Neutrophilic inflammation, which often persists over days despite appropriate antibiotic therapy, contributes substantially to brain damage in bacterial meningitis. We hypothesized that persistent inflammation is the consequence of a vicious cycle in which inflammation-induced cell injury leads to the release of endogenous danger molecules (e.g. high mobility group box 1) that drive the inflammatory response, causing further damage. The present study aimed to assess the mechanisms of high mobility group box 1 protein release and its functional relevance for the development and progression of pneumococcal meningitis. High mobility group box 1 was found in large quantities in cerebrospinal fluid samples of patients and mice with pneumococcal meningitis (predominantly in advanced stages of the disease). By using macrophages, we demonstrated that the release of high mobility group box 1 from macrophages following pneumococcal challenge is passive in nature and probably not connected with inflammasome- and oxidative stress-dependent inflammatory cell death forms. In a mouse meningitis model, treatment with the high mobility group box 1 antagonists ethyl pyruvate or Box A protein had no effect on the development of meningitis, but led to better resolution of inflammation during antibiotic therapy, which was accompanied by reduced brain pathology and better disease outcome. Additional experiments using gene-deficient mice and murine neutrophils provided evidence that high mobility group box 1 acts as a chemoattractant for neutrophils in a receptor for advanced glycosylation end products-dependent fashion. In conclusion, the present study implicated high mobility group box 1, likely released from dying cells, as a central propagator of inflammation in pneumococcal meningitis. Because persistent inflammation contributes to meningitis-associated brain damage, high mobility group box 1 may represent a promising target for adjunctive therapy of this disease.
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Sensors (Basel)
December 2024
Institute of Mechanical Intelligence, Scuola Superiore Sant'Anna, Via G. Moruzzi 1, 56124 Pisa, Italy.
CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over an extended region. Data handling in DAS remains an open issue, as many applications need continuous monitoring of a volume of samples whose storage and processing in real time require high-capacity memory and computing resources.
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December 2024
CARISSMA Institute of Electric, Connected, and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.
The perception of the vehicle's environment is crucial for automated vehicles. Therefore, environmental sensors' reliability and correct functioning are becoming increasingly important. Current vehicle inspections and self-diagnostics must be adapted to ensure the correct functioning of environmental sensors throughout the vehicle's lifetime.
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December 2024
Department of Industrial Design, Guangdong University of Technology, Guangzhou 510006, China.
Research into new solutions for wearable assistive devices for the visually impaired is an important area of assistive technology (AT). This plays a crucial role in improving the functionality and independence of the visually impaired, helping them to participate fully in their daily lives and in various community activities. This study presents a bibliometric analysis of the literature published over the last decade on wearable assistive devices for the visually impaired, retrieved from the Web of Science Core Collection (WoSCC) using CiteSpace, to provide an overview of the current state of research, trends, and hotspots in the field.
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December 2024
Center for Automotive Research and Sustainable Mobility (CARS@PoliTO), Politecnico di Torino, 10129 Torino, Italy.
The fusion of multiple sensors' data in real-time is a crucial process for autonomous and assisted driving, where high-level controllers need classification of objects in the surroundings and estimation of relative positions. This paper presents an open-source framework to estimate the distance between a vehicle equipped with sensors and different road objects on its path using the fusion of data from cameras, radars, and LiDARs. The target application is an Advanced Driving Assistance System (ADAS) that benefits from the integration of the sensors' attributes to plan the vehicle's speed according to real-time road occupation and distance from obstacles.
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December 2024
CARISSMA Institute of Electric, Connected and Secure Mobility, Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.
Cooperative intelligent transportation systems continuously send self-referenced data about their current status in the Cooperative Awareness Message (CAM). Each CAM contains the current position of the vehicle based on GPS accuracy, which can have inaccuracies in the meter range. However, a high accuracy of the position data is crucial for many applications, such as electronic toll collection or the reconstruction of traffic accidents.
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