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Background: Over the past three years, the COVID-19 pandemic has brought an overwhelming impact on China's hospital system and health care workers, which can lead to post traumatic stress disorder (PTSD) symptoms. Previous research has shown that the COVID-19 pandemic had long-term adverse effects on the mental health of health care workers. Indeed, PTSD symptoms have emerged as one of the significant mental health issues for health care workers arising from the COVID-19 pandemic. Therefore, we conducted this cross-sectional survey to investigate the prevalence of PTSD symptoms in health care workers and its relationship with the fear of COVID-19 and the COVID-19 burden after the full liberalization of COVID-19 prevention and control policy in China. Methods: This study was conducted three years after the global COVID-19 pandemic (January 15 to January 16, 2023). This study was conducted via the Wenjuanxing platform and used the Chinese versions of the scales PC-PTSD-5, COVID-19 Anxiety Scale (FCV-19S), Social Support Scale, COVID-19 Stress Scale, GAD-2, and PHQ-2. Results: The prevalence of PTSD symptoms in health care workers was 24.3% (232/955). depression(P < 0.001), anxiety(P < 0.05), the fear of COVID-19(P < 0.001), and COVID-19 burden(P < 0.001) were highly correlated with PTSD symptoms in health care workers. Social support(P < 0.05) was a protective factor of PTSD symptoms. Conclusions: This survey shows that PTSD symptoms were highly prevalent among Chinese health care workers after the COVID-19 pandemic. Governments and leaders of medical institutions should, through psychological interventions, address the current situation of PTSD symptoms among health care workers and develop targeted programs and strategies to reduce their psychological problems. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, official social media became a critical channel for the public to obtain pandemic information. No matter the positive function or negative effect of information dissemination, it involves the public's risk perception and behavior. This study was designed to contribute to the existing research on how official social media information quality (IQ) and risk perception (RP) affect preventive behavior (PB) and continued use behavior (CB) of official social media during the first wave of COVID-19.Methods: The required data were extracted from a national online survey of the Chinese Mainland during March 24-30 2020, a random sample was asked to participate in the survey (n = 666). Data analysis was performed using regression analysis, structural equation modeling, mediating effect analysis, and one-way ANOVA analysis.Results: The results show that IQ (10.010 ± 3.568) has direct and indirect positive impact on PB (9.475 ± 3.571), and has a low significant positive indirect impact on CB (3.739 ± 1.566). The IQ has a significant positive impact on RP (β = 0.548), which show that there is no "risk perception paradox" in COVID-19. Furth more, this study also provides new evidence indicating that RP mediates the relationship between IQ and PB. According to the region, gender, age and annual income, and there are significant differences in PB and CB.Conclusion: The study findings have remarkable implications for improving the information quality and public behaviors. Too high or too low level of risk perception is not conducive to pandemic prevention and control. Official social media should indirectly affect information flow through the reasonable supply of pandemic information and constantly improve the quality of pandemic information to avoid public's undue panic and excessive health concerns during this ongoing outbreak and subsequent national public emergency events. [ABSTRACT FROM AUTHOR]

The formulation of substrates with reliable quantitative detection and robust molecular anchoring is crucial for surface-enhanced Raman scattering (SERS) technique. In this context, an appealing hydrophilic-hydrophobic polymer strip with Raman internal standard (IS) was generated by in-situ growing hydrophilic non-metallic black phosphorus/metal-organic frameworks (BP/ZIF-67) onto special regions of hydrophobic polydimethylsiloxane (PDMS) matrix, which dramatically promoted the concentration of target molecules. Furthermore, a specific detection of coronavirus disease 2019 (SARS-CoV-2) N-protein with a limit of 6.4 × 10−6 mg/mL was facilitated, benefiting from the selective capture rendered by the mesoporous surface of ZIF-67 and the ultra-high enhancement effect arose from BP/ZIF-67 hybrid structure-triggered charge transfer resonance process. Moreover, the extent of quantification in the SERS assay was significantly remedied due to the IS strategy. Especially, the slight local surface plasmon resonance (LSPR) capability of BP could readily provoke additional electromagnetic field to complement its powerful chemical enhancement, which was significantly superior to other non-metallic substrates. Such an upgraded platform may deliver another prospering technology for dependable virus monitoring. [Display omitted] • SERS-active polymer strip with BP/ZIF-67 for straightforward virus detection. • Comprehensive explanation of chemical-electromagnetic synergistic enrichment mechanism. • Quantitative detection of SARS-CoV-2 with LOD of 6.4 × 10−6 mg/mL was achieved. [ABSTRACT FROM AUTHOR]

The lockdown policy deals a severe blow to the economy and greatly reduces the nitrogen oxides (NOx) emission in China when the coronavirus 2019 spreads widely in early 2020. Here we use satellite observations from Tropospheric Monitoring Instrument to study the year-round variation of the nitrogen dioxide (NO 2) tropospheric vertical column density (TVCD) in 2020. The NO 2 TVCD reveals a sharp drop, followed by small fluctuations and then a strong rebound when compared to 2019. By the end of 2020, the annual average NO 2 TVCD declines by only 3.4% in China mainland, much less than the reduction of 24.1% in the lockdown period. On the basis of quantitative analysis, we find the rebound of NO 2 TVCD is mainly caused by the rapid recovery of economy especially in the fourth quarter, when contribution of industry and power plant on NO 2 TVCD continues to rise. This revenge bounce of NO 2 indicates the emission reduction of NOx in lockdown period is basically offset by the recovery of economy, revealing the fact that China's economic development and NOx emissions are still not decoupled. More efforts are still required to stimulate low-pollution development. [ABSTRACT FROM AUTHOR]

Background: The emergency of Omicron variants, spreading in China and worldwide, has sparked a new wave of the coronavirus disease 2019 (COVID-19) pandemic. The high infectivity and persistence of the pandemic may trigger some degrees of post-traumatic stress disorder (PTSD) for nursing students experiencing indirect trauma exposure to the epidemic, which hinders the role transition from students to qualified nurses and exacerbates the health workforce shortage. Thus, it's well worth an exploration to understand PTSD and its underlying mechanism. Specifically, PTSD, social support, resilience, and fear of COVID-19 were selected after widely literature review. This study aimed to investigate the relationship between social support and PTSD among nursing students during COVID-19, to address the mediating role of resilience and fear of COVID-19 between social support and PTSD, and to provide practical guidance for nursing students' psychological intervention. Methods: From April 26 to April 30, 2022, 966 nursing students from Wannan Medical College were selected by the multistage sampling method to fill the Primary Care PTSD Screen for the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), Brief Resilience Scale, Fear of COVID-19 Scale, and Oslo 3 Items Social Support Scale. Data were analyzed by descriptive statistics, spearman's correlation analysis, regression analysis, and path analysis. Results: 15.42% of nursing students had PTSD. There were significant correlations between social support, resilience, fear of COVID-19, and PTSD (r =-0.291 ~ 0.353, P <0.001). Social support had a direct negative effect on PTSD (β =-0.216; 95% confidence interval, CI: -0.309~-0.117), accounting for 72.48% of the total effect. Analysis of mediating effects revealed that social support influenced PTSD through three indirect pathways: the mediated effect of resilience was statistically significant (β =-0.053; 95% CI: -0.077~-0.031), accounting for 17.79% of the total effect; the mediated effect of fear of COVID-19 was statistically significant (β =-0.016; 95% CI: -0.031~-0.003), accounting for 5.37% of the total effect; the chain mediating effect of resilience and fear of COVID-19 was statistically significant (β =-0.013; 95% CI: -0.022~-0.006), accounting for 4.36% of the total effect. Conclusion: The social support of nursing students not only directly affects PTSD, but also indirectly affects PTSD through the separate and chain mediating effect of resilience and fear of COVID-19. The compound strategies targeted at boosting perceived social support, fostering resilience, and controlling fear of COVID-19 are warranted for reducing PTSD. [ABSTRACT FROM AUTHOR]

Background: Insomnia has become an important issue in recent years. Insomnia is affected by many factors. Previous research has shown that during the COVID-19 pandemic, there would be a long-term negative effect on the mental health of medical college students. The state of medical college students' insomnia directly determines the results of medical education and the career development prospects of the medical students themselves. Therefore, it is very important to understand the insomnia situation of medical students in the post-epidemic era. Methods: This study was conducted 2 years after the global COVID-19 pandemic (April 1-April 23, 2022). The study used an online questionnaire, administered through a web-based survey platform. The Athens Insomnia Scale (AIS), Fear of COVID‐19 Scale (FCV-19S), GAD-2, PHQ-2, and socio-demographic information were surveyed by the Questionnaire Star platform. Results: The prevalence of insomnia was 27.80% (636/2289). Grade(P < 0.05), age(P < 0.001), loneliness(P < 0.001), depression(P < 0.001), anxiety(P < 0.001), fear of COVID-19 was highly correlated with insomnia (P < 0.001). Adapting to online class(P < 0.001) was a protective factor of smartphone addiction. Conclusions: This survey shows that Insomnia was highly prevalent among the Chinese medical college students during the COVID-19 pandemic. Governments and schools should through psychological interventions to address the current situation of insomnia among medical students, and formulate targeted programs and strategies to reduce their psychological problems. [ABSTRACT FROM AUTHOR]

The Coronavirus disease 2019 (COVID-19) pandemic brings great challenges to the public health and social economics around the world. As the pandemic continues and the mass vaccination goes on, monitoring the antibodies is particularly important for the epidemiological survey and vaccine assessment. Here, we developed a luciferase immunoprecipitation assay combined with an automated platform to detect anti-Receptor Binding Domain (RBD) antibody, where protein A and protein G modified magnetic beads were used to capture antibodies in serum samples and SARS-CoV-2 RBD was fused with Gaussia luciferase to label the captured target antibodies. The whole detection procedure can be completed within 20 min. The developed assay has proven up to 32 times more sensitive than ELISA for the detection of RBD antibodies. Furthermore, the results of the antibody detection of sera from vaccination as well as convalescence displayed good performance. The automated platform may provide a powerful tool for the control of COVID-19 pandemic by vaccination and the research of SARS-CoV-2 seroconversion. [Display omitted] • Fast and sensitive detection of SARS-CoV-2 antibodies. • Luciferase immunoprecipitation system combined with automatic platform. • Detecting RBD specific antibodies which can predict the viral neutralizing capability. [ABSTRACT FROM AUTHOR]

Aim: We aim to investigate the prevalence and associated factors for compassion fatigue among nurses in Fangcang Shelter Hospitals in Wuhan. Studies have shown that compassion fatigue was more common among nurses than other health‐care providers, and its predictors were also different. In recent years, most studies have investigated compassion fatigue in emergency and oncology nurses, whereas there is little information on compassion fatigue among nurses from the frontline of Fangcang Shelter Hospitals during the COVID‐19 pandemic. Methods: A descriptive, cross‐sectional design was used in this study. An online survey was conducted among nurses (n = 972) of five Fangcang Shelter Hospitals in Wuhan, Hubei province, China, from 6 March to 10 March 2020. A self‐administered questionnaire including demographic information, work‐related information, General Health Questionnaire, Perceived Stress Scale and Compassion Fatigue Scale was used. Results: The prevalence of compassion fatigue among nurses in Fangcang Shelter Hospitals was moderate, and most cases were mild. There was a significant relationship between compassion fatigue and work‐related factors, mental health and perceived stress among nurses working in Fangcang Shelter Hospitals. Conclusions: Various factors contribute to compassion fatigue, including lower job satisfaction and job adaptability, less praise from patients, more fear of infection and more perceived stress. A good working atmosphere, organizational support and psychological consultation are essential to alleviate nurses' compassion fatigue during the anti‐epidemic period. Summary statement: What is already known about this topic? Nurses are particularly vulnerable to compassion fatigue among health‐care providers, and it causes billions of dollars in productivity losses to economies in different countries every year.Personal factors (professional cognition, health condition, gender etc.), working environment (working hours, working load, working system problems, professional roles etc.), mental health and stress may affect nurses' compassion fatigue. What this paper adds? Both extrinsic and intrinsic factors played a crucial role in nurses in Fangcang Shelter Hospitals who are exposed to adverse events, leading to compassion fatigue. The implications of this paper: This paper reveals a new perspective on the factors influencing compassion fatigue of nurses working in Fangcang Shelter Hospitals.We suggest that compassion fatigue among nurses in Fangcang Shelter Hospitals could be reduced by maintaining a good working atmosphere, providing organizational support, helping nurses keep a healthy mental state and reducing stress levels. [ABSTRACT FROM AUTHOR]

Purpose: Coronavirus disease 2019 (COVID‐19) is a recently declared worldwide pandemic. Triaging of patients into severe and non‐severe could further help in targeted management. "Potential severe patients" is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT‐based radiomics signature for the prediction of these potential severe COVID‐19 patients. Methods: One hundred fifty COVID‐19 patients were enrolled and randomly divided into cross‐validation and independent test sets. First, their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the inter‐ and intra‐class correlation coefficients (ICC) analysis, minimum‐redundancy maximum‐relevance (mRMR) selection, and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer–Lemeshow test and Delong test. Results: Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR, and LASSO methods resulted in the selection of ten radiomics features, which made up of the radiomics signature. The differences in the radiomics signature between the potential severe and non‐severe groups in cross‐validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models. Conclusions: A CT‐based radiomics signature for the prediction of potential severe COVID‐19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID‐19 pandemic prevention and control. [ABSTRACT FROM AUTHOR]

A new antibody diagnostic assay with more rapid and robust properties is demanded to quantitatively evaluate anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunity in a large population. Here, we developed a nanometer-scale fluorescent biosensor system consisting of CdSe-ZnS quantum dots (QDs) coupled with the highly sensitive B-cell epitopes of SARS-CoV-2 that could remarkably identify the corresponding antibody with a detection limit of 100 pM. Intriguingly, we found that fluorescence quenching of QDs was stimulated more obviously when coupled with peptides than the corresponding proteins, indicating that the energy transfer between QDs and peptides was more effective. Compared to the traditional enzyme-linked immunosorbent assay (ELISA), the B-cell-epitope-based QD-biosensor could robustly distinguish coronavirus disease 2019 (COVID-19) antibody-positive patients from uninfected individuals with a higher sensitivity (92.3–98.1% positive rates by QD-biosensor vs. 78.3–83.1% positive rates by ELISAs in 207 COVID-19 patients' sera) in a more rapid (5 min) and labor-saving manner. Taken together, the 'QD-peptides' biosensor provided a novel real-time, quantitative, and high-throughput method for clinical diagnosis and home-use tests. [ABSTRACT FROM AUTHOR]

Background: In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. Purpose: To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. Material and Methods: The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. Results: Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867–0.957) in the training dataset and 0.907 (95% CI 0.849–0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. Conclusion: The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has posed significant threats to both the physical and psychological health of healthcare workers working in the front-line combating COVID-19. However, studies regarding the medium to long term impact of COVID-19 on mental health among healthcare workers are limited. Therefore, we conducted this cross-sectional survey to investigate the prevalence, factors and impact of post-traumatic stress disorder (PTSD) in healthcare workers exposed to COVID-19 8 months after the end of the outbreak in Wuhan, China. Methods: A web-based questionnaire was delivered as a link via the communication application WeChat to those healthcare workers who worked at several COVID-19 units during the outbreak (from December 2019 to April 2020) in Wuhan, China. The questionnaire included questions on social-demographic data, the post-traumatic stress disorder checklist-5 (PCL-5), the family care index questionnaire (Adaptation, Partnership, Growth, Affection and Resolve, APGAR), and the quality-of-life scale (QOL). The prevalence, risk and protective factors, and impact of PTSD on healthcare workers were subsequently analyzed. Results: Among the 659 participants, 90 healthcare workers were still suffering from PTSD 8 months after the end of the outbreak of COVID-19 in Wuhan, in which avoidance and negative impact were the most affected dimensions. Suffering from chronic disease, experiencing social isolation, and job dissatisfaction came up as independent risk factors for PTSD, while obtaining COVID-19 related information at an appropriate frequency, good family function, and working in well-prepared mobile cabin hospitals served as protective factors. The impact of PTSD on COVID-19 exposed healthcare workers was apparent by shortened sleeping time, feeling of loneliness, poorer quality of life and intention to resign. Conclusions: Eight months after the end of the COVID-19 outbreak in Wuhan, the level of PTSD in healthcare workers exposed to COVID-19 was still high. Apart from the commonly recognized risk factors, comorbid chronic disease was identified as a new independent risk factor for developing PTSD. For countries where the pandemic is still ongoing or in case of future outbreaks of new communicable diseases, this study may contribute to preventing cases of PTSD in healthcare workers exposed to infectious diseases under such circumstances. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a radiomics nomogram for timely predicting severe COVID-19 pneumonia. Materials and methods: Three hundred and sixteen COVID-19 patients (246 non-severe and 70 severe) were retrospectively collected from two institutions and allocated to training, validation, and testing cohorts. Radiomics features were extracted from chest CT images. Radiomics signature was constructed based on reproducible features using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm with 5-fold cross-validation. Logistic regression modeling was employed to build different models based on quantitative CT features, radiomics signature, clinical factors, and/or the former combined features. Nomogram performance for severe COVID-19 prediction was assessed with respect to calibration, discrimination, and clinical usefulness. Results: Sixteen selected features were used to build the radiomics signature. The CT-based radiomics model showed good calibration and discrimination in the training cohort (AUC, 0.9; 95% CI, 0.843–0.942), the validation cohort (AUC, 0.878; 95% CI, 0.796–0.958), and the testing cohort (AUC, 0.842; 95% CI, 0.761–0.922). The CT-based radiomics model showed better discrimination capability (all p < 0.05) compared with the clinical factors joint quantitative CT model (AUC, 0.781; 95% CI, 0.708–0.843) in the training cohort, the validation cohort (AUC, 0.814; 95% CI, 0.703–0.897), and the testing cohort (AUC, 0.696; 95% CI, 0.581–0.796). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics model outperformed the clinical factors model and quantitative CT model alone. Conclusions: The CT-based radiomics signature shows favorable predictive efficacy for severe COVID-19, which might assist clinicians in tailoring precise therapy. Key Points: • Radiomics can be applied in CT images of COVID-19 and radiomics signature was an independent predictor of severe COVID-19. • CT-based radiomics model can predict severe COVID-19 with satisfactory accuracy compared with subjective CT findings and clinical factors. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings, and clinical factors can achieve better severity prediction with improved diagnostic performance. [ABSTRACT FROM AUTHOR]

Aims and Objectives: To investigate the present status of anxiety among nurses fighting the spread of COVID‐19 and its association with perceived stress and insomnia. Background: With the outbreak of COVID‐19, nurses have been caring for infected patients for a considerable length of time in Wuhan, China. Previous COVID‐19 studies generally focused on patients' medical treatment, but few considered healthcare workers' psychological needs while working with a pandemic involving an unfamiliar infectious disease. Numerous nurses have experienced mental health problems, such as anxiety. Design: The STROBE guidelines for a cross‐sectional questionnaire were implemented. Methods: An online survey of 643 frontline nurses working with COVID‐19‐infected patients was conducted from 3–10 March 2020. Sociodemographic data were collected, and the Generalized Anxiety Disorder Scale, the Chinese Perceived Stress Scale and the Athens Insomnia Scale were administered. Results: One‐third (33.4%) of participants reported anxiety, which was associated with perceived stress and insomnia among Chinese frontline nurses in Wuhan during the COVID‐19 pandemic. Significant associations were found between anxiety, perceived stress, insomnia, working four‐night shifts per week, experience working during more than two epidemics and fear of COVID‐19. Conclusions: This study found that a substantial proportion of frontline nurses caring for COVID‐19‐infected patients experienced anxiety. We recommend that nurse managers focus on working conditions and cultivate safe and satisfactory work environments. Meanwhile, frontline nurses should foster awareness of mental health and rely on online resources for psychological training to alleviate anxiety. Relevance to clinical practice: The findings of this study could facilitate better understanding of anxiety among frontline nurses; more importantly, healthcare authorities and nursing managers need to pay more attention to ensuring intervention training to reduce anxiety for frontline nurses worldwide. [ABSTRACT FROM AUTHOR]

Rapid and reliable detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody can provide immunological evidence in addition to nucleic acid test for the early diagnosis and on-site screening of coronavirus disease 2019 (COVID-19). All-solid-state biosensor capable of rapid, quantitative SARS-CoV-2 antibody testing is still lacking. Herein, we propose an electronic labelling strategy of protein molecules and demonstrate SARS-CoV-2 protein biosensor employing colloidal quantum dots (CQDs)-modified electrode. The feature current peak corresponding to the specific binding reaction of SARS-CoV-2 antigen and antibody proteins was observed for the first time. The unique charging and discharging effect depending on the alternating voltage applied was ascribed to the quantum confinement, Coulomb blockade and quantum tunneling effects of quantum dots. CQDs-modified electrode could recognize the specific binding reaction between antigen and antibody and then transduce it into significant electrical current. In the case of serum specimens from COVID-19 patient samples, the all-solid-state protein biosensor provides quantitative analysis of SARS-CoV-2 antibody with correlation coefficient of 93.8% compared to enzyme-linked immunosorbent assay (ELISA) results. It discriminates patient and normal samples with accuracy of about 90%. The results could be read within 1 min by handheld testing system prototype. The sensitive and specific protein biosensor combines the advantages of rapidity, accuracy, and convenience, facilitating the implement of low-cost, high-throughput immunological diagnostic technique for clinical lab, point-of-care testing (POCT) as well as home-use test. [Display omitted] • The facile electronic labelling method through one-step ligand exchange treatment without organic cross-linkers was proposed. • All-solid-state SARS-CoV-2 protein biosensor employing CQDs-modified electrode was demonstrated. • The detailed model for the signal transduction mechanism based on the quantum effect of CQDs was elucidated. • The patient and normal samples could be discriminated with an accuracy of about 90%. The results could be read within 1 min by the handheld testing system prototype. [ABSTRACT FROM AUTHOR]

A new SARS-CoV-2 variant B.1.1.529 was named by the WHO as Omicron and classified as a Variant of Concern (VOC) on 26 November 2021. Because this variant has more than 50 mutations, including 30 mutations on the spike, it has generated a lot of concerns on the potential impacts of the VOC on COVID-19. Here through ELISA assays using the recombinant RBD proteins with sequences the same to that of SARS-CoV-2 WIV04 (lineage B.1), the Delta variant and the Omicron variant as the coating antigens, the binding capabilities between the RBDs and the antibodies in COVID-19 convalescent sera and vaccine sera after two doses of the inactivated vaccine produced by Sinopharm WIBP are compared with each other. The results showed that the Omicron variant may evade antibodies induced by the ancestral strain and by the inactivated vaccine, with significant reduction in the binding capability of its RBD much greater than that of the Delta variant. [ABSTRACT FROM AUTHOR]

Background: In this study, our focus was on pulmonary sequelae of coronavirus disease 2019 (COVID-19). We aimed to develop and validate CT-based radiomic models for predicting the presence of residual lung lesions in COVID-19 survivors at three months after discharge. Methods: We retrospectively enrolled 162 COVID-19 confirmed patients in our hospital (84 patients with residual lung lesions and 78 patients without residual lung lesions, at three months after discharge). The patients were all randomly allocated to a training set (n = 114) or a test set (n = 48). Radiomic features were extracted from chest CT images in different regions (entire lung or lesion) and at different time points (at hospital admission or at discharge) to build different models, sequentially, or in combination, as follows: (1) Lesion_A model (based on the lesion region at admission CT); (2) Lesion_D model (based on the lesion region at discharge CT); (3) Δlesion model (based on the lesion region at admission CT and discharge CT); (4) Lung_A model (based on the lung region at admission CT); (5) Lung_D model (based on the lung region at discharge CT); (6) Δlung model (based on the lung region at admission CT and discharge CT). The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the predictive performances of the radiomic models. Results: Among the six models, the Lesion_D and the Δlesion models achieved better predictive efficacy, with AUCs of 0.907 and 0.927, sensitivity of 0.898 and 0.763, and specificity of 0.855 and 0.964 in the training set, and AUCs of 0.875 and 0.837, sensitivity of 0.920 and 0.680, and specificity of 0.826 and 0.913 in the test set, respectively. Conclusions: The CT-based radiomic models showed good predictive effects on the presence of residual lung lesions in COVID-19 survivors at three months after discharge, which may help doctors to plan follow-up work and to reduce the psychological burden of COVID-19 survivors. [ABSTRACT FROM AUTHOR]