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The ongoing COVID-19 has led to an increase in negative emotions and health awareness among consumers. This paper discusses the emotion bias of Chinese consumers during the three periods: the pre-COVID-19 period, the COVID-19 lockdown period, and the COVID-19 normalization period. This study takes health products as the research object and crawls relevant reviews on the JD platform to classify products. The data were classified into emotion, the intensity of emotion was calculated, and the logistic regression model and variance analysis were used to analyze the difference in emotion expression. The study reveals that consumers are willing to express fear and sadness during the COVID-19 lockdown era and are willing to express like emotions before the pandemic compared to the three periods. There are also differences in the emotional intensity of different product reviews. The intensity of emotional expression is more vigorous for consumers who purchase nutritional products, while for those who purchase healthcare equipment, the intensity of emotional expression is lower. This study offers the emotion bias of consumers in response to COVID-19 to provide a theoretical basis and reference solution for implementing marketing strategies for health product companies. [ABSTRACT FROM AUTHOR]

As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce consumers. Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics. And then use our proposed model to make predictions. Experiments show that the model's overall performance with increased interactive behavior features is better and has higher accuracy. Compared with the existing prediction models, the improved deep forest model has certain advantages, which not only improves the prediction accuracy but also reduces the cost of training time. [ABSTRACT FROM AUTHOR]

We have previously reported that L-arginine, a nitric oxide synthase substrate, inhibits the basolateral 10-pS Cl- channel through the cGMP/PKG signaling pathway in the thick ascending limb (TAL). As a NO releasing agent, the effect of S-nitroso-N-acetyl-penicillamine (SNAP) on the channel activity was examined in thick ascending limb of C57BL/6 mice in the present study. SNAP inhibited the basolateral 10-pS Cl- channel in a dose-dependent manner with an IC50 value of 6.6 μM. The inhibitory effect of SNAP was abolished not only by NO scavenger (carboxy-PTIO) but also by blockers of soluble guanylate cyclase (ODQ or LY-83583), indicating that the cGMP-dependent signaling pathway is involved. Moreover, the inhibitory effect of SNAP on the channel was strongly attenuated by a protein kinase G (PKG)-specific inhibitor, KT-5823, but not by the PDE2 inhibitor, BAY-60-7550. We concluded that SNAP inhibited the basolateral 10-pS Cl- channels in the TAL through a cGMP/PKG signaling pathway. As the 10-pS Cl- channel is important for regulation of NaCl absorption along the nephron, these data suggest that SNAP might be served as a regulator to prevent high-salt absorption related diseases, such as hypertension. [ABSTRACT FROM AUTHOR]