Subgroups of perceived professional preparedness of senior nursing students and their differences in professional identity: a latent profile analysis (preprint)

Background Senior nursing students’ perceptions of their professional preparedness help them perform their nursing role confidently and independently in the future. Therefore, it is critical to both identify the classification features of perceived professional preparedness and develop targeted interventions. Professional identity (PI) may contribute to cultivating the nursing students’ perceptions of professional preparedness, but the relationship between the two is unclear currently. This study aims to explore the subgroups of senior nursing students’ perceptions of professional preparedness and their differences in PI.Method This was a cross-sectional study. A total of 319 senior nursing students from five universities in China were enrolled. The Perceived Professional Preparedness of Senior Nursing Students’ Questionnaire (PPPNS) and the Professional Identity Scale for Nursing Students (PISNS) were adopted. Latent profile analysis (LPA) was used to analyze the latent profiles of perceived professional preparedness among senior nursing students. Multiple logistic regression was applied to explore the predictors of different profiles, and a one-way analysis of variance was conducted to compare the PI scores in each latent profile.Result Three latent profiles were identified and labeled “low perceived professional preparedness” (n = 90, 28.2%), “low clinical competency-low EBP(Evidence-Based Practice)” (n = 190, 59.5%) and “high perceived professional preparedness” (n = 39, 12.2%). The “low perceived professional preparedness” group was less likely to include those senior nursing students who worked more than 7 hours per day during the clinical practicum, resided in town and urban areas, had part-time experience, had good relationships with classmates, and felt nobility to nursing due to the COVID-19 pandemic. The average PI score was statistically different across the three profiles (F = 54.69, p < 0.001).Conclusion Promoting PI may effectively cultivate the perceived professional preparedness of senior nursing students. This study highlights the importance of targeted interventions by considering their distinct perceptions of professional preparedness patterns.

Cross-species tropism and antigenic landscapes of circulating SARS-CoV-2 variants

Zhang et al. show in vitro cross-species infectivity and neutralization-escape characteristics of 153 SARS-CoV-2 RBD mutants and 11 globally circulating VOC/VOI variants. They reveal an association between enhanced cross-species infection potential and the current cumulative prevalence of mutations, which can inform surveillance and forecasting of SARS-CoV-2 spike mutations.

EANet: Iterative Edge Attention Network for Medical Image Segmentation

Accurate and automatic segmentation of medical images can greatly assist the clinical diagnosis and analysis. However, it remains a challenging task due to (1) the diversity of scale in the medical image targets and (2) the complex context environments of medical images, including ambiguity of structural boundaries, complexity of shapes, and the heterogeneity of textures. To comprehensively tackle these challenges, we propose a novel and effective iterative edge attention network (EANet) for medical image segmentation with steps as follows. First, we propose a dynamic scale-aware context (DSC) module, which dynamically adjusts the receptive fields to extract multi-scale contextual information efficiently. Second, an edge-attention preservation (EAP) module is employed to effectively remove noise and help the edge stream focus on processing only the boundary-related information. Finally, a multi-level pairwise regression (MPR) module is designed to combine the complementary edge and region information for refining the ambiguous structure. This iterative optimization helps to learn better representations and more accurate saliency maps. Extensive experimental results demonstrate that the proposed network achieves superior segmentation performance to state-of-the-art methods in four different challenging medical segmentation tasks, including lung nodule segmentation, COVID-19 infection segmentation, lung segmentation, and thyroid nodule segmentation. The source code of our method is available at https://github.com/DLWK/EANet

A hemagglutination-based, semi-quantitative test for point-of-care determination of SARS-CoV-2 antibody levels (preprint)

Serologic, point-of-care tests to detect antibodies against SARS-CoV-2 are an important tool in the COVID-19 pandemic. The majority of current point-of-care antibody tests developed for SARS-CoV-2 rely on lateral flow assays, but these do not offer quantitative information. To address this, we developed a new method of COVID-19 antibody testing employing hemagglutination tested on a dry card, similar to that which is already available for rapid typing of ABO blood groups. A fusion protein linking red blood cells (RBCs) to the receptor-binding domain (RBD) of SARS-CoV-2 spike protein was placed on the card. 200 COVID-19 patient and 200 control plasma samples were reconstituted with O-negative RBCs to form whole blood and added to the dried protein, followed by a stirring step and a tilting step, 3-minute incubation, and a second tilting step. The sensitivity for the hemagglutination test, Euroimmun IgG ELISA test and RBD-based CoronaChek lateral flow assay was 87.0%, 86.5%, and 84.5%, respectively, using samples obtained from recovered COVID-19 individuals. Testing pre-pandemic samples, the hemagglutination test had a specificity of 95.5%, compared to 97.3% and 98.9% for the ELISA and CoronaChek, respectively. A distribution of agglutination strengths was observed in COVID-19 convalescent plasma samples, with the highest agglutination score (4) exhibiting significantly higher neutralizing antibody titers than weak positives (2) (p<0.0001). Strong agglutinations were observed within 1 minute of testing, and this shorter assay time also increased specificity to 98.5%. In conclusion, we developed a novel rapid, point-of-care RBC agglutination test for the detection of SARS-CoV-2 antibodies that can yield semi-quantitative information on neutralizing antibody titer in patients. The five-minute test may find use in determination of serostatus prior to vaccination, post-vaccination surveillance and travel screening.

Real-time monitoring epidemic trends and key mutations in SARS-CoV-2 evolution by an automated tool (preprint)

With the global epidemic of SARS-CoV-2, it is important to monitor the variation, haplotype subgroup epidemic trends and key mutations of SARS-CoV-2 over time effectively, which is of great significance to the development of new vaccines, the update of therapeutic drugs, and the improvement of detection reagents. The AutoVEM tool developed in the present study could complete all mutations detections, haplotypes classification, haplotype subgroup epidemic trends and key mutations analysis for 131,576 SARS-CoV-2 genome sequences in 18 hours on a 1 core CPU and 2G internal storage computer. Through haplotype subgroup epidemic trends analysis of 131,576 genome sequences, the great significance of the previous 4 specific sites (C241T, C3037T, C14408T and A23403G) was further revealed, and 6 new mutation sites of highly linked (T445C, C6286T, C22227T, G25563T, C26801G and G29645T) were discovered for the first time that might be related to the infectivity, pathogenicity or host adaptability of SARS-CoV-2. In brief, we proposed an integrative method and developed an efficient automated tool to monitor haplotype subgroup epidemic trends and screen out the key mutations in the evolution of SARS-CoV-2 over time for the first time, and all data could be updated quickly to track the prevalence of previous key mutations and new key mutations because of high efficiency of the tool. In addition, the idea of combinatorial analysis in the present study can also provide a reference for the mutation monitoring of other viruses.

A rapid, point of care red blood cell agglutination assay for detecting antibodies against SARS-CoV-2 (preprint)

The COVID-19 pandemic has brought the world to a halt, with cases observed around the globe causing significant mortality. There is an urgent need for serological tests to detect antibodies against SARS-CoV-2, which could be used to assess the prevalence of infection, as well as ascertain individuals who may be protected from future infection. Current serological tests developed for SARS-CoV-2 rely on traditional technologies such as enzyme-linked immunosorbent assays (ELISA) and lateral flow assays, which may lack scalability to meet the demand of hundreds of millions of antibody tests in the coming year. Herein, we present an alternative method of antibody testing that just depends on one protein reagent being added to patient serum/plasma or whole blood and a short five-minute assay time. A novel fusion protein was designed that binds red blood cells (RBC) via a single-chain variable fragment (scFv) against the H antigen and displays the receptor-binding domain (RBD) of SARS-CoV-2 spike protein on the surface of RBCs. Upon mixing of the fusion protein, RBD-scFv with recovered COVID-19 patient serum and RBCs, we observed agglutination of RBCs, indicating the patient developed antibodies against SARS-CoV-2 RBD. Given that the test uses methods routinely used in hospital clinical labs across the world, we anticipate the test can be rapidly deployed with only the protein reagent required at projected manufacturing cost at U.S. cents per test. We anticipate our agglutination assay may find extensive use in low-resource settings for detecting SARS-CoV-2 antibodies.Competing Interest StatementR.L.K. is an inventor on a provisional patent application related to the work described in the manuscript. All other authors have no competing interests.View Full Text