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BackgroundRecent research in machine-learning techniques has led to significant progress in various research fields. In particular, knowledge discovery using this method has become a hot topic in traditional Chinese medicine. As the key clinical manifestations of patients, symptoms play a significant role in clinical diagnosis and treatment, which evidently have their underlying traditional Chinese medicine mechanisms. ObjectiveWe aimed to explore the core symptoms and potential regularity of symptoms for diagnosing insomnia to reveal the key symptoms, hidden relationships underlying the symptoms, and their corresponding syndromes. MethodsAn insomnia dataset with 807 samples was extracted from real-world electronic medical records. After cleaning and selecting the theme data referring to the syndromes and symptoms, the symptom network analysis model was constructed using complex network theory. We used four evaluation metrics of node centrality to discover the core symptom nodes from multiple aspects. To explore the hidden relationships among symptoms, we trained each symptom node in the network to obtain the symptom embedding representation using the Skip-Gram model and node embedding theory. After acquiring the symptom vocabulary in a digital vector format, we calculated the similarities between any two symptom embeddings, and clustered these symptom embeddings into five communities using the spectral clustering algorithm. ResultsThe top five core symptoms of insomnia diagnosis, including difficulty falling asleep, easy to wake up at night, dysphoria and irascibility, forgetful, and spiritlessness and weakness, were identified using evaluation metrics of node centrality. The symptom embeddings with hidden relationships were constructed, which can be considered as the basic dataset for future insomnia research. The symptom network was divided into five communities, and these symptoms were accurately categorized into their corresponding syndromes. ConclusionsThese results highlight that network and clustering analyses can objectively and effectively find the key symptoms and relationships among symptoms. Identification of the symptom distribution and symptom clusters of insomnia further provide valuable guidance for clinical diagnosis and treatment.

In order to study the corrosion behavior of copper-containing 430 stainless steel in normal saline environment, the corrosion resistance of ferritic stainless steel containing Cu 430 in simulated physiological saline solution (0.9% NaCl, mass fraction, the same below) was studied using electrochemical testing methods. The semiconductor properties and surface composition of the passivation film were characterized using Mott-Schottky curves and X-ray photoelectron spectroscopy (XPS), and the surface morphology after electrochemical corrosion was observed using scanning electron microscopy (SEM). Results showed that in 0.9% NaCl solution, passive films were formed on the surface of 430 ferritic stainless steel without Cu and with 1.57% Cu. Compared with stainless steel without Cu, the corrosion resistance of 1.57% Cu stainless steel was reduced. Because Cu element inhibited the formation of Cr2O3 in the passivation film of 430 ferritic stainless steel, the defect density in the passivation film increased. Additionally, the pitting corrosion pits of 430 ferritic stainless steel were mainly distributed near the copper-rich phase after adding the copper element, and the copper-rich phase destroyed the continuity of the passive film, resulting in the decrease of corrosion resistance and the increase of surface morphology damage of stainless steel.

Resistance to care (RTC) is a common abnormal behavioral manifestation of behavioral and psychological symptoms of dementia. Although RTC is strongly associated with agitation or aggressive behavior, it is a specific behavioral response of dementia patients to caregivers, and may result in a negative feedback mechanism as it often directly, negatively impacts caregivers. Currently, there are rare studies on RTC in dementia patients or measuring RTC in these patients using relevant tools in China, which may be because domestic researchers easily confuse RTC with other behavioral concepts. While the concept of RTC was introduced earlier in foreign studies, and has been systematically explored and studied. In this paper, we reviewed available relevant studies, and gave a detailed description of the concept, impact, assessment tools, and interventions regarding RTC, with a view to providing ideas and strategies for the study of RTC in patients with dementia in China.

Recently, two-way or longitudinal functional data analysis has attracted much attention in many fields. However, little is known on how to appropriately characterize the association between two-way functional predictor and scalar response. Motivated by a mortality study, in this paper, we propose a novel two-way functional linear model, where the response is a scalar and functional predictor is two-way trajectory. The model is intuitive, interpretable and naturally captures relationship between each way of two-way functional predictor and scalar-type response. Further, we develop a new estimation method to estimate the regression functions in the framework of weak separability. The main technical tools for the construction of the regression functions are product functional principal component analysis and iterative least square procedure. The solid performance of our method is demonstrated in extensive simulation studies. We also analyze the mortality dataset to illustrate the usefulness of the proposed procedure. [ABSTRACT FROM AUTHOR]