Hillock Related Degradation Mechanism for AlGaN-Based UVC LEDs.

Heteroepitaxial growth of high Al-content AlGaN often results in a high density of threading dislocations and surface hexagonal hillocks, which degrade the performance and reliability of AlGaN-based UVC light emitting diodes (LEDs). In this study, the degradation mechanism and impurity/defect behavior of UVC LEDs in relation to the hexagonal hillocks have been studied in detail. It was found that the early degradation of UVC LEDs is primarily caused by electron leakage. The prominent contribution of the hillock edges to the electron leakage is unambiguously evidenced by the transmission electron microscopy measurements, time-of-flight secondary ion mass spectrometry, and conductive atomic force microscopy. Dislocations bunching and segregation of impurities, including C, O, and Si, at the hillock edges are clearly observed, which facilitate the trap-assisted carrier tunneling in the multiple quantum wells and subsequent recombination in the p-AlGaN. This work sheds light on one possible degradation mechanism of AlGaN-based UVC LEDs.

Effectiveness of a booster dose of COVID-19 vaccines during an outbreak of SARS-CoV-2 Omicron BA.2.2 in China: A case-control study.

Real-world evidence on the effectiveness of COVID-19 vaccines marketed in China against the Omicron BA.2.2 variant remains scarce. A case-control study was conducted to estimate the vaccine effectiveness (VE) of COVID-19 vaccines marketed in China (inactivated vaccines, an Ad5-nCoV vaccine, and a recombinant protein vaccine). There were 414 cases infected with SARS-CoV-2 and 828 close contacts whose test results were consecutively negative as controls during the outbreak of the Omicron variant in Lu'an City, Anhui Province, China, in April 2022. The overall adjusted VE against Omicron BA.2.2 variant infection in the vaccinated group with any COVID-19 vaccine was 35.0% (95% CI: -9.1-61.3%), whereas the adjusted VE for booster vaccination was 51.6% (95% CI: 15.2-72.4%). Subgroup analysis showed that the overall adjusted VE of the Ad5-nCoV vaccine (65.8%, 95% CI: 12.8-86.6%) during the outbreak while any dose of inactivated vaccines and recombinant protein vaccine offered no protection. The adjusted VE of three-dose inactivated vaccines was 48.0% (95% CI: 8.0-70.6%), and the two-dose Ad5-nCoV vaccine was 62.9% (95% CI: 1.8-86%). There is no protection from a three-dose recombinant protein vaccine. COVID-19 vaccines offered 46.8% (95% CI: 9.5-68.7%) protection from infection within six months. There were statistically significant differences between the VEs of heterologous booster (VE = 76.4%, 95% CI: 14.3-93.5%) and homologous booster vaccination (VE = 51.8%, 95% CI: 9.6-74.3%) (P = .036). Booster vaccination of COVID-19 vaccines offered more protection than full vaccination. A booster vaccination campaign for a booster dose after three doses of a recombinant protein vaccine must be urgently conducted.

Whole genome sequence analysis of SARS-CoV-2 from 40 imported cases of confirmed COVID-19 in Sichuan

Objective To understand the genomic characteristics of SARS-CoV-2 from 40 imported cases with confirmed COVID-19 in Sichuan during January and March 2022. Methods Total viral RNA was extracted from respiratory samples of 182 confirmed COVID-19 cases who entered China through Chendu International Airport from January to March 2022.Mutation nucleic acid detection kit was used to identify the mutant strains and Illumina sequencing platform was applied for whole genome sequence（WGS） of virus.SARS-CoV-2 reference sequences were downloaded from NCBI database for genetic evolution and antigen variation analysis.The Nextclade and Pangolin online virus analysis platform were used to determine the virus family and type,and to analyze the mutation loci of the virus.The phylogenetic tree was constructed,along with the epidemiological data of cases to analyze the source and correlation of viruses. Results Among 182 imported COVID-19 cases,B.1.617.2 mutations were identified in 3 cases and B.1.1.529 mutations were detected in 57 cases.A total of 40 SARS-CoV-2 whole genome sequences with coverage>95% were obtained in this study.Nextclade typing analysis showed that 3 sequences belonged to 21J（Delta）,5 sequences belonged to 21K（Omicron）and the remaining 32 sequences belonged to 21L（Omicron）.Pangolin typing analysis showed that the 3 sequences of 21J（Delta）belonged to AY.4,AY.109and B.1.617.2,the 5sequences of 21K（Omicron）all belonged to BA.1.1,and the remaining 32 sequences of 21L（Omicron）belonged to BA.2.Our sequence results were99.7% consistency with the Omicron variants sequences in current GISAID database.Compared with the reference sequence strain Wuhan-Hu-1（NC＿045512.2）,45,47and 42nucleotide variation sites and 36,25 and 36amino acid variation sites were found in the 3 sequences of 21J（Delta）.There were average 59（26-64）nucleotide mutation sites and 48（10-53）amino acid mutation sites in the 5sequences of 21K（Omicron）.The median number of nucleotide mutation sites of 71（66-76）and amino acid mutation sites of 53（40-56）were identified in the 32sequences of 21L（Omicron）.Phylogenetic tree analysis showed that 40SARS-CoV-2WGSs were all related to the current variants of concern（VOC）. Conclusions Continuous sequencing of SARS-CoV-2whole genome from imported cases with confirmed COVID-19is of great significance for the prevention and control of the outbreak and prevalence of local epidemic caused by imported viruses in Sichuan.

Ultrabright nanoparticle-labeled lateral flow immunoassay for detection of anti-SARS-CoV-2 neutralizing antibodies in human serum.

The level of anti-SARS-CoV-2 neutralizing antibodies (NAb) is an indispensable reference for evaluating the acquired protective immunity against SARS-CoV-2. Here, we established an ultrabright nanoparticles-based lateral flow immunoassay (LFIA) for one-step rapid semi-quantitative detection of anti-SARS-CoV-2 NAb in vaccinee's serum. Once embedded in polystyrene (PS) nanoparticles, the aggregation-induced emission (AIE) luminogen, AIE490, exhibited ultrabright fluorescence due to the rigidity of PS and severe inhibition of intramolecular motions. The ultrabright AIE490-PS nanoparticle was used as a fluorescent marker of LFIA. Upon optimized conditions including incubation time, concentrations of coated proteins and conjugated nanoparticles, amounts of antigens modified on the surface of nanoparticles, dilution rate of serum samples, and so on, the ultrabright nanoparticles-based LFIA could accurately identify 70 negative samples and 63 positive samples from human serum (p < 0.0001). The intra- and inter-assay precisions of the established method are above 13% and 16%, respectively. The established LFIA has tremendous practical value of generalization as a rapid semi-quantitative detection method of anti-SARS-CoV-2 NAb. Meanwhile, the AIE490-PS nanoparticle is also promising to detect many other analytes by altering the protein on the surface.

The psychological impact of COVID-19 pandemic on healthcare workers.

Background: As unprecedented and prolonged crisis, healthcare workers (HCWs) are at high risk of developing psychological disorders. We investigated the psychological impact of COVID-19 pandemic on HCWs. Methods: This cross-sectional study randomly recruited 439 HCWs in Hunan Cancer Hospital via a web-based sampling method from June 1st 2021 to March 31st 2022. Anxiety and depression levels were measured using Hospital Anxiety and Depression Scale (HADS). The Post Traumatic Stress Disorder (PTSD) Checklist for DSM-5 (PCL-5) was used to assess the presence and severity of PTSD. Fear was measured by modified scale of SARS. Data were collected based on these questionnaires. Differences in fear, anxiety, depression and PTSD among HCWs with different clinical characteristics were analyzed using a multivariate analysis of variance. The Cronbach's alpha scores in our samples were calculated to evaluate the internal consistency of HADS, fear scale and PCL-5. Results: The prevalence of anxiety, depression, and PTSD in HCWs was 15.7, 9.6, and 12.8%, respectively. Females and nurses were with higher fear level (P < 0.05) and higher PTSD levels (P < 0.05). Further analysis of female HCWs revealed that PTSD levels in the 35-59 years-old age group were higher than that in other groups; while married female HCWs were with increased fear than single HCWs. The internal consistency was good, with Cronbach's α = 0.88, 0.80 and 0.84 for HADS, fear scale, and PCL, respectively. Conclusion: Gender, marital status, and age are related to different level of psychological disorders in HCWs. Clinical supportive care should be implemented for specific group of HCWs.

The roles of primary care doctors in the COVID-19 pandemic: consistency and influencing factors of doctor's perception and actions and nominal definitions.

BACKGROUND: At the end of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic broke out. As front-line health professionals, primary care doctors play a significant role in screening SARS-CoV-2 infection and transferring suspected cases. However, the performance of primary care doctors is influenced by their knowledge and role perception. A web-based cross-sectional survey was conducted to assess the consistency and influencing factors of primary care doctor's role perception and expert advice in the guidelines (regulatory definition). METHODS: We designed the questionnaire using "Wenjuanxing" platform, distributed and collected the questionnaire through WeChat social platform, and surveyed 1758 primary care doctors from 11 community health service stations, community health service centers and primary hospitals in Zhejiang Province, China. After the questionnaire was collected, descriptive statistics were made on the characteristics of participants, and univariate analysis and multivariate analysis were used to determine the relevant factors affecting their role cognition. RESULTS: In the reporting and referral suspected cases and patients receiving treatment, most participants' cognition of their roles were consistent with the requirements of guidelines. However, 49.54% and 61.43% of participant doctors were not in line with the government guidelines for diagnosing and classifying COVID-19 and treating suspected cases, respectively. Having a middle or senior professional title and participating in front-line COVID-19 prevention and control work is beneficial to the accurate role perception of diagnosis and classification of COVID-19, the reporting and transfer of suspected cases, and the treatment of suspected cases. CONCLUSIONS: Primary care doctors' role perceptions in the COVID-19 pandemic are not always consistent with government guidelines in some aspects, such as transferring and diagnosing suspected cases. Therefore, it is essential to guide primary care doctors in performing their duties, especially those with lower professional titles.

A Granulation Tissue Detection Model to Track Chronic Wound Healing in DM Foot Ulcers

Diabetes mellitus (DM) foot ulcer is a chronic wound and is highly related to the mortality and morbidity of infection, and might induce sepsis and foot amputation, especially during the isolation stage of the COVID-19 pandemic. Visual observation when changing dressings is the most common and traditional method of detecting wound healing. The formation of granulation tissues plays an important role in wound healing. In the complex pathophysiology of excess and unhealthy granulation induced by infection, oxygen supply may explain the wound healing process in DM patients with multiple complicated wounds. Thus, advanced and useful tools to observe the condition of wound healing are very important for DM patients with extremities ulcers. For this purpose, we developed an artificial intelligence (AI) detection model to identify the growth of granulation tissue of the wound bed. We recruited 100 patients to provide 219 images of wounds at different healing stages from 2 hospitals. This was performed to understand the wound images of inconsistent size, and to allow self-inspection on mobile devices, having limited computing resources. We segmented those images into 32 ×32 blocks and used a reduced ResNet-18 model to test them individually. Furthermore, we conducted a learning method of active learning to improve the efficiency of model training. Experimental results reveal that our model can identify the region of granulation tissue with an Intersection-over-Union (IOU) rate higher than 0.5 compared to the ground truth. Multiple cross-repetitive validations also confirm that the detection results of our model may serve as an auxiliary indicator for assessing the progress of wound healing. The preliminary findings may help to identify the granulation tissue of patients with DM foot ulcer, which may lead to better long-term home care during the COVID-19 pandemic. The current limit of our model is an IOU of about 0.6. If more actual data are available, the IOU is expected to improve. We can continue to use the currently established active learning process for subsequent training.

High-fidelity Cas13 variants for targeted RNA degradation with minimal collateral effects.

CRISPR-Cas13 systems have recently been used for targeted RNA degradation in various organisms. However, collateral degradation of bystander RNAs has limited their in vivo applications. Here, we design a dual-fluorescence reporter system for detecting collateral effects and screening Cas13 variants in mammalian cells. Among over 200 engineered variants, several Cas13 variants including Cas13d and Cas13X exhibit efficient on-target activity but markedly reduced collateral activity. Furthermore, transcriptome-wide off-targets and cell growth arrest induced by Cas13 are absent for these variants. High-fidelity Cas13 variants show similar RNA knockdown activity to wild-type Cas13 but no detectable collateral damage in transgenic mice or adeno-associated-virus-mediated somatic cell targeting. Thus, high-fidelity Cas13 variants with minimal collateral effects are now available for targeted degradation of RNAs in basic research and therapeutic applications.

The prevalence of psychological disorders among cancer patients during the COVID-19 pandemic: A meta-analysis.

PURPOSE: We aimed to assess the prevalence rate (PR) of depression, anxiety, posttraumatic stress disorder (PTSD), insomnia, distress, and fear of cancer progression/recurrence among patients with cancer during the COVID-19 pandemic. METHODS: Studies that reported the PR of six psychological disorders among cancer patients during the COVID-19 pandemic were searched in PubMed, Embase, PsycINFO, and Web of Science databases, from January 2020 up to 31 January 2022. Meta-analysis results were merged using PR and 95% confidence intervals, and heterogeneity among studies was evaluated using I² and Cochran's Q test. Publication bias was examined using funnel plots and Egger's tests. All data analyses were performed using Stata14.0 software. RESULTS: Forty studies with 27,590 participants were included. Pooled results showed that the PR of clinically significant depression, anxiety, PTSD, distress, insomnia, and fear of cancer progression/recurrence among cancer patients were 32.5%, 31.3%, 28.2%, 53.9%, 23.2%, and 67.4%, respectively. Subgroup analysis revealed that patients with head and neck cancer had the highest PR of clinically significant depression (74.6%) and anxiety (92.3%) symptoms. Stratified analysis revealed that patients with higher education levels had higher levels of clinically significant depression (37.2%). A higher level of clinically significant PTSD was observed in employed patients (47.4%) or female with cancer (27.9%). CONCLUSION: This meta-analysis evaluated the psychological disorders of cancer patients during the COVID-19 outbreak. Therefore, it is necessary to develop psychological interventions to improve the mental health of cancer patients during the pandemic.

COVID-19 Vaccine Hesitancy Among Older Adolescents and Young Adults: A National Cross-Sectional Study in China.

Background: With promotion of COVID-19 vaccinations, there has been a corresponding vaccine hesitancy, of which older adolescents and young adults represent groups of particular concern. In this report, we investigated the prevalence and reasons for vaccine hesitancy, as well as potential risk factors, within older adolescents and young adults in China. Methods: To assess these issues, an online survey was administered over the period from March 14 to April 15, 2021. Older adolescents (16-17 years old) and young adults (18-21 years old) were recruited nationwide from Wechat groups and results from a total of 2,414 respondents were analyzed. Socio-demographic variables, vaccine hesitancy, psychological distress, abnormal illness behavior, global well-being and social support were analyzed in this report. Results: Compared to young adults (n = 1,405), older adolescents (n = 1,009) showed higher prevalence rates of COVID-19 vaccine hesitancy (16.5 vs. 7.9%, p < 0.001). History of physical diseases (p = 0.007) and abnormal illness behavior (p = 0.001) were risk factors for vaccine hesitancy among older adolescents, while only a good self-reported health status (p = 0.048) was a risk factor for young adults. Concerns over COVID-19 vaccine side effects (67.1%) and beliefs of invulnerability regarding infection risk (41.9%) were the most prevalent reasons for vaccine hesitancy. Providing evidence on the vaccine reduction of COVID-19 infection risk (67.5%), ensuring vaccine safety (56.7%) and the low risk of side effects (52.7%) were the most effective persuasions for promoting vaccinations. Conclusion: In China, older adolescents showed a higher prevalence for vaccine hesitancy than that of young adults. Abnormal illness behavior and history of physical diseases were risk factors for vaccine hesitancy among these older adolescents, while social support represents an important factor which could help to alleviate this hesitancy.

Roles and Challenges for Village Doctors in COVID-19 Pandemic Prevention and Control in Rural Beijing, China: A Qualitative Study

Objectives Rural areas in China are more vulnerable to COVID-19 pandemic than urban areas, due to their far fewer health care resources. Village doctors, as rural grassroots health workers in China, have been actively engaged in the pandemic prevention and control. This study aims to describe the roles of village doctors in rural China, and the challenges they have faced during the prevention and control of the COVID-19 pandemic. Setting This study was conducted in three towns in Huairou District, Beijing, China. Design We carried out semi-structured interviews with 75 key informants. All the interviews were audio-recorded and transcribed verbatim. We employed thematic analysis to define themes and sub-themes from the qualitative data. Results We reported four themes. First, the village doctor guided the village committee to carry out decontamination, monitored home-isolated residents, and disseminated knowledge on prevention of the COVID-19 pandemic during the rural pandemic prevention and control. Second, they took pandemic prevention measures in village clinics, distributed pandemic prevention materials, and undertook pre-screening triage. Third, village doctors provided basic medical care, including treatment of common diseases as well as the purchase and delivery of medicines to villagers. Fourth, village doctors faced difficulties and challenges, such as inadequate medical skills, aging staff structure, and lack of pandemic prevention materials. Conclusions Despite many difficulties and challenges, village doctors have actively participated in rural pandemic prevention and control, and made outstanding contributions to curbing spread of COVID-19 pandemic in rural areas. Village doctors provide basic health care while participating in various non-medical tasks.

Effects of Micro Architectural Environmental Interventions on Handwashing Compliance of Adolescents: A School-Based Intervention Trial.

PURPOSE: Aimed to explore more practical interventions for handwashing compliance (HWC) and protect the minor' health with the pandemic background, this study tested whether micro architectural interventions would promote HWC of adolescents through active experience. BACKGROUND: Despite the well-documented benefits of handwashing (HW), low compliance is common among adolescents. HW space in schools has always been treated as attached spaces of little matter, which is unfavorable to public health. According to environment behavior perspective, personal motivation of HWC may be motivated by active environment. METHOD: A school-based investigation and a 30-week environmental behavior experiment were conducted when students returned to school after the COVID-19 pandemic closure in 2020 in China to evaluate the effects of active environment interventions selected by previous survey on promoting adolescents' HW rate. Digital infrared counters were used to unobtrusively document their behavior. RESULTS: Results in summer revealed a positive effect on all intervention groups. The effects of combined interventions were higher than the effect of any single intervention. However, HW rates of all groups declined sharply in the coldest month and the between-group differences decreased. Significant correlations were found between HW rates and satisfaction with the intervention schemes. CONCLUSIONS: Findings suggest that architectural environmental intervention may potentially be a positive, friendly, and one-time investment mean to expand the HWC intervention scope from passive policies to positive experience, and HW space design for adolescents should be treated as a public health strategy.

Prevalence and risk of COVID-19 in patients with rheumatic diseases: a systematic review and meta-analysis.

OBJECTIVES: COVID-19 pandemic has already had a tremendous impact on the process of human society; the survival of mankind and the healthy living environment deterioration with the influence will last for many years. This meta-analysis aims to assess the risk of COVID-19 in patients with rheumatic diseases. METHODS: PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Database (CBM) were systematically searched with no language restriction up to July 5, 2021. The pooled rates were synthesized by fixed effect model or random effect model depending on heterogeneity. RESULTS: A total of 83 articles were included in this meta-analysis. The incidence of COVID-19 in patient with rheumatic diseases was 0.0190 (95% CI: 0.0136-0.0252), and the hospitalization rate, intensive care unit admission rate, mechanical ventilation rate, and case fatality rate of patients with rheumatic diseases infected with COVID-19 were 0.4396 (95% CI: 0.3899-0.4898), 0.0635 (95% CI: 0.0453-0.0836), 0.0461 (95% CI: 0.0330-0.0609), and 0.0346 (95% CI: 0.0218-0.0493), respectively. CONCLUSIONS: Our research shows that patients with rheumatic diseases have great risk of COVID-19. Differences in COVID-19 incidence, hospitalization rates, and mortality rates in regions were statistically significant. We still need to pay attention to the risk of COVID-19 in patients with rheumatic diseases. KEY POINTS: • Although the risk of COVID-19 in patients with rheumatic diseases has been discussed in previous meta-analysis, their research directions were inconsistent, and few studies focus on prevalence or serious outcomes of COVID-19 in patient with rheumatic diseases, while the quality of these articles was variable. • The incidence of COVID-19 and serious clinical outcomes in patients with rheumatic diseases were still high along with differential risks in most regions. • The use of glucocorticoids and conventional synthetic disease-modifying antirheumatic drugs did not affect the hospitalization rate and mortality in rheumatism patients with COVID-19.

Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks.

BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance Method: We have improved the GraphDTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in GraphDTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then inputted into a 2 layer FC network, and a valuable binding affinity is the output. RESULT: We used the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model showed better performance when compared with the experimental results of GraphDTA Conclusion: In this paper, we altered the GraphDTA model to predict drug-target affinity. It represents the drug as a graph and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time-series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

Megakaryocytes in pulmonary diseases.

Megakaryocytes (MKs) are typical cellular components in the circulating blood flowing from the heart into the lungs. Physiologically, MKs function as an important regulator of platelet production and immunoregulation. However, dysfunction in MKs is considered a trigger in various diseases. It has been described that the lung is an important site of platelet biogenesis from extramedullary MKs, which may play an essential role in various pulmonary diseases. With detailed studies, there are different degrees of numerical changes of MKs in coronavirus disease 2019 (COVID-19), acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), lung cancer, pulmonary fibrosis (PF), and other pulmonary diseases. Also, MKs inhibit or promote the development of pulmonary diseases through various pathways. Here, we summarize the current knowledge of MKs in pulmonary diseases, highlighting the physiological functions and integrated molecular mechanisms. We aim to shine new light on not only the subsequent study of MKs but also the diagnosis and treatment of pulmonary diseases.

Spatial Modeling and Analysis of Human Traffic and Infectious Virus Spread in Community Networks.

The use of network models to study the spread of infectious diseases is gaining increasing interests. They allow the flexibility to represent epidemic systems as networks of components with complex and interconnected structures. However, most of previous studies are based on networks of individuals as nodes and their social relationships (e.g., friendship, workplace connections) as links during the virus spread process. Notably, the transmission and spread of infectious viruses are more pertinent to human dynamics (e.g., their movements and interactions with others) in the spatial environment. This paper presents a novel network-based simulation model of human traffic and virus spread in community networks. We represent spatial points of interests (POI) as nodes where human subjects interact and perform activities, while edges connect these POIs to form a community network. Specifically, we derive the spatial network from the geographical information systems (GIS) data to provide a detailed representation of the underlying community network, on which human subjects perform activities and form traffics that impact the process of virus transmission and spread. The proposed framework is evaluated and validated in a community of university campus. Experimental results showed that the proposed simulation model is capable of describing interactive human activities at an individual level, as well as capturing the spread dynamics of infectious diseases. This framework can be extended to a wide variety of infectious diseases and shows strong potentials to aid the design of intervention policies for epidemic control.

Potent Anti-SARS-CoV-2 Efficacy of COVID-19 Hyperimmune Globulin from Vaccine-Immunized Plasma.

Coronavirus disease 2019 (COVID-19) remains a global public health threat. Hence, more effective and specific antivirals are urgently needed. Here, COVID-19 hyperimmune globulin (COVID-HIG), a passive immunotherapy, is prepared from the plasma of healthy donors vaccinated with BBIBP-CorV (Sinopharm COVID-19 vaccine). COVID-HIG shows high-affinity binding to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein, the receptor-binding domain (RBD), the N-terminal domain of the S protein, and the nucleocapsid protein; and blocks RBD binding to human angiotensin-converting enzyme 2 (hACE2). Pseudotyped and authentic virus-based assays show that COVID-HIG displays broad-spectrum neutralization effects on a wide variety of SARS-CoV-2 variants, including D614G, Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Kappa (B.1.617.1), Delta (B.1.617.2), and Omicron (B.1.1.529) in vitro. However, a significant reduction in the neutralization titer is detected against Beta, Delta, and Omicron variants. Additionally, assessments of the prophylactic and treatment efficacy of COVID-HIG in an Adv5-hACE2-transduced IFNAR-/- mouse model of SARS-CoV-2 infection show significantly reduced weight loss, lung viral loads, and lung pathological injury. Moreover, COVID-HIG exhibits neutralization potency similar to that of anti-SARS-CoV-2 hyperimmune globulin from pooled convalescent plasma. Overall, the results demonstrate the potential of COVID-HIG against SARS-CoV-2 infection and provide reference for subsequent clinical trials.

Chinese nurses' self-expression media image during COVID-19 pandemic: A qualitative media image analysis.

AIM: To explore and describe nurses' self-expression media image during COVID-19 pandemic in China. BACKGROUND: Nurses play an important role in COVID-19 pandemic. Although nurses were widely reported by the media, which included praise for nurses and nursing work, the researches on how nurses expressed their self-images were limited. DESIGN: Qualitative media analysis. METHODS: Qualitative media analysis was conducted from January to April 2020, the researchers collected images and texts of 16 Chinese nurses who take care of COVID-19 patients. These images and texts were published on WeChat Moments by themselves. After analysed each image and text, researchers identified the denotative and connotative elements in each image and summarized each image in narrative way. FINDINGS: This study analysed 219 pictures and 15 short videos of 16 nurses' self-expression in WeChat moments. In this study, the media image self-expression of nurses were mostly positive. The images expressed by nurses in this study included care image; hero image; soldier image; female image; hope image and team image. Nurses rarely showed negative images in the media; The negative nurses image were expressed in hidden way, which included exhausted nurses image and fragile nurses image. Moreover, the nurse self-expression media image emphasized the nursing professionalism, but less showed the nursing connotation. CONCLUSIONS: The positive media image self-expression of nurses should be encouraged. Nurse Managers should pay attention to the deficiency of nursing image expression and guide nurses to show the essence and connotation of nursing.

Spatial Tessellation of Infectious Disease Spread for Epidemic Decision Support.

Infectious diseases such as COVID-19 have severe impacts on both economy and public health in the US and the world. Due to the heterogeneity of virus spread, there are spatial variations in the demand for medical resources such as personal protective equipment (PPE), testing kits, and vaccines. The availability of such medical resources is critical to effective epidemic control. Although these resources can be readily transported to designated areas for fighting an epidemic, the demand is increasing and varying in space that places significant stress on the supply and allocation of medical resources. However, little has been done on the tessellation of infection distributions for resource management. In this letter, we develop new tessellation algorithms for decision support in epidemic resource allocation and management. The objective is to estimate resource locations and coverage based on the spatial analysis of heterogeneous infection distribution. First, spatial tessellation centroids are initialized through either greedy or cluster-centric approaches. Next, the locations of tessellation centroids are calibrated through a gradient learning algorithm. Lastly, the spread tessellation is computed to provide an estimation of resource coverages under the heterogeneous infection distribution. The proposed methodology is evaluated and validated using a COVID-19 case study of infection data in Pennsylvania. Experimental results show the proposed methodology effectively tessellates the spread of infectious diseases. The new spread tessellation algorithms are shown to have strong potentials for epidemic decision support in infection modelling and resource allocation.

Statistical Analysis of Spatial Network Characteristics in Relation to COVID-19 Transmission Risks in US Counties.

Since the pandemic of COVID-19 began in January 2020, the world has witnessed drastic social-economic changes. To harness the virus spread, several studies have been done to study contributing factors that are pertinent to COVID-19 transmission risks. However, little has been done to investigate how human activities on the spatial network are correlated to the virus transmission and spread. This paper performs a statistical analysis to examine interrelationships between spatial network characteristics and cumulative cases of COVID-19 in US counties. Specifically, both county-level transportation profiles (e.g., the total number of commute workers, route miles of freight railroad) and road network characteristics of US counties are considered. Then, the lasso regression model is utilized to identify a sparse set of significant variables that are sensitive to the response variable of COVID-19 cases. Finally, the fixed-effect model is built to capture the relationship between the selected set of predictors and the response variable. This work helps identify and determine salient features from spatial network characteristics and transportation profiles, thereby improving the understanding of COVID-19 spread dynamics. These significant variables can also be utilized to develop simulation models for the prediction of real-time positions of virus spread and the optimization of intervention strategies.