β-Eudesmol is sesquiterpenoid alcohol which contains the rhizome of Atractylodes lancea. Although it has multiple pharmacological effects, the anti-inflammatory effect of β-eudesmol and its molecular mechanisms are poorly elucidated. In this study, we investigated the regulatory mechanism of β-eudesmol on mast cell-mediated inflammatory response. The results indicated that β-eudesmol inhibited the production and expression of interleukin (IL)-6 on phorbol 12-myristate 13-acetate and calcium ionophore A23187-stimulated human mast cell (HMC). In activated HMC-1 cells, β-eudesmol suppressed activation of p38 mitogen-activated protein kinase (MAPKs) and nuclear factor-κB. In addition, β-eudesmol suppressed the activation of caspase-1 and expression of receptor-interacting protein-2. These results provide new insights into the pharmacological actions of β-eudesmol as a potential molecule for use in therapy in mast cell-mediated inflammatory diseases.

A rising number of authors are drawing evidence on the diagnostic capacity of specific volatile organic compounds (VOCs) resulting from some body fluids. While cancer incidence in society is on the rise, it becomes clear that the analysis of these VOCs can yield new strategies to mitigate advanced cancer incidence rates. This paper presents the methodology implemented to test whether a device consisting of an electronic nose inspired by a dog’s olfactory system and olfactory neurons is significantly informative to detect breast cancer (BC). To test this device, 90 human urine samples were collected from control subjects and BC patients at a hospital. To test this system, an artificial intelligence-based classification algorithm was developed. The algorithm was firstly trained and tested with data resulting from gas chromatography-mass spectrometry (GC–MS) urine readings, leading to a classification rate of 92.31%, sensitivity of 100.00%, and specificity of 85.71% (N = 90). Secondly, the same algorithm was trained and tested with data obtained with our eNose prototype hardware, and class prediction was achieved with a classification rate of 75%, sensitivity of 100%, and specificity of 50%.

Accounting for all operating conditions of a system at the design stage is typically infeasible for complex systems. Monitoring and verifying system requirements at runtime enable a system to continuously and introspectively ensure the system is operating correctly in the presence of dynamic execution scenarios. In this article, we present a requirements-driven methodology enabling efficient runtime monitoring of embedded systems. The proposed approach extracts a runtime monitoring graph from system requirements specified using UML sequence diagrams. Non-intrusive, on-chip hardware dynamically monitors the system execution, verifies the execution adheres to the requirements model, and in the event of a failure provides detailed information that can be analyzed to determine the root cause. Using case studies of an autonomous vehicle and pacemaker prototypes, we analyze the relationship between event coverage, detection rate, and hardware requirements

Tracing or trace interface has been used in various ways to find system defects or bugs. As embedded systems are increasingly used in safety-critical applications, tracing can provide useful information during system execution at runtime. Non-intrusive tracing that does not affect system performance has become especially important, but unfortunately, the biggest obstacle to this approach was the vast amount of real-time trace data, making it challenging to address complex requirements with relatively limited hardware implementations. Automata processors can be programmed with a memory-like structure of automata and have a structure specific to streaming data, large capacity, and parallel processing functions. This paper promotes the idea of high-level system-on-chip monitoring using automata processors. We used a safety-critical pacemaker application in the experiments, described timed automata (TA)-based requirements, and tested intentionally injected 4,000 random failures. The TA model converted for Automata Processor to monitor system, correctness, and safety properties achieved 100% failure detection rate in the experiment, and the detected failure is reported as fast enough to allow enough extent for failure recovery.

These results suggest that OH reduces the severity of AP by inhibiting acinar cell death through c-Jun N-terminal kinases.