Customer's Adoption Intentions toward Autonomous Delivery Vehicle Services: Extending DOI Theory with Social Awkwardness and Use Experience

The high demand and acute timeliness that characterizes instant delivery entail the challenges of high labor costs and an increase in courier traffic accidents. Autonomous delivery vehicles (ADVs) may serve as a key solution, with their attendant reduced labor input and higher efficiency. Customers play a key role in the successful implementation of ADVs on a large scale. However, understanding the factors that affect customers' intentions to use ADVs is still limited. Compared to autonomous driving, ADV customers are ultimately not the real users, who only are served by ADVs during the last leg of a trip. On account of this, the Technology Acceptance Model (TAM) may not be well-fitted for explaining the dynamics involved in ADV adoption. Within the context of ADVs, our study identified influencing factors that have not been captured by prior studies. This study incorporates infection risk, use experience, and social awkwardness into the Diffusion of Innovation (DOI) theory to explore customers' intentions to use ADVs. Data from 691 survey respondents were collected to validate the research design. The results demonstrate that compatibility, social influence, infection risk, green image, social awkwardness, and use experience all have a significantly positive impact on customers' intentions to adopt ADV services, while complexity and perceived risk both exhibited a negative impact. But no effect could be found for relative advantage, which may be because of the fact that customers only need ADVs to meet their delivery demand. This study contributes to understanding customers' adoption intentions toward ADVs, informing policymakers in formulating ADV regulations and standards, and promoting the large-scale application of ADVs in instant delivery services.

Generative face inpainting hashing for occluded face retrieval.

COVID-19 has resulted in a significant impact on individual lives, bringing a unique challenge for face retrieval under occlusion. In this paper, an occluded face retrieval method which consists of generator, discriminator, and deep hashing retrieval network is proposed for face retrieval in a large-scale face image dataset under variety of occlusion situations. In the proposed method, occluded face images are firstly reconstructed using a face inpainting model, in which the adversarial loss, reconstruction loss and hash bits loss are combined for training. With the trained model, hash codes of real face images and corresponding reconstructed face images are aimed to be as similar as possible. Then, a deep hashing retrieval network is used to generate compact similarity-preserving hashing codes using reconstructed face images for a better retrieval performance. Experimental results show that the proposed method can successfully generate the reconstructed face images under occlusion. Meanwhile, the proposed deep hashing retrieval network achieves better retrieval performance for occluded face retrieval than existing state-of-the-art deep hashing retrieval methods.

GPR183 antagonism reduces macrophage infiltration in influenza and SARS-CoV-2 infection.

RATIONALE: Severe viral respiratory infections are often characterised by extensive myeloid cell infiltration and activation and persistent lung tissue injury. However, the immunological mechanisms driving excessive inflammation in the lung remain poorly understood. OBJECTIVES: To identify the mechanisms that drive immune cell recruitment in the lung during viral respiratory infections and identify novel drug targets to reduce inflammation and disease severity. METHODS: Preclinical murine models of influenza virus and severe acute respiratory coronavirus 2 (SARS-CoV-2) infection. RESULTS: Oxidised cholesterols and the oxysterol-sensing receptor GPR183 were identified as drivers of monocyte-macrophage infiltration to the lung during influenza virus (IAV) and SARS-CoV-2 infection. Both IAV and SARS-CoV-2 infection upregulated the enzymes cholesterol 25-hydroxylase (CH25H) and cytochrome P450 family 7 subfamily member B1 (CYP7B1) in the lung, resulting in local production of the oxidised cholesterols 25-hydroxycholesterol (25-OHC) and 7α,25-dihydroxycholesterol (7α,25-OHC). Loss-of-function mutation of GPR183, or treatment with a GPR183 antagonist, reduced macrophage infiltration and inflammatory cytokine production in the lungs of IAV- or SARS-CoV-2-infected mice. The GPR183 antagonist significantly attenuated the severity of SARS-CoV-2 infection and viral loads. Analysis of single cell RNASeq data on bronchoalveolar lavage samples from healthy controls and COVID-19 patients with moderate and severe disease revealed that CH25H, CYP7B1 and GPR183 are significantly upregulated in macrophages during COVID-19. CONCLUSION: This study demonstrates that oxysterols drive inflammation in the lung via GPR183 and provides the first preclinical evidence for therapeutic benefit of targeting GPR183 during severe viral respiratory infections.

Mortality and risk factors for COVID-19 in hemodialysis patients: A systematic review and meta-analysis.

Introduction: The present study systematically reviewed the clinical features and risk factors in patients undergoing maintenance hemodialysis (MHD) who also acquired coronavirus disease 2019 (COVID-19). More specifically, clinical manifestations, prognosis, and risk factors for death among this population were explored. Method: A literature search using the PubMed, Web of Science, and Embase databases, for articles involving patients with laboratory-confirmed COVID-19 and undergoing MHD published between January 1, 2020, and March 13, 2022, was performed. Random-effects meta-analyses were performed to calculate the weighted mean prevalence and corresponding 95% confidence interval (CI) or weighted means and 95% CI. Heterogeneity among studies was assessed using I2 statistics. Results: Twenty-two studies including 13,191 patients with COVID-19 undergoing MHD were selected. The most common symptoms included fever (53% [95% CI 41%-65%]) and cough (54% [95% CI 48%-60%]); however, 17% (95% CI 11%-22%) of the cases were asymptomatic. In subgroup analysis, the proportion of male patients (65% [95% CI 58%-71%]), and patients with coronary artery disease (30% [95% CI 17%-44%) and chronic obstructive pulmonary disease (9% [95% CI 4%-15%]) was greater in the non-survivor group compared with the survivor group. Furthermore, patients undergoing MHD, who were also positive for COVID-19, exhibited a high mortality rate (24% [95% CI 19%-28%]). Conclusions: MHD patients with COVID-19 may initially present as asymptomatic or with mild symptoms; nevertheless, in this study, these patients exhibited a higher risk for death compared with COVID-19 patients not undergoing MHD. Moreover, male sex and underlying cardiovascular and respiratory diseases increased the mortality risk.

Large scale clinical trials: lessons from the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has presented substantial new challenges to clinical and research teams. Our objective was to analyse the experience of investigators and research delivery staff regarding the research response to COVID-19 in order to identify these challenges as well as solutions for future pandemic planning. METHODS: We conducted a survey of diverse research staff involved in delivery of COVID-19 clinical trials across the UK. This was delivered online across centres linked to the NIHR Respiratory Translational Research Collaboration. Responses were analysed using a formal thematic analysis approach to identify common themes and recommendations. RESULTS: 83 survey participants from ten teaching hospitals provided 922 individual question responses. Respondents were involved in a range of research delivery roles but the largest cohort (60%) was study investigators. A wide range of research experiences were captured, including early and late phase trials. Responses were coded into overarching themes. Among common observations, complex protocols without adaptation to a pandemic were noted to have hampered recruitment. Recommendations included the need to develop and test pandemic-specific protocols, and make use of innovations in information technology. Research competition needs to be avoided and drug selection processes should be explicitly transparent. CONCLUSIONS: Delivery of clinical trials, particularly earlier phase trials, in a pandemic clinical environment is highly challenging, and was reactive rather than anticipatory. Future pandemic studies should be designed and tested in advance, making use of pragmatic study designs as far as possible and planning for integration between early and later phase trials and regulatory frameworks.

Safety of inactivated SARS-CoV-2 vaccines in patients with allergic diseases.

BACKGROUND: Considering the considerable prevalence of allergic disease in the general population, an urgent need exists for inactivated SARS-CoV-2 vaccines that can be safely administered to those subjects. METHODS: This retrospective cohort study including 1926 participants who received inactivated SARS-CoV-2 vaccines, compared their local and systemic reactions in 7 days after each dose of inactivated SARS-CoV-2 vaccine, and anti-SARS-CoV-2 IgG after vaccination in all participants. RESULTS: Pain at the injection site within seven days after the first injection was the most commonly reported local reaction, occurring in 31.0% of the patients with allergic disease and 18.9% in the control group, respectively (P < 0.001). After the first dose, systemic events were more frequently reported in patients with allergic disease than control group (30.2% vs. 22.9%, P < 0.001). After the second dose, systemic events occurred less often, affecting 17.1% of the patients with allergic disease and 11.1% of the control group (P < 0.002). The occurrence of fatigue, vertigo, diarrhea, skin rash, sore throat were the most frequent systemic reactions. Overall, a lower incidence of local and systemic reactive events was observed after the second dose than the first dose in patients with allergic disease and control group. Nearly all participants had positive IgG antibodies, and participants with allergic disease had higher frequencies compared with control group (100.0 vs.99.4%). CONCLUSIONS: Although local and systemic reactions were more frequently reported in patients with allergic disease than control group, administration of the inactivated SARS-CoV-2 vaccine was safe and well tolerated by all participants; no participants experienced a serious adverse event, and none were hospitalized. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2100048549. Registered Jul 10, 2021.

Clinical and experimental bacteriophage studies: Recommendations for possible approaches for standing against SARS-CoV-2.

In 2019, the world faced a serious health challenge, the rapid spreading of a life-threatening viral pneumonia, coronavirus disease 2019 (COVID-19) caused by a betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 2022 WHO statistics shows more than 5.6 million death and about 350 million infection by SARS-CoV-2. One of the life threatening aspects of COVID-19 is secondary infections and reduced efficacy of antibiotics against them. Since the beginning of COVID-19 many researches have been done on identification, treatment, and vaccine development. Bacterial viruses (bacteriophages) could offer novel approaches to detect, treat and control COVID-19. Phage therapy and in particular using phage cocktails can be used to control or eliminate the bacterial pathogen as an alternative or complementary therapeutic agent. At the same time, phage interaction with the host immune system can regulate the inflammatory response. In addition, phage display and engineered synthetic phages can be utilized to develop new vaccines and antibodies, stimulate the immune system, and elicit a rapid and well-appropriate defense response. The emergence of SARS-CoV-2 new variants like delta and omicron has proved the urgent need for precise, efficient and novel approaches for vaccine development and virus detection techniques in which bacteriophages may be one of the plausible solutions. Therefore, phages with similar morphology and/or genetic content to that of coronaviruses can be used for ecological and epidemiological modeling of SARS-CoV-2 behavior and future generations of coronavirus, and in general new viral pathogens. This article is a comprehensive review/perspective of potential applications of bacteriophages in the fight against the present pandemic and the post-COVID era.

GACDN: generative adversarial feature completion and diagnosis network for COVID-19.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) causes tens of million infection world-wide. Many machine learning methods have been proposed for the computer-aided diagnosis between COVID-19 and community-acquired pneumonia (CAP) from chest computed tomography (CT) images. Most of these methods utilized the location-specific handcrafted features based on the segmentation results to improve the diagnose performance. However, the prerequisite segmentation step is time-consuming and needs the intervention by lots of expert radiologists, which cannot be achieved in the areas with limited medical resources. METHODS: We propose a generative adversarial feature completion and diagnosis network (GACDN) that simultaneously generates handcrafted features by radiomic counterparts and makes accurate diagnoses based on both original and generated features. Specifically, we first calculate the radiomic features from the CT images. Then, in order to fast obtain the location-specific handcrafted features, we use the proposed GACDN to generate them by its corresponding radiomic features. Finally, we use both radiomic features and location-specific handcrafted features for COVID-19 diagnosis. RESULTS: For the performance of our generated location-specific handcrafted features, the results of four basic classifiers show that it has an average of 3.21% increase in diagnoses accuracy. Besides, the experimental results on COVID-19 dataset show that our proposed method achieved superior performance in COVID-19 vs. community acquired pneumonia (CAP) classification compared with the state-of-the-art methods. CONCLUSIONS: The proposed method significantly improves the diagnoses accuracy of COVID-19 vs. CAP in the condition of incomplete location-specific handcrafted features. Besides, it is also applicable in some regions lacking of expert radiologists and high-performance computing resources.

Diagnosing Asthma with and without Aerosol-Generating Procedures.

BACKGROUND: Asthma diagnostic guidelines require procedures with aerosol-generating potential (aerosol-generating procedures [AGPs]) to guide decision making. Restricted access to AGPs poses significant challenges in primary care and resource-poor countries, further amplified during the coronavirus disease 2019 pandemic. OBJECTIVE: To establish an approach to asthma diagnosis that does not require AGPs. METHOD: Symptomatic yet untreated (beyond as-required bronchodilator use) adults with clinician-suspected asthma and maximum 10 pack year smoking history were recruited. Clinical history, physical examination, spirometry with bronchodilator reversibility, home peak flow monitoring, and bronchial challenges were performed, and fractional exhaled nitric oxide and serum eosinophils measured. Tests were then repeated following treatment with inhaled corticosteroids before an asthma diagnosis was confirmed or refuted by an expert panel. RESULTS: A total of 65 adults (mean age, 34.8 ± 12.2 years) were recruited. Five were excluded as "unclassifiable," because of borderline results or missing data. Of the remainder, 36 were diagnosed with asthma and 24 were not. Using data from non-AGPs only (wheeze on auscultation and blood eosinophilia) and home peak flow variability, a "rule-in" diagnostic model provided comparable discriminative ability to the application of established guidelines. Clinical suspicion of asthma together with at least 1 positive non-AGP test result provided a sensitivity of 55%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 60%. Application of this model reduced the need for spirometry-based tests by one-third. CONCLUSIONS: The proposed diagnostic algorithm may be clinically useful in "ruling-in" asthma in adults when access to AGPs is limited. This algorithm is not suitable for those with low clinical probability, with a significant smoking history, or where alternative diagnoses are more likely. This pragmatic approach to asthma diagnosis merits prospective validation.

Pulmonary Sequelae at 4 Months After COVID-19 Infection: A Single-Centre Experience of a COVID Follow-Up Service.

INTRODUCTION: At the end of the first year of the COVID-19 pandemic, more than 78 million known survivors were recorded. The long-term pulmonary sequelae of COVID-19 remain unknown. METHODS: We performed a retrospective analysis of a post-COVID follow-up service to estimate the burden of persistent pulmonary morbidity in hospitalised COVID survivors. RESULTS: A total of 221 patients were followed-up: 44 intensive care unit (ICU) and 177 ward patients. Further investigations were planned as per British Thoracic Society Guidelines: For all ICU patients (n = 44) and for 38 of 177 (21%) ward-based patients who had persistent symptoms and/or persistent radiographic changes on CXR at their initial 8-week follow-up visit. In the ward-based cohort, statistically significant associations with persistent symptoms were being an ex- or current smoker, having pre-existing diabetes, and having a longer length of stay. In patients requiring further investigations, pulmonary function tests (PFTs; n = 67) at an average of 15 weeks post-discharge showed abnormalities in at least one PFT parameter in 79% (equating to 24% of the entire cohort). The most common abnormality was an abnormal diffusion capacity of carbon monoxide (TLCO), highest in the ICU cohort (64% ICU vs. 38% non-ICU). TLCO correlated negatively with length of stay and with maximum inspired FiO2 in the patient group as a whole. In ICU patients, TLCO correlated negatively with maximum inspired positive airway pressure. Computed tomography scans (n = 72) at an average of 18 weeks post-discharge showed evidence of persistent ground glass opacities in 44% and fibrosis in 21% (equating to 7% of the entire cohort). CONCLUSION: Our data add to the growing evidence that there will be pulmonary sequelae in a proportion of COVID survivors, providing some insight into what may become a significant chronic global health problem.

Seroprevalence of SARS-CoV-2 infections among children visiting a hospital.

IMPORTANCE: In this study, we retrospectively investigated the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies within serum samples from children in Beijing, China. These findings provide preliminary guidance regarding population susceptibility to SARS-CoV-2, which will aid in establishing policy toward coronavirus disease 2019 (COVID-19) prevention and control. OBJECTIVE: To understand the seropositivity of anti-SARS-CoV-2 IgM/IgG antibodies among children in Beijing, China, evaluate the susceptibility of children in Beijing to SARS-CoV-2, and provide prima facie evidence to guide SARS-CoV-2 prevention and control. METHODS: IgM/IgG antibody kits (colloidal gold) were used to conduct preliminary screening of SARS-CoV-2 IgM/IgG antibodies in serum samples of children who presented to Beijing Children's Hospital, Capital Medical University, having fever or requiring hospitalization, from March 2020 to August 2020. Statistical analysis of anti-SARS-CoV-2 antibody seropositivity was performed according to the children's general demographic characteristics, timing of admission to hospital, presence of pneumonia, and viral nucleic acid test results. RESULTS: The study included 19 797 children with both IgM and IgG antibody results. Twenty-four children had anti-SARS-CoV-2 IgM-positive results (positive rate of 1.2‰), twelve children had anti-SARS-CoV-2 IgG-positive results (positive rate of 0.6‰). Viral nucleic acid test results were negative for the above-mentioned children with positive antibody findings; during the study, two children exhibited positive viral nucleic acid test results, but their anti-SARS-CoV-2 IgM/IgG antibody results were negative. Anti-SARS-CoV-2 IgM antibody seropositivity was higher in the <1-year-old group than in the ≥6-year-old group. The rates of anti-SARS-CoV-2 IgM seropositivity was highest in August from March to August; IgG results did not significantly differ over time. The rates of anti-SARS-CoV-2 IgM or IgG seropositivity among children with and without suspected pneumonia did not significantly differ between groups. INTERPRETATION: During the study period, the rates of anti-SARS-CoV-2 IgM/IgG antibody seropositivity were low among children who presented to Beijing Children's Hospital, Capital Medical University. The findings suggest that children in Beijing are generally susceptible to SARS-CoV-2 infection; COVID-19 prevention and control measures should be strengthened to prevent disease in children.

Matters needing attention about invoking ecological niche model in epidemiology

The outbreak of COVID-19 has spurred a number of risk assessments within the scientific community regarding its spread and intensity. A popular ecological tool, ecological niche models (ENMs) are often used in these studies, and have been used to predict potential hotspots and trends of epidemics. However, ENMs are not the best tool for predicting COVID-19 spread due to the virus' characteristics. This article reviews the application of ENMs for various epidemiological studies in recent decades, comparing advantages and disadvantages of ENM methods for predicting disease characteristics and other models. ENMs can only be used to analyze the impact of environmental disturbances of intermediate hosts during the epidemic transmission process, but SARS-CoV-2 is more reliant on human transmission, leading to poor ENM performance. Therefore, we must choose the appropriate modeling method for the transmission pathways of the disease to accurately predict the epidemic trend. Under appropriate conditions, ENMs can analyze the spread range of epidemics but we must include other interference factors to test and evaluate ENMs accurately. Misusing ENMs would mislead decision-makers. Therefore, when applying ENMs to predict the spread of infectious diseases, the primary consideration must be whether the scientific question meets the ecological assumptions.