How negative anthropomorphic message framing and nostalgia enhance pro-environmental behaviors during the COVID-19 pandemic in China: An SEM-NCA approach.

Although extensive research has been conducted on promoting pro-environmental behaviors among consumers, little is known about whether and how negative anthropomorphic message framing (NAMF) and nostalgia affect pro-environmental behavior. To provide a framework for explaining pro-environmental behavior, this study integrates protection motivation theory, the stimulus-organism-response model, and message framing. To create the model of the influences on pro-environmental behavior, NAMF was employed as the external stimulus; the sense of environmental responsibility, environmental empathy, perceived threat, and perceived vulnerability as the psychological and cognitive response factors; pro-environmental behavior as the final decision of consumers; and nostalgia as the moderating variable. An online questionnaire was distributed and 380 usable questionnaires were collected using convenience sampling and analyzed using two complementary approaches: partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA). PLS-SEM results showed that pro-environmental behavior was significantly affected by NAMF (ß = 0.313, t-value = 5.583), environmental responsibility (ß = 0.207, t-value = 3.994), and perceived threats (ß = 0.252, t-value = 4.889). Meanwhile, an increase in nostalgia increased the effect of NAMF and environmental responsibility on pro-environmental behavior. The NCA results revealed that NAMF (d = 0.108, p < 0.001) and perceived threat (d = 0.209, p < 0.001) were key factors of pro-environmental behavior. In addition, for high level of pro-environmental behavior (>80%), NAMF (12.1%) and perceived threat (39.6%) are required. Finally, we offer several suggestions based on the results of our empirical research. For example, marketing and service offerings should be tailored to the needs of masses with different nostalgic tendencies to enhance their pro-environmental behaviors.

Toll-Like Receptor Signaling in Severe Acute Respiratory Syndrome Coronavirus 2-Induced Innate Immune Responses and the Potential Application Value of Toll-Like Receptor Immunomodulators in Patients With Coronavirus Disease 2019.

Toll-like receptors (TLRs) are key sensors that recognize the pathogen-associated molecular patterns (PAMPs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to activate innate immune response to clear the invading virus. However, dysregulated immune responses may elicit the overproduction of proinflammatory cytokines and chemokines, resulting in the enhancement of immune-mediated pathology. Therefore, a proper understanding of the interaction between SARS-CoV-2 and TLR-induced immune responses is very important for the development of effective preventive and therapeutic strategies. In this review, we discuss the recognition of SARS-CoV-2 components by TLRs and the downstream signaling pathways that are activated, as well as the dual role of TLRs in regulating antiviral effects and excessive inflammatory responses in patients with coronavirus disease 2019 (COVID-19). In addition, this article describes recent progress in the development of TLR immunomodulators including the agonists and antagonists, as vaccine adjuvants or agents used to treat hyperinflammatory responses during SARS-CoV-2 infection.

Query bot for retrieving patients' clinical history: A COVID-19 use-case.

OBJECTIVE: With increasing patient complexity whose data are stored in fragmented health information systems, automated and time-efficient ways of gathering important information from the patients' medical history are needed for effective clinical decision making. Using COVID-19 as a case study, we developed a query-bot information retrieval system with user-feedback to allow clinicians to ask natural questions to retrieve data from patient notes. MATERIALS AND METHODS: We applied clinicalBERT, a pre-trained contextual language model, to our dataset of patient notes to obtain sentence embeddings, using K-Means to reduce computation time for real-time interaction. Rocchio algorithm was then employed to incorporate user-feedback and improve retrieval performance. RESULTS: In an iterative feedback loop experiment, MAP for final iteration was 0.93/0.94 as compared to initial MAP of 0.66/0.52 for generic and 1./1. compared to 0.79/0.83 for COVID-19 specific queries confirming that contextual model handles the ambiguity in natural language queries and feedback helps to improve retrieval performance. User-in-loop experiment also outperformed the automated pseudo relevance feedback method. Moreover, the null hypothesis which assumes identical precision between initial retrieval and relevance feedback was rejected with high statistical significance (p â‰ª 0.05). Compared to Word2Vec, TF-IDF and bioBERT models, clinicalBERT works optimally considering the balance between response precision and user-feedback. DISCUSSION: Our model works well for generic as well as COVID-19 specific queries. However, some generic queries are not answered as well as others because clustering reduces query performance and vague relations between queries and sentences are considered non-relevant. We also tested our model for queries with the same meaning but different expressions and demonstrated that these query variations yielded similar performance after incorporation of user-feedback. CONCLUSION: In conclusion, we develop an NLP-based query-bot that handles synonyms and natural language ambiguity in order to retrieve relevant information from the patient chart. User-feedback is critical to improve model performance.