Association Between Dimensions of Professional Burnout and Turnover Intention Among Nurses Working in Hospitals During Coronavirus Disease (COVID-19) Pandemic in Iran Based on Structural Model.

Purpose: This study was done to assess the dimensions of professional burnout and turnover intention among nurses working in hospitals during the coronavirus disease 2019 (COVID-19) pandemic in Iran based on a structural model. Methods: This cross-sectional study was performed among 170 nurses working in two referral hospitals of COVID-19 in Tehran Province, Iran, from September to December 2020. Data were collected using the sociodemographic form, Maslach Burnout Inventory (MBI), and Turnover Intention Questionnaire. Data were analyzed with SPSS and Amos software version 22 using independent t-test, ANOVA, and structural equation model. Results: The mean scores for burnout in emotional fatigue, depersonalization, and personal accomplishment dimensions were 25.38 ± 7.55, 9.47 ± 4.40, and 34.94 ± 7.80, respectively, moreover for the turnover intention, the score was 6.51 ± 3.17. The reduced personal accomplishment was identified as a positive predictor of turnover intention (p = 0.01). Work position and interest in attending the organization were significantly correlated with the turnover intention (p < 0.05). Conclusions: There is an immediate need to prepare nurses to cope better with the COVID-19 outbreak. Work-related stressors during the COVID-19 pandemic have led to an increase in nurses' burnout and turnover intention. Identifying and managing the factors related to professional burnout will make it possible to prevent the nurses' turnover intention in such critical situations.

VHH Structural Modelling Approaches: A Critical Review.

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.

Structural and antigenic variations in the spike protein of emerging SARS-CoV-2 variants.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus is continuously evolving, and this poses a major threat to antibody therapies and currently authorized Coronavirus Disease 2019 (COVID-19) vaccines. It is therefore of utmost importance to investigate and predict the putative mutations on the spike protein that confer immune evasion. Antibodies are key components of the human immune system's response to SARS-CoV-2, and the spike protein is a prime target of neutralizing antibodies (nAbs) as it plays critical roles in host cell recognition, fusion, and virus entry. The potency of therapeutic antibodies and vaccines partly depends on how readily the virus can escape neutralization. Recent structural and functional studies have mapped the epitope landscape of nAbs on the spike protein, which illustrates the footprints of several nAbs and the site of escape mutations. In this review, we discuss (1) the emerging SARS-CoV-2 variants; (2) the structural basis for antibody-mediated neutralization of SARS-CoV-2 and nAb classification; and (3) identification of the RBD escape mutations for several antibodies that resist antibody binding and neutralization. These escape maps are a valuable tool to predict SARS-CoV-2 fitness, and in conjunction with the structures of the spike-nAb complex, they can be utilized to facilitate the rational design of escape-resistant antibody therapeutics and vaccines.

Research on the factors of extremely short construction period under the sufficient resources based on Grey-DEMATEL-ISM.

Under the condition of sufficient resources, there are many factors affecting the realization of extremely short construction period of engineering construction projects. Based on literature review and questionnaire survey, this paper firstly selected 17 influencing factors from the five dimensions of design, management, technology, policy and environment. And the factor analytic hierarchy process model was established based on Grey-DEMATEL-ISM. The model introduced the improved grey system theory and combined decision-making trial and evaluation laboratory (DEMATEL) with interpretative structural modeling method (ISM). In addition, the model can not only identify the critical factors in the system, but also present the internal logical relationship between the influencing factors through the multi-level hierarchical structure diagram. Finally, through the analysis of the influencing factors of extremely short construction period under the sufficient resources, it defined that the key factor is the natural environment and second is the structure type. The methodology implemented in this paper helps decision makers and managers of construction projects to understand the interrelationship and degree of influence among factors affecting the duration under the condition of sufficient resources, to effectively grasp key factors, and to effectively achieve project success.

The missing link: covalent linkages in structural models.

Covalent linkages between constituent blocks of macromolecules and ligands have been subject to inconsistent treatment during the model-building, refinement and deposition process. This may stem from a number of sources, including difficulties with initially detecting the covalent linkage, identifying the correct chemistry, obtaining an appropriate restraint dictionary and ensuring its correct application. The analysis presented herein assesses the extent of problems involving covalent linkages in the Protein Data Bank (PDB). Not only will this facilitate the remediation of existing models, but also, more importantly, it will inform and thus improve the quality of future linkages. By considering linkages of known type in the CCP4 Monomer Library (CCP4-ML), failure to model a covalent linkage is identified to result in inaccurate (systematically longer) interatomic distances. Scanning the PDB for proximal atom pairs that do not have a corresponding type in the CCP4-ML reveals a large number of commonly occurring types of unannotated potential linkages; in general, these may or may not be covalently linked. Manual consideration of the most commonly occurring cases identifies a number of genuine classes of covalent linkages. The recent expansion of the CCP4-ML is discussed, which has involved the addition of over 16 000 and the replacement of over 11 000 component dictionaries using AceDRG. As part of this effort, the CCP4-ML has also been extended using AceDRG link dictionaries for the aforementioned linkage types identified in this analysis. This will facilitate the identification of such linkage types in future modelling efforts, whilst concurrently easing the process involved in their application. The need for a universal standard for maintaining link records corresponding to covalent linkages, and references to the associated dictionaries used during modelling and refinement, following deposition to the PDB is emphasized. The importance of correctly modelling covalent linkages is demonstrated using a case study, which involves the covalent linkage of an inhibitor to the main protease in various viral species, including SARS-CoV-2. This example demonstrates the importance of properly modelling covalent linkages using a comprehensive restraint dictionary, as opposed to just using a single interatomic distance restraint or failing to model the covalent linkage at all.

DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes.

Information about macromolecular structure of protein complexes and related cellular and molecular mechanisms can assist the search for vaccines and drug development processes. To obtain such structural information, we present DeepTracer, a fully automated deep learning-based method for fast de novo multichain protein complex structure determination from high-resolution cryoelectron microscopy (cryo-EM) maps. We applied DeepTracer on a previously published set of 476 raw experimental cryo-EM maps and compared the results with a current state of the art method. The residue coverage increased by over 30% using DeepTracer, and the rmsd value improved from 1.29 Å to 1.18 Å. Additionally, we applied DeepTracer on a set of 62 coronavirus-related cryo-EM maps, among them 10 with no deposited structure available in EMDataResource. We observed an average residue match of 84% with the deposited structures and an average rmsd of 0.93 Å. Additional tests with related methods further exemplify DeepTracer's competitive accuracy and efficiency of structure modeling. DeepTracer allows for exceptionally fast computations, making it possible to trace around 60,000 residues in 350 chains within only 2 h. The web service is globally accessible at https://deeptracer.uw.edu.

Covid-19.bioreproducibility.org: A web resource for SARS-CoV-2-related structural models.

The COVID-19 pandemic has triggered numerous scientific activities aimed at understanding the SARS-CoV-2 virus and ultimately developing treatments. Structural biologists have already determined hundreds of experimental X-ray, cryo-EM, and NMR structures of proteins and nucleic acids related to this coronavirus, and this number is still growing. To help biomedical researchers, who may not necessarily be experts in structural biology, navigate through the flood of structural models, we have created an online resource, covid19.bioreproducibility.org, that aggregates expert-verified information about SARS-CoV-2-related macromolecular models. In this article, we describe this web resource along with the suite of tools and methodologies used for assessing the structures presented therein.