impaired parasympathetic nervous system through pharmacological or interventional procedures. However, the understanding of the precise mechanisms involved in neuromodulation, whether achieved through medication or intervention, remains insufficient. This limited understanding hinders our ability to determine the appropriate timing and scope of such treatment. This review aims to integrate the findings from clinical and mechanistic studies on ANS rebalancing as a treatment approach for PAH, with the ultimate goal of identifying a path to enhance the safety and efficacy of neuromodulation therapy and improve the prognosis of PAH.

To reduce the emission of fine particulate matter (PM) from gasoline engines, a gasoline particulate filter (GPF) filtration model that considers the fractal characteristics of both particulate matter and filters is designed and its filtration characteristics are analyzed based on several GPF filtration models. Based on the GPF filtration mechanism, a new mathematical model of fiber efficiency filtration is established after correcting the classical filtration formula based on correcting the particle size of particles and considering the relationship between the porosity of the fiber filter and the fractal dimension of the filter. Simulation of the filtration efficiency of the established mathematical filtration model is carried out to obtain visual filtration efficiency curves. The filtration efficiency and pressure drop of the model are analyzed, and the effects of parameters such as particle fractal dimension, filter fractal dimension, filter thickness, flow rate, and fiber diameter on filtration efficiency are also comprehensively analyzed. The results show that when the fractal dimension of particles is in the range of 1.8-2.8, the most penetrating particle size decreases with the increase of the fractal dimension of particles. When the particle size is small, the effect of fractal dimension on filtration efficiency is not obvious. When the particle size is larger than the most penetrating particle range, the larger the fractal dimension, the filtration efficiency increases obviously. Considering the relationship between the fractal dimension of the filter body and the porosity and the influence of each on the filtration efficiency, the optimal fractal dimension of the filter body should be between 1.82 and 1.925, and the thickness of the filter has some influence on the filtration efficiency. The slower the gas flow rate is, the better the trapping effect will be. However, considering the problem of automobile emission, a lower exhaust speed can be chosen. Increasing the fiber diameter can improve the trapping efficiency of the model, but will reduce the porosity of the trapping collective. Therefore, the fiber diameter should not be too large under the combined action.

Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves mAP@0.5 and mAP@0.5:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment. [ABSTRACT FROM AUTHOR]

Background: Multiple studies have demonstrated that the gut microbiome is closely related to the onset of Alzheimer's disease, but the causal relationship between the gut microbiome and AD, as well as potential mediating factors, have not been fully explored. Objective: Our aim is to validate the causal relationship between the gut microbiome and the onset of AD and determine the key mechanism by which the gut microbiome mediates AD through blood metabolites using Mendelian randomization (MR) analysis methods. Methods: We first conducted bidirectional and mediating MR analyses using gut microbiota, blood amino acid metabolites, and AD-related single nucleotide polymorphisms as research data. In the analysis process, the inverse variance-weighted average method was mainly used as the primary method, with other methods serving as supplementary evidence. Results: Ultimately, we found that six types of gut bacteria and two blood amino acid metabolites have a causal effect on AD. Subsequent mediation analysis proved that decreased glutamine concentration mediates the negative causal effect of Holdemanella bacteria on AD (mediation ratio of 14.5%), and increased serum alanine concentration mediates the positive causal effect of Parabacteroide bacteria on AD (mediation ratio of 9.4%). Conclusions: Our study demonstrates the causality of Holdemanella and Parabacteroides bacteria in the onset of AD and suggests that the reduced glutamine and increased alanine serums concentration may be key nodes in mediating this effect. [ABSTRACT FROM AUTHOR]