Research on mobile impulse purchase intention in the perspective of system users during COVID-19.

COVID-19 has caused a serious impact on the global economy. Effectively stimulating consumption has become a momentous mission in responding to the impact of the epidemic. The popularity of mobile shopping makes shopping behavior no longer limited by time and space, so impulse purchase is more commonly seen nowadays; it can effectively promote residents' consumption. However, consensus has not been reached regarding how impulse purchase emerges as a phenomenon, thus making it difficult to promote consumers' purchase behavior. This article aims to explore the generation process of consumers' impulsive purchase intention during the COVID-19 outbreak from the perspective of system users. For this purpose, the research proposes three mobile situation factors: personalized recommendation, visual appeal, and system usability. They have a positive impact on impulse purchase intention by influencing perceived arousal and perceived enjoyment. The experimental method is used for data collection and hypothesis testing. All the hypotheses are supported. And the theoretical value of the model of "mobile environment stimulation-consumer emotion-impulse purchase intention" is confirmed. Based on the conclusion, management suggestions are proposed for mobile shopping merchants from the perspective of improving consumers' shopping experience and expanding marketing.

Impact of campus living conditions on Chinese medical school students' mental health during the COVID-19 campus lockdown: the chain mediating role of cognitive reappraisal and expression suppression.

Objective: To investigate the effect of changes in campus living conditions related to the Corona Virus Disease 2019 (COVID-19) pandemic on medical school students' mental health status, to explore the mediating role of emotion regulation strategies, and to provide effective suggestions for promoting medical school students' mental health. Methods: A self-report questionnaire, an emotion regulation questionnaire (ERQ), and psychological questionnaires for emergent events of public health (PQEEPH) were used to interview 998 medical school students who experienced campus lockdowns during the COVID-19 pandemic. Results: The mean total PQEEPH score was 3.66 ± 3.06. The degrees of inconvenience in daily life and change in routine and expression suppression as an emotion regulation strategy were significantly positively correlated with all PQEEPH dimensions. Cognitive reappraisal was significantly negatively associated with depression, neurosis, obsessive-compulsive anxiety, and hypochondriasis (ps < 0.05). Cognitive reappraisal and expression suppression demonstrated a chain mediating role between the degree of inconvenience in life and mental health and between the degree of change in routine and mental health (F = 32.883, 41.051, ps < 0.05). Conclusion: Campus lockdown management significantly impacts medical school students' mental health. Extensive use of cognitive reappraisal and expression suppression can reduce students' adverse psychological reactions during campus lockdowns to an extent.

A Face Detection and Standardized Mask-Wearing Recognition Algorithm.

In the era of coronavirus disease (COVID-19), wearing a mask could effectively protect people from the risk of infection and largely reduce transmission in public places. To prevent the spread of the virus, instruments are needed in public places to monitor whether people are wearing masks, which has higher requirements for the accuracy and speed of detection algorithms. To meet the demand for high accuracy and real-time monitoring, we propose a single-stage approach based on YOLOv4 to identify the face and whether to regulate the wearing of masks. In this approach, we propose a new feature pyramidal network based on the attention mechanism to reduce the loss of object information that can be caused by sampling and pooling in convolutional neural networks. The network is able to deeply mine the feature map for spatial and communication factors, and the multi-scale feature fusion makes the feature map equipped with location and semantic information. Based on the complete intersection over union (CIoU), a penalty function based on the norm is proposed to improve positioning accuracy, which is more accurate at the detection of small objects; the new bounding box regression function is called Norm CIoU (NCIoU). This function is applicable to various object-detection bounding box regression tasks. A combination of the two functions to calculate the confidence loss is used to mitigate the problem of the algorithm bias towards determinating no objects in the image. Moreover, we provide a dataset for recognizing faces and masks (RFM) that includes 12,133 realistic images. The dataset contains three categories: face, standardized mask and non-standardized mask. Experiments conducted on the dataset demonstrate that the proposed approach achieves mAP@.5:.95 69.70% and AP75 73.80%, outperforming the compared methods.

A novel S2-derived peptide-based ELISA for broad detection of antibody against infectious bronchitis virus.

Avian infectious bronchitis (IB) is a highly contagious disease caused by infectious bronchitis virus (IBV). Vaccination is an effective approach for controlling IBV. Therefore, reliable immune monitoring for IB is critical for poultry. In this study, a novel peptide derived from S2 protein was used to develop an enzyme-linked immunosorbent assay (ELISA) for the detection of broadly cross-reactive antibodies against IBV. The peptide-based ELISA (pELISA) showed good specificity and sensitivity in detecting IBV antibodies against different serotypes. A semilogarithmic regression method for determining IBV antibody titers was also established. Antibody titers detected by pELISA and calculated with this equation were statistically similar to those evaluated by indirect fluorescence assay (IFA). Moreover, the comparison analysis showed a 96.07% compatibility between the pELISA and IDEXX ELISA. All these data demonstrate that the pELISA generated here can be as a rapid and reliable serological surveillance tool for monitoring IBV infection or vaccination.

SARS-Cov-2 Spike-S1 Antigen Test Strip with High Sensitivity Endowed by High-Affinity Antibodies and Brightly Fluorescent QDs/Silica Nanospheres.

The extensive research into developing novel strategies for detecting respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens in clinical specimens, especially the sensitive point-of-care testing method, is still urgently needed to reach rapid screening of viral infections. Herein, a new lateral flow immunoassay (LFIA) platform was reported for the detection of SARS-CoV-2 spike-S1 protein antigens, in which four sensitive and specific SARS-CoV-2 mouse monoclonal antibodies (MmAbs) were tailored by using quantum dot (QD)-loaded dendritic mesoporous silica nanoparticles modified further for achieving the -COOH group surface coating (named Q/S-COOH nanospheres). Importantly, compact QD adsorption was achieved in mesoporous channels of silica nanoparticles on account of highly accessible central-radial pores and electrostatic interactions, leading to significant signal amplification. As such, a limit of detection for SARS-CoV-2 spike-S1 testing was found to be 0.03 ng/mL, which is lower compared with those of AuNPs-LFIA (traditional colloidal gold nanoparticles, Au NPs) and enzyme-linked immunosorbent assay methods. These results show that optimizing the affinity of antibody and the intensity of fluorescent nanospheres simultaneously is of great significance to improve the sensitivity of LFIA.

Nirmatrelvir/Ritonavir for hemodialysis patients with COVID-19.

Background: Hemodialysis patients have a high risk of severe/critical COVID-19 and related high mortality, but nirmatrelvir/ritonavir is not recommended for hemodialysis patients with COVID-19 infection because of lack of evidence of safety. Objectives: Our study aims to evaluate the minimum plasma concentration (Cmin) of nirmatrelvir and its safety of different doses of nirmatrelvir/ritonavir in hemodialysis patients with mild COVID-19. Method: This was a prospective, two step, nonrandomized, open-label study. Participants were treated with nirmatrelvir 150 mg or 300 mg once a day (another 75 mg or 150 mg supplied after hemodialysis) and ritonavir 100 mg twice daily for 5 days, respectively. The primary outcome was the safety of nirmatrelvir/ritonavir, including the Cmin of nirmatrelvir and the number of adverse events (AE). The secondary outcome was the time of viral elimination in hemodialysis patients. Results: Adverse events were happened in 3 and 7 participants in the step 1 and step 2 group, respectively (p = 0.025). Among them, 2 and 6 participants were identified as drug-related adverse events (p = 0.054). No SAE or liver function damage happened. The Cmin of nirmatrelvir in step 1 and step 2 group were 5,294.65 ± 2,370.59 ng/mL and 7,675.67 ± 2,745.22 ng/mL (p = 0.125). The Cmin of the control group was 2,274.10 ± 1,347.25 ng/mL (p = 0.001 compared to step 2 and p = 0.059 compared to step 1). Compared to hemodialysis patients without nirmatrelvir/ritonavir, there were no statistical differences in overall viral elimination time (p = 0.232). Conclusion: In our study, two doses of nirmatrelvir/ritonavir appeared to be excessive for hemodialysis patients. Although all of the patients tolerated 5-day administration, nearly half of the patients experienced drug-related adverse events. In addition, the medication group did not show a significant advantage in the time of viral elimination.

Four new eudesmane-type and one new eremophilane-type sesquiterpenes from the whole plant of Carpesium abrotanoides L.

The extract of the whole plant of Carpesium abrotanoides L. yielded five new sesquiterpenes including four eudesmanes (1-4) and one eremophilane (5). The new compounds were characterized by spectroscopic analysis especially 1D and 2D NMR spectroscopy and HRESIMS data. Structurally, both compounds 1 and 2 were sesquiterpene epoxides and 2 owned an epoxy group at C-4/C-15 position to form a spiro skeleton. Compounds 4 and 5 were two sesquiterpenes without lactones and 5 possessed a carboxy group in the molecule. Additionally, all the isolated compounds were preliminarily evaluated for the inhibitory activity against SARS-CoV-2 main protease. As a result, compound 2 showed moderate activity with an IC50 value of 18.79 µM, while other compounds were devoid of noticeable activity (IC50 > 50 µM).

Case report: Severe pneumonia and pneumomediastinum in a previously robust adolescent caused by Omicron BA.5.2.

The manifestation of severe pneumonia is only occasional, and pneumomediastinum is a condition that occurs rarely in Coronavirus disease 2019 (COVID-19) patients, especially in those patients who are infected with the Omicron variant. In addition, whether severe pneumonia or pneumomediastinum often occurs in patients in older age, in poor physical condition, or with underlying diseases remains to be ascertained. To date, severe pneumonia and pneumomediastinum due to Omicron infection had not been reported in a young patient with an excellent physical condition. In this study, we report such a case with the aforementioned manifestations in a robust adolescent infected with Omicron BA.5.2.

Quantitative annotations of T-Cell repertoire specificity.

The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modeled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddressed. We developed a package, TCRanno, to quantify the specificity of TCR repertoires. We created deep-learning-based, epitope-aware vector embeddings to infer individual TCR specificity. Then we aggregated clonotype frequencies of TCRs to obtain a quantitative profile of repertoire specificity at epitope, antigen and organism levels. Applying TCRanno to 4195 TCR repertoires revealed quantitative changes in repertoire specificity upon infections, autoimmunity and cancers. Specifically, TCRanno found cytomegalovirus-specific TCRs in seronegative healthy individuals, supporting the possibility of abortive infections. TCRanno discovered age-accumulated fraction of severe acute respiratory syndrome coronavirus 2 specific TCRs in pre-pandemic samples, which may explain the aggressive symptoms and age-related severity of coronavirus disease 2019. TCRanno also identified the encounter of Hepatitis B antigens as a potential trigger of systemic lupus erythematosus. TCRanno annotations showed capability in distinguishing TCR repertoires of healthy and cancers including melanoma, lung and breast cancers. TCRanno also demonstrated usefulness to single-cell TCRseq+gene expression data analyses by isolating T-cells with the specificity of interest.

Continuation of telework in the post-pandemic era: Healthcare employees' preference and determinants.

Now in the post-pandemic era, healthcare employers and leaders must navigate decisions around use of telework arrangements made popular during the COVID-19 pandemic. Among healthcare employees who teleworked during the pandemic, this study investigates preference to continue teleworking post-pandemic and the determinants of this preference. An overwhelming majority (99%) preferred to continue teleworking to some degree and the majority (52%) preferred to telework for all work hours. Healthcare employers should consider that most employees who teleworked during the pandemic prefer to continue teleworking for most or all work hours, and that hybrid work arrangements are especially important for clinical telework employees. In addition to space and resource allocation, management considerations include supports to promote productivity, work-life balance, and effective virtual communication while teleworking to promote positive employee health, recruitment, and retention outcomes.

Sparse and risk diversification portfolio selection.

Portfolio risk management has become more important since some unpredictable factors, such as the 2008 financial crisis and the recent COVID-19 crisis. Although the risk can be actively managed by risk diversification, the high transaction cost and managerial concerns ensue by over diversifying portfolio risk. In this paper, we jointly integrate risk diversification and sparse asset selection into mean-variance portfolio framework, and propose an optimal portfolio selection model labeled as JMV. The weighted piecewise quadratic approximation is considered as a penalty promoting sparsity for the asset selection. The variance associated with the marginal risk regard as another penalty term to diversify the risk. By exposing the feature of JMV, we prove that the KKT point of JMV is the local minimizer if the regularization parameter satisfies a mild condition. To solve this model, we introduce the accelerated proximal gradient (APG) algorithm [Wen in SIAM J. Optim 27:124-145, 2017], which is one of the most efficient first-order large-scale algorithm. Meanwhile, the APG algorithm is linearly convergent to a local minimizer of the JMV model. Furthermore, empirical analysis consistently demonstrate the theoretical results and the superiority of the JMV model.

Culture Change in Long-Term Care-Post COVID-19: Adapting to a New Reality Using Established Ideas and Systems.

The response to the COVID-19 pandemic in long-term care (LTC) has threatened to undo efforts to transform the culture of care from institutionalized to de-institutionalized models characterized by an orientation towards person- and relationship-centred care. Given the pandemic's persistence, the sustainability of culture-change efforts has come under scrutiny. Drawing on seven culture-change models implemented in Canada, we identify organizational prerequisites, facilitatory mechanisms, and frontline changes relevant to culture change that can strengthen the COVID-19 pandemic response in LTC homes. We contend that a reversal to institutionalized care models to achieve public health goals of limiting COVID-19 and other infectious disease outbreaks is detrimental to LTC residents, their families, and staff. Culture change and infection control need not be antithetical. Both strategies share common goals and approaches that can be integrated as LTC practitioners consider ongoing interventions to improve residents' quality of life, while ensuring the well-being of staff and residents' families.

Cognitive appraisal of health and its influencing factors among pregnant women with gestational diabetes mellitus: A cross-sectional study.

AIM: To investigate the status quo of cognitive appraisal of health and its influencing factors among pregnant women with gestational diabetes mellitus. METHODS: A cross-sectional survey was conducted from June 2020 to November 2020. Participants were recruited from a tertiary hospital by a convenient sample method. A total of 300 pregnant women with gestational diabetes mellitus completed the survey, including self-compiled individual information questionnaire, Cognitive Appraisal of Health Scale, Pregnancy Stress Rating Scale and General Self-Efficacy Scale. RESULTS: For cognitive appraisal of health, the median score of challenge dimension was 3.75 (3.50, 4.00), benign/irrelevant was 2.75 (2.00, 3.50), harm/loss was 2.38 (2.00, 3.00) and threat was 2.40 (2.00, 2.80), respectively. Regression analyses showed that gestational age, mode of conception, history of abortion, insulin usage, pregnancy stress and self-efficacy were the predictors of cognitive appraisal of health. CONCLUSIONS: This study revealed that pregnant women with gestational diabetes mellitus tended to make positive cognitive appraisal of health. And healthcare providers need to make full use of their predictors of cognitive appraisal of health to improve cognitive appraisal to manage stress and ameliorate pregnancy outcomes.

Clinical characteristics and remission of nine cases with coronavirus disease 2019 infection in Zunyi, Southwest of China: A retrospective study.

The outbreak of coronavirus disease 2019 (COVID-19) has become a rock-ribbed public pandemic and caused substantial health concerns worldwide. In addition to therapeutic strategies, the epidemiologic features and clinical characteristics of patients responded to COVID-19 infection are of equal importance. The study aims to systematically evaluate the clinical presentations and remission of cases with COVID-19 infection in Zunyi, Southwest of China, and to determine the similarities and variations for further clinical classification and comprehensive treatment. Herein, we conducted a retrospective study upon 9 patients in Zunyi, southwest of China, including 1 mild (LPA), 5 severe (SPA) and 3 critical (CPA) types of COVID-19 infection. In details, the demographic data, historical epidemiology, previous medical history, clinical symptoms and complications, laboratory examination, chest imaging, treatment and outcomes of the patients were throughout explored. The non-normal distribution of the data was conducted by utilizing the SPSS software, and significant statistical differences were identified when P < .05. By retrospective analysis of the 9 cases, we found there were multifaceted similarities and differences among them in clinical representation. The patients collectively showed negative for nucleic acid test (NAT) and favorable prognosis after receiving comprehensive therapy such as hormonotherapy, hemopruification, and antiviral administration as well as respiratory support. On the basis of the information, we systematically dissected the clinical features and outcomes of the enrolled patients with COVID-19 and the accompanied multiple syndromes, which would serve as new references for clinical classification and comprehensive treatment. Analysis of clinical characteristics and therapeutic effect of 9 cases of novel coronavirus pneumonia (COVID-19), ChiCTR2000031930. Registered April 15, 2020 (retrospective registration).

Research on the evolution of the Chinese urban biomedicine innovation network pattern: An analysis using multispatial scales.

This paper addresses the spatial pattern of urban biomedicine innovation networks by separately using four scales, i.e., the national scale, interregional scale, urban agglomeration scale, and provincial scale, on the basis of Chinese biomedicine patent data from the incoPat global patent database (GPD) (2001-2020) and using the method of social network analysis (SNA). Through the research, it is found that (1) on the national scale, the Chinese biomedicine innovation network becomes denser from west to the east as its complexity continuously increases. Its spatial structure takes the form of a radial network pattern with Beijing and Shanghai as its centers. The COVID-19 pandemic has not had an obvious negative impact on this network at present. (2) On the interregional scale, the strength of interregional network ties is greater than that of intraregional network ties. The eastern, central and western biomedicine innovation networks appear to be heterogeneous networks with regional central cities as the cores. (3) At the urban agglomeration scale, the strength of intraurban-agglomeration network ties is greater than that of interurban-agglomeration network ties. The three major urban agglomerations have formed radial spatial patterns with central cities as the hubs. (4) At the provincial scale, the intraprovincial networks have poor connectivity and low internal ties strength, which manifest as core-periphery structures with the provincial capitals as centers. Our research conclusion helps to clarify the current accumulation of technology and offer guidance for the development of China's biomedicine industry.

Relationship between Risk Perception, Emotion, and Coping Behavior during Public Health Emergencies: A Systematic Review and Meta-Analysis

Complex mechanisms exist between public risk perception, emotions, and coping behaviors during health emergencies. To unravel the relationship between these three phenomena, a meta-analytic approach was employed in this study. Using Comprehensive Meta-Analysis 3.0, 81 papers were analyzed after selection. The results of the meta-analysis showed that (1) risk perception (perceived severity, perceived susceptibility) and negative emotions (especially fear) are both correlated with coping behaviors;(2) risk perception is strongly correlated with fear and moderately correlated with anxiety;and (3) anxiety predicts the adoption of coping behaviors. The existing research provided an empirical basis for implementing effective coping behavior interventions and implied that management decisionmakers need to consider reasonable interventions through multiple channels to maintain the public's risk perception and emotions within appropriate levels. Finally, future research directions are suggested.

GSAL: Geometric structure adversarial learning for robust medical image segmentation

• A new learning mechanism for medical image segmentation. We introduce a novel Geometric Structure Learning Mechanism (GSLM) that enhances model learning "focus, path, and difficulty". It enables geometric structure attention learning to bridge image features with large differences, thus capturing the contextual dependencies of images. The image features maintain consistency and continuity along the internal and external geometry structure, which improves the integrity and boundary accuracy of the segmentation results. To the best of our knowledge, we are the first attempt to explicitly establish the target's geometric structure, which has been successfully applied to medical image segmentation. • A novel geometric structure adversarial learning for robust medical image segmentation. We present the geometric structure adversarial learning model (GSAL) that consists of a geometric structure generator, skeleton-like and boundary discriminators, and a geometric structure fusion sub-network. The generator yields the geometric structure that preserves interior characteristics consistency and external boundary structure continuity. The dual discriminators are trained simultaneously to enhance and correct the characterization of interior structure and boundary structure, respectively. The fusion sub-network aims to fuse the geometric structure that optimized by adversarial learning to refine the final segmentation results with higher credibility. • State-of-art results on widely-used benchmarks. Our GSAL achieves SOTA performance on a variety of benchmarks, including Kvasir&CVC-612 dataset, COVID-19 dataset, and LIDC-IDRI dataset. It confirms the robustness and generalizability of our framework. In addition, our method has great advantages in terms of the integrity and boundary accuracy of the segmentation target compared to other competitive methods. GSAL can also achieve a considerable trade-off in terms of accuracy, inference speed, and model complexity, which helps deploy in clinical practice systems. Automatic medical image segmentation plays a crucial role in clinical diagnosis and treatment. However, it is still a challenging task due to the complex interior characteristics (e.g. , inconsistent intensity, low contrast, texture heterogeneity) and ambiguous external boundary structures. In this paper, we introduce a novel geometric structure learning mechanism (GSLM) to overcome the limitations of existing segmentation models that lack learning "focus, path, and difficulty." The geometric structure in this mechanism is jointly characterized by the skeleton-like structure extracted by the mask distance transform (MDT) and the boundary structure extracted by the mask distance inverse transform (MDIT). Among them, the skeleton-like and boundary pay attention to the trend of interior characteristics consistency and external structure continuity, respectively. With this idea, we design GSAL, a novel end-to-end geometric structure adversarial learning for robust medical image segmentation. GSAL has four components: a geometric structure generator, which yields the geometric structure to learn the most discriminative features that preserve interior characteristics consistency and external boundary structure continuity, skeleton-like and boundary structure discriminators, which enhance and correct the characterization of internal and external geometry to mutually promote the capture of global contextual dependencies, and a geometric structure fusion sub-network, which fuses the two complementary and refined skeleton-like and boundary structures to generate the high-quality segmentation results. The proposed approach has been successfully applied to three different challenging medical image segmentation tasks, including polyp segmentation, COVID-19 lung infection segmentation, and lung nodule segmentation. Extensive experimental results demonstrate that the proposed GSAL achieves favorably against most state-of-the-art methods under different evaluation metrics. The code is available at: https://github.com/DLWK/GSAL. [ BSTRACT FROM AUTHOR] Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Awake Prone Positioning in Non-Intubated Patients With Acute Hypoxemic Respiratory Failure Due to COVID-19.

BACKGROUND: Awake prone positioning (APP) has been advocated to improve oxygenation and prevent intubation of patients with acute hypoxemic respiratory failure due to coronavirus disease 2019 (COVID-19). This paper aims to synthesize the available evidence on the efficacy of APP. METHODS: We performed a systematic review of proportional outcomes from observational studies to compare intubation rate in patients treated with APP or with standard care. RESULTS: A total of 46 published and 4 unpublished observational studies that included 2,994 subjects were included, of which 921 were managed with APP and 870 were managed with usual care. APP was associated with significant improvement of oxygenation parameters in 381 cases of 19 studies that reported this outcome. Among the 41 studies assessing intubation rates (870 subjects treated with APP and 852 subjects treated with usual care), the intubation rate was 27% (95% CI 19-37%) as compared to 30% (95% CI 20-42%) (P = .71), even when duration of application, use of adjunctive respiratory assist device (high-flow nasal cannula or noninvasive ventilation), and severity of oxygenation deficit were taken into account. There appeared to be a trend toward improved mortality when APP was compared with usual care (11% vs 22%), which was not statistically significant. CONCLUSIONS: APP was associated with improvement of oxygenation but did not reduce the intubation rate in subjects with acute respiratory failure due to COVID-19. This finding is limited by the high heterogeneity and the observational nature of included studies. Randomized controlled clinical studies are needed to definitively assess whether APP could improve key outcome such as intubation rate and mortality in these patients.

The potential of Panax notoginseng against COVID-19 infection.

The COVID-19 pandemic has changed the world and has presented the scientific community with unprecedented challenges. Infection is associated with overproduction of proinflammatory cytokines secondary to hyperactivation of the innate immune response, inducing a cytokine storm and triggering multiorgan failure and significant morbidity/mortality. No specific treatment is yet available. For thousands of years, Panax notoginseng has been used to treat various infectious diseases. Experimental evidence of P. notoginseng utility in terms of alleviating the cytokine storm, especially the cascade, and improving post-COVID-19 symptoms, suggests that P. notoginseng may serve as a valuable adjunct treatment for COVID-19 infection.