Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care.

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.

Vaccaria hypaphorine impairs RANKL-induced osteoclastogenesis by inhibition of ERK, p38, JNK and NF-κB pathway and prevents inflammatory bone loss in mice.

Osteoclasts are sole bone-resorbing cells which exert a profound effect on skeletal metabolism. The search for medicines that affect the differentiation and function of osteoclasts is crucial in developing therapies for osteoclast-based diseases. Vaccaria hypaphorine, the main active compound of the traditionally used Chinese herb Vaccaria segetalis, has anti-inflammatory activity. The present study demonstrated for the first time that vaccaria hypaphorine could significantly inhibit the receptor activator of nuclear factor kappa B ligand (RANKL)-induced osteoclastic differentiation in vitro and alleviate lipopolysaccharide (LPS)-induced bone loss in vivo. Further study showed that vaccaria hypaphorine decreased osteoclastogenesis in a dose-dependent manner. Furthermore, vaccaria hypaphorine was confirmed to inhibit osteoclasts differentiation at early stage but not at later stage. Pit formation assay and F-actin ring staining showed that vaccaria hypaphorine inhibited the bone-resorbing activity of osteoclasts. Mechanistically, vaccaria hypaphorine impaired RANKL-induced osteoclastogenesis through reduction of extracellular signal-regulated kinases (ERK), p38, c-Jun N-terminal kinase (JNK) and NF-κB p65 phosphorylation. Taken together, our results provided evidences that vaccaria hypaphorine might be considered as potential therapeutic agent for treating osteoclast-based bone loss.