During global pandemics, such as coronavirus disease 2019 (COVID-19), crisis communication is indispensable in dispelling fears, uncertainty, and unifying individuals worldwide in a collective fight against health threats. Inadequate crisis communication can bring dire personal and economic consequences. Mounting research shows that seemingly endless newsfeeds related to COVID-19 infection and death rates could considerably increase the risk of mental health problems. Unfortunately, media reports that include infodemics regarding the influence of COVID-19 on mental health may be a source of the adverse psychological effects on individuals. Owing partially to insufficient crisis communication practices, media and news organizations across the globe have played minimal roles in battling COVID-19 infodemics. Common refrains include raging QAnon conspiracies, a false and misleading “Chinese virus” narrative, and the use of disinfectants to “cure” COVID-19. With the potential to deteriorate mental health, infodemics fueled by a kaleidoscopic range of misinformation can be dangerous. Unfortunately, there is a shortage of research on how to improve crisis communication across media and news organization channels. This paper identifies ways that legacy media reports on COVID-19 and how social media-based infodemics can result in mental health concerns. This paper discusses possible crisis communication solutions that media and news organizations can adopt to mitigate the negative influences of COVID-19 related news on mental health. Emphasizing the need for global media entities to forge a fact-based, person-centered, and collaborative response to COVID-19 reporting, this paper encourages media resources to focus on the core issue of how to slow or stop COVID-19 transmission effectively.

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Large-scale neuroimaging studies have been collecting brain images of study individuals, which take the form of two-dimensional, three-dimensional, or higher dimensional arrays, also known as tensors. Addressing scientific questions arising from such data demands new regression models that take multidimensional arrays as covariates. Simply turning an image array into a long vector causes extremely high dimensionality that compromises classical regression methods, and, more seriously, destroys the inherent spatial structure of array data that possesses wealth of information. In this article, we propose a family of generalized linear tensor regression models based upon the Tucker decomposition of regression coefficient arrays. Effectively exploiting the low rank structure of tensor covariates brings the ultrahigh dimensionality to a manageable level that leads to efficient estimation. We demonstrate, both numerically that the new model could provide a sound recovery of even high rank signals, and asymptotically that the model is consistently estimating the best Tucker structure approximation to the full array model in the sense of Kullback-Liebler distance. The new model is also compared to a recently proposed tensor regression model that relies upon an alternative CANDECOMP/PARAFAC (CP) decomposition.

In this paper, we aim to underscore the need for a more nuanced understanding of vaccine non-adopters. As the availability of vaccines does not translate into their de facto adoption—a phenomenon that may be more pronounced amid “Operation Warp Speed”—it is important for public health professionals to thoroughly understand their “customers” (i.e., end users of COVID-19 vaccines) to ensure satisfactory vaccination rates and to safeguard society at large.

Growth differentiation factor (GDF11) is a member of TGF-β/BMP superfamilythat activates Smad and non-Smad signaling pathways and regulates expression of its target nuclear genes. Since its discovery in 1999, studies have shown the involvement of GDF11 in normal physiological processes, such as embryonic development and erythropoiesis, as well as in the pathophysiology of aging, cardiovascular disease, diabetes mellitus, and cancer. In addition, there are contradictory reports regarding the role of GDF11 in aging, cardiovascular disease, diabetes mellitus, osteogenesis, skeletal muscle development, and neurogenesis. In this review, we describe the GDF11 signaling pathway and its potential role in development, physiology and disease.

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This study examines the main and interactive relations of stressors and social support with Chinese college students' psychological symptoms (e.g., anxiety, depression) during the COVID-19 pandemic. All the constructs are assessed by self-report in an anonymous survey during the pandemic outbreak. The results show that the number of stressors has a positive relation with psychological symptoms, and social support has a negative relation with psychological symptoms. In addition, social support serves as a buffer against the negative impact of stressors. These findings hold implications for university counseling services during times of acute, large-scale stressors. Specifically, effective screening procedures should be developed to identify students who experience large number of stressors and provide suitable psychological intervention for them.

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.