Predictors of self-regulation fatigue patterns in patients before total knee arthroplasty: A cross-sectional study.

BACKGROUND: Patients with total knee arthroplasty encounter stressful events that consume their coping resources, resulting in self-control fatigue. Few studies have focused on this phenomenon. AIM: To identify subgroups of patients before total knee arthroplasty according to the heterogeneous patterns of self-regulation fatigue and to analyse the predictors of subtypes. METHODS: A total of 210 patients awaiting total knee arthroplasty were enrolled. Data of demographic characteristics, clinical characteristics, psychological and social factors were collected. Latent profile analysis was employed to define the subgroups. Predictors of patterns were identified using multinomial logistic regression. RESULTS: Three latent classes were identified: the low, medium, and high self-regulation fatigue classes. For the high self-regulation fatigue class, lower levels of hope, social support, self-efficacy and education were major predictors. CONCLUSION: These predictors of patients with different levels of self-regulation fatigue provide evidence for the identification of vulnerable populations and lay a foundation for targeted interventions.

Predictors of psychological resilience trajectories in patients with knee arthroplasty: A longitudinal study.

AIMS: To identify the different classes of total knee arthroplasty patients according to the heterogenous trajectories of psychological resilience and investigate the predictors for different patterns of resilience. DESIGN: A prospective cohort study. METHODS: A total of 210 patients with total knee arthroplasty from March to December 2021 were included. Baseline assessment (T0) data were collected before surgery and included demographic, biological (clinical characteristics), psychological (psychological resilience, self-efficacy, psychological distress, hope, medical coping mode) and social (social support) factors. Resilience measurements were repeated at 3 days after surgery (T1), the date of discharge (T2), and 1 month (T3) and 3 months (T4) after discharge. Latent growth mixture modelling was employed to define different resilience trajectories. Predictors of class membership were identified using multinomial logistic regression. RESULTS: Data from 198 patients were analysed. Three latent classes were identified with similar patterns in different intercepts, showing a significant decrease in resilience from admission (T0) to 3 days after surgery (T1) followed by an increase from T1 to T4. The three trajectories of psychological resilience were named the stable-resilience class (65.66%), high-resilience class (17.68%), and low-resilience class (16.66%). Multinomial logistic regression showed that compared with the stable-resilience class, the high-resilience class was predicted by having a higher level of hope, having higher education, living in urban areas and having more children, while the low-resilience class was predicted by having lower levels of self-efficacy and hope, living in semirural areas, and having more children. CONCLUSIONS: The three trajectories indicated that surgery was the major stressor influencing patients' psychological resilience and that patients in the low-resilience class needed to be intervened. IMPACT: Predictors of patients in different classes provide evidence for the identification of vulnerable populations and lay a foundation for future research contributing to the development of targeted interventions for improving patients' psychological resilience. No patient or public contribution but the time points of investigation were decided based on our interviews with 12 total knee arthroplasty patients.

Innovations in teaching during the COVID-19 pandemic: comparisons of the impacts of different teaching approaches in psychiatric nursing on undergraduate nursing students.

BACKGROUND: Psychiatric nursing education was significantly impacted during the COVID-19 pandemic, and innovative teaching can be challenging. This study aims to compare the effectiveness of four approaches to psychiatric nursing education in the context of the pandemic. METHODS: A quasi-experimental design was adopted. Students were subjected to different teaching designs: face-to-face teaching (Class A in 2021), blended teaching with flipped classroom using roleplay (Class B in 2021), live broadcast teaching (Class A in 2022), and online blended teaching with flipped classroom using case studies (Class B in 2022). Multivariable logistic regression was used to analyse the outcomes in terms of academic performance and course workload. RESULTS: The number of valid data points was 270. The results indicated that compared with Class A in 2021, the two classes in 2022 achieved significantly higher academic performance scores, and Class B in 2021 exhibited a significantly lower workload. Compared with Class A in 2022, Class B in 2021 exhibited a significantly lower workload. Compared with Class B in 2022, Class B in 2021 exhibited a significantly lower workload and achieved lower academic performance scores. CONCLUSION: This study demonstrated that innovative teaching designs for psychiatric nursing offer advantages with regard to either facilitating academic performance or optimizing learners' task loads. Furthermore, blended learning is a promising teaching approach in the context of the COVID-19 pandemic. Future teaching initiatives could adopt student-centred constructive learning designs and ensure feasible teaching.

A DDoS Detection Method Based on Feature Engineering and Machine Learning in Software-Defined Networks.

Distributed denial-of-service (DDoS) attacks pose a significant cybersecurity threat to software-defined networks (SDNs). This paper proposes a feature-engineering- and machine-learning-based approach to detect DDoS attacks in SDNs. First, the CSE-CIC-IDS2018 dataset was cleaned and normalized, and the optimal feature subset was found using an improved binary grey wolf optimization algorithm. Next, the optimal feature subset was trained and tested in Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree, and XGBoost machine learning algorithms, from which the best classifier was selected for DDoS attack detection and deployed in the SDN controller. The results show that RF performs best when compared across several performance metrics (e.g., accuracy, precision, recall, F1 and AUC values). We also explore the comparison between different models and algorithms. The results show that our proposed method performed the best and can effectively detect and identify DDoS attacks in SDNs, providing a new idea and solution for the security of SDNs.

Workflow interruption and nurses' mental workload in electronic health record tasks: An observational study.

BACKGROUND: Workflow interruptions are common in modern work systems. Electronic health record (EHR) tasks are typical tasks involving human-machine interactions in nursing care, but few studies have examined interruptions and nurses' mental workload in the tasks. Therefore, this study aims to investigate how frequent interruptions and multilevel factors affect nurses' mental workload and performance in EHR tasks. METHODS: A prospective observational study was conducted in a tertiary hospital providing specialist and sub-specialist care from June 1st to October 31st, 2021. An observer documented nurses' EHR task interruptions, reactions and performance (errors and near errors) during one-shift observation sessions. Questionnaires were administered at the end of the electronic health record task observation to measure nurses' mental workload for the electronic health record tasks, task difficulty, system usability, professional experience, professional competency, and self-efficacy. Path analysis was used to test a hypothetical model. RESULTS: In 145 shift observations, 2871 interruptions occurred, and the mean task duration was 84.69 (SD 56.68) minutes per shift. The incidence of error or near error was 158, while 68.35% of errors were self-corrected. The total mean mental workload level was 44.57 (SD 14.08). A path analysis model with adequate fit indices is presented. There was a relationship among concurrent multitasking, task switching and task time. Task time, task difficulty and system usability had direct effects on mental workload. Task performance was influenced by mental workload and professional title. Negative affect mediated the path from task performance to mental workload. CONCLUSIONS: Nursing interruptions occur frequently in EHR tasks, come from different sources and may lead to elevated mental workload and negative outcomes. By exploring the variables related to mental workload and performance, we offer a new perspective on quality improvement strategies. Reducing harmful interruptions to decrease task time can avoid negative outcomes. Training nurses to cope with interruptions and improve competency in EHR implementation and task operation has the potential to decrease nurses' mental workload and improve task performance. Moreover, improving system usability is beneficial to nurses to mitigate mental workload.

Enhancing the work engagement of frontline nurses during the COVID-19 pandemic: the mediating role of affective commitment and perceived organizational support.

BACKGROUND: Overload and anxiety were common phenomena among frontline nurses during the pandemic. Understanding the potential pathway for fostering engagement in high-stress working conditions can provide evidence of targeted intervention to facilitate nurses' well-being and safety practices. This study aims to investigate the level of nurses' work engagement during nucleic acid collection tasks in the COVID-19 pandemic and identify its potential antecedents. METHODS: A cross-sectional design was adopted. A sample of 824 nurses who engaged in nucleic acid collection tasks completed an online self-report questionnaire between 1 March and 31 May 2022. Descriptive and path analyses were utilized to analyse the interrelationships among anxiety, perceived workload, affective commitment, perceived organizational support and work engagement. This study was conducted and reported under the guidelines for Strengthening the Reporting of Observational Studies in Epidemiology. RESULTS: The results showed that frontline nurses engaged in such tasks reported high levels of anxiety and task load and low levels of work engagement. Path analysis identified anxiety symptoms, perceived workload, perceived organizational support, and affective commitment as associated with work engagement, and among these factors, perceived organizational support and affective commitment played key roles in mediating the relationship of anxiety, workload and work engagement in high-stress working conditions. CONCLUSIONS: Affective commitment and perceived organizational support were associated with frontline nurses' level of work engagement during the COVID-19 pandemic; these two variables might explain how engagement is generated in high-anxiety and high-workload situations. When healthcare organizations give more attention to frontline nurses' physical and psychological conditions and are able to innovatively motivate affective commitment and facilitate organizational support, nurses' work engagement in high-level tasks may increase, thus enhancing work safety and personal well-being.

Mental workload of frontline nurses aiding in the COVID-19 pandemic: A latent profile analysis.

AIMS: To investigate the mental workload level of nurses aiding the most affected area during the Coronavirus disease 2019 (COVID-19) pandemic and explore the subtypes of nurses regarding their mental workload. DESIGN: Cross-sectional study. METHODS: A sample of 446 frontline nurses participated from March 8 to 19, 2020. A latent profile analysis was performed to identify clusters based on the six subscales of the Chinese version of the National Aeronautics and Space Administration Task Load Index. The differences among the classes and the variables including sociodemographic characteristics, psychological capital and coping style were explored. RESULTS: The level of mental workload indicates that the nurses had high self-evaluations of their performance while under extremely intensive task loads. The following three latent subtypes were identified: 'low workload & low self-evaluation' (8.6%); 'medium workload & medium self-evaluation' (35.3%) and 'high workload & high self-evaluation' (56.1%) (Classes 1, 2, and 3, respectively). Nurses with shared accommodations, fewer years of practice, junior professional titles, lower incomes, nonmanagement working positions, lower psychological capital levels and negative coping styles had a higher likelihood of belonging to Class 1. In contrast, senior nurses with higher psychological capital and positive coping styles were more likely to belong to Classes 2 and 3. CONCLUSION: The characteristics of the 'low workload & low self-evaluation' subtype suggest that attention should be paid to the work pressure and psychological well-being of junior nurses. Further research on regular training program of public health emergency especially for novices is needed. Personnel management during public health events should be focused on the allocation between novice and senior frontline nurses. IMPACT: This study addresses the level of mental workload of frontline nurses who aid in the most severe area of the COVID-19 pandemic in China and delineates the characteristics of the subtypes of these nurses.

The Proposed Molecular Mechanisms Used by Archaea for Fe(III) Reduction and Fe(II) Oxidation.

Iron (Fe) is the fourth most abundant element in the Earth's crust where ferrous Fe [Fe(II)] and ferric Fe [Fe(III)] can be used by archaea for energy conservation. In these archaea-Fe interactions, Fe(III) serves as terminal electron acceptor for anaerobic respiration by a variety of archaea, while Fe(II) serves as electron donor and/or energy sources for archaeal growth. As no Fe is incorporated into the archaeal cells, these redox reactions are referred to as dissimilatory Fe(III) reduction and Fe(II) oxidation, respectively. Dissimilatory Fe(III)-reducing archaea (FeRA) and Fe(II)-oxidizing archaea (FeOA) are widespread on Earth where they play crucial roles in biogeochemical cycling of not only Fe, but also carbon and sulfur. To reduce extracellular Fe(III) (oxyhydr)oxides, some FeRA transfer electrons directly to the Fe(III) (oxyhydr)oxides most likely via multiheme c-type cytochromes (c-Cyts). These multiheme c-Cyts may form the pathways similar to those found in bacteria for transferring electrons from the quinone/quinol pool in the cytoplasmic membrane to the Fe(III) (oxyhydr)oxides external to the archaeal cells. Use of multiheme c-Cyts for extracellular Fe(III) reduction by both Domains of Archaea and Bacteria emphasizes an ancient mechanism of extracellular electron transfer, which is well conserved. Other FeRA, however, reduce Fe(III) (oxyhydr)oxides indirectly via electron shuttles. Similarly, it is proposed that FeOA use pathways to oxidize Fe(II) on the surface of the cytoplasmic membrane and then to transfer the released electrons across the cytoplasmic membrane inward to the O2 and NAD+ in the cytoplasm. In this review, we focus on the latest understandings of the molecular mechanisms used by FeRA and FeOA for Fe(III) reduction and Fe(II) oxidation, respectively.

Biofilm Biology and Engineering of Geobacter and Shewanella spp. for Energy Applications.

Geobacter and Shewanella spp. were discovered in late 1980s as dissimilatory metal-reducing microorganisms that can transfer electrons from cytoplasmic respiratory oxidation reactions to external metal-containing minerals. In addition to mineral-based electron acceptors, Geobacter and Shewanella spp. also can transfer electrons to electrodes. The microorganisms that have abilities to transfer electrons to electrodes are known as exoelectrogens. Because of their remarkable abilities of electron transfer, Geobacter and Shewanella spp. have been the two most well studied groups of exoelectrogens. They are widely used in bioelectrochemical systems (BESs) for various biotechnological applications, such as bioelectricity generation via microbial fuel cells. These applications mostly associate with Geobacter and Shewanella biofilms grown on the surfaces of electrodes. Geobacter and Shewanella biofilms are electrically conductive, which is conferred by matrix-associated electroactive components such as c-type cytochromes and electrically conductive nanowires. The thickness and electroactivity of Geobacter and Shewanella biofilms have a significant impact on electron transfer efficiency in BESs. In this review, we first briefly discuss the roles of planktonic and biofilm-forming Geobacter and Shewanella cells in BESs, and then review biofilm biology with the focus on biofilm development, biofilm matrix, heterogeneity in biofilm and signaling regulatory systems mediating formation of Geobacter and Shewanella biofilms. Finally, we discuss strategies of Geobacter and Shewanella biofilm engineering for improving electron transfer efficiency to obtain enhanced BES performance.

Effect of Bushen Jianpi formula on survival of patients with moderate and advanced hepatocellular carcinoma: a retrospective study.

OBJECTIVE: To investigate the effect of Bushen Jianpi formula (BSJP) therapy on survival of the patients with moderate and advanced hepatocellular carcinoma (HCC). METHODS: This was a five-year retrospective study, enrolling 209 adult patients with moderate and advanced HCC patients. Log-rank test, Cox regression analysis and Kaplan-Meier analysis was applied to analyze the data. RESULTS: The outcome of single-factor Kaplan-Meier analysis and Log-rank test showed that stage of Barcelona-clinic liver cancer (BCLC), locoregional therapy and BSJP therapy were three factors that dominantly influenced the survival of moderate and advanced HCC patients (P < 0.05). In Cox multiple-factor regression analysis, lower BCLC staging (RR = 2.016; P = 0.000), locoregional therapy (RR = 0.611; P = 0.011) and BSJP therapy (RR = 0.684; P = 0.044) were the independent protective factors affecting survival of patients, and BSJP exerted a more significant effect on patients at BCLC-C stage (RR = 0.513; P = 0.002). CONCLUSION: BSJP therapy and locoregional therapy both contributed to prolonged survival for moderate and advanced HCC patients.

Traditional Chinese medicine for post-stroke depression: A systematic review and network meta-analysis (Protocol).

BACKGROUND: Post-stroke depression (PSD) is common in stroke survivors, with significantly negative effects and serious impairments in terms of personal and social functioning. While both pharmacological and traditional Chinese medicine (TCM) interventions have been administered for PSD, there is still uncertainty about the balance between these and what treatment strategy should be preferred in clinical practice. Therefore, we aim to compare and rank, describing the protocol of a systematic review and network meta-analysis (NMA), the commonly used TCM interventions for PSD. METHODS AND ANALYSIS: We will search CENTRAL (the Cochrane Central Register of Controlled Trials), CINAHL, Embase, PubMed, CBM and PsycINFO, the US National Institutes of Health and the World Health Organisation International Trials Registry Platform search portal from inception to November 2018. There will be no restrictions on language, publication year or publication type. Only randomized clinical trials (RCTs) accessing any TCM treatments against active comparator or other controls for PSD will be included. The primary outcomes will be efficacy (the total number of participants, declining more than 50% on the total score between baseline) and acceptability of treatment (dropout rate due to any cause). A Bayesian NMA will be performed to compare all relative outcome of different TCM interventions. we will conduct the network meta-regression meta-analyses of data on the sex ratio, the types of stroke and the treatment duration of TCM interventions. Potential explanations in extra subgroup analyses according to the results of heterogeneity and inconsistency will be explored, and sensitivity analyses will be conducted to assess the robustness of the findings. TRIALS REGISTRATION NUMBER: PROSPERO CRD42018082400. CONCLUSION: Our study will generate evidence for TCM in the treatment of PSD and help to reduce the uncertainty about the effectiveness of PSD management, which will encourage further suggestions for TCM clinical practice or guideline.

Transcriptome Analysis Reveals Long Intergenic Noncoding RNAs Contributed to Growth and Meat Quality Differences between Yorkshire and Wannanhua Pig.

There are major differences between Yorkshire (lean-type) and Wannanhua pig (fat-type) in terms of growth performance and meat quality. Long intergenic noncoding RNAs (lincRNAs) are a class of regulators that are involved in numerous biological processes and widely identified in many species. However, the role of lincRNAs in pig is largely unknown, and the mechanisms by which they affect growth and meat quality are elusive. In this study, we used published data to identify 759 lincRNAs in porcine longissimus dorsi muscle. These putative lincRNAs shared many features with mammalian lincRNAs, such as shorter length and fewer exons. Gene ontology and pathway analysis indicated that many potential target genes (PTGs) of lincRNAs were involved in muscle growth-related and meat quality-related biological processes. Moreover, we constructed a co-expression network between differentially expressed lincRNAs (DELs) and their PTGs, and found a potential mechanism that most DELs can use to upregulate their PTGs, which may finally contribute to the growth and meat quality differences between the two breeds through an unknown manner. This work details some lincRNAs and their PTGs related to muscle growth or meat quality, and facilitates future research on the roles of lincRNAs in these two types of pig, as well as molecular-assisted breeding for pig.