Nurses' preparedness to respond to COVID-19 and associated factors after the outbreak in China.

AIM: Nurses are key staff in the response to the COVID-19 epidemic. The aim of present study was to assess Chinese clinical nurses' preparedness levels for COVID-19 after the outbreak, as well as the associated demographic factors. DESIGN: The design was a cross-sectional survey. METHODS: We distributed an online questionnaire to nurses from five eastern coastal area hospitals. The questionnaire collected demographic information, and included the nurses' preparedness to respond to COVID-19 questionnaire (NPR COVID-19). RESULTS: The total mean NPR COVID-19 score was 200.99 (standard deviation = 33.60), and the psychological approaches subscale had the lowest mean score. Education and training were positively associated with the NPR COVID-19 score. Nurses' characteristics, such as seniority, job category and educational level, were entered into the NPR COVID-19 regression model, and seniority (≤5 years) showed the strongest negative association with NPR COVID-19 scores (standard coefficient = -0.20). CONCLUSIONS: Chinese nurse's preparedness to respond to COVID-19 was adequate. Nurses with less than 5 years of work experience, nursing researchers and diploma-educated nurses reported feeling a low preparedness to respond to COVID-19. These nurses should receive specific training.

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study.

BACKGROUND: To date, performance comparisons between men and machines have been carried out in many health domains. Yet machine learning (ML) models and human performance comparisons in audio-based respiratory diagnosis remain largely unexplored. OBJECTIVE: The primary objective of this study was to compare human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. METHODS: In this study, we compared human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. Prediction performance on 24 audio samples (12 tested positive) made by 36 clinicians with experience in treating COVID-19 or other respiratory illnesses was compared with predictions made by an ML model trained on 1162 samples. Each sample consisted of voice, cough, and breathing sound recordings from 1 subject, and the length of each sample was around 20 seconds. We also investigated whether combining the predictions of the model and human experts could further enhance the performance in terms of both accuracy and confidence. RESULTS: The ML model outperformed the clinicians, yielding a sensitivity of 0.75 and a specificity of 0.83, whereas the best performance achieved by the clinicians was 0.67 in terms of sensitivity and 0.75 in terms of specificity. Integrating the clinicians' and the model's predictions, however, could enhance performance further, achieving a sensitivity of 0.83 and a specificity of 0.92. CONCLUSIONS: Our findings suggest that the clinicians and the ML model could make better clinical decisions via a cooperative approach and achieve higher confidence in audio-based respiratory diagnosis.

Analysis of the anxiety level and influencing factors during the coronavirus disease 2019 epidemic among the parents of students in China.

Background: Due to the outbreak of Coronavirus Disease 2019, there has been a significant impact on the mental health of parents. However, no detailed study on the mental health status of parents has been conducted to date. Methods: This study was a cross-sectional used a whole-group random sampling method to conduct an online questionnaire survey with 102,883 parents in Guangdong Province, China, April 25, 2020 and May 14, 2020. Anxiety was assessed by using the Generalized Anxiety Disorder tool (GAD-7). Potential factors of anxiety were estimated using univariate analysis and multivariate logistic regression analysis by SPSS 22.0 statistical software. Results: Among the total 94,705 parents who have completed the questionnaire survey (92.05% response rate). The incidence of anxiety was 23.77%. Parents' anxiety symptoms are more likely to be caused by female family roles, higher levels of education, unemployed or jobless employment status, children not being an only child, and children having negative attitudes toward online courses. Conclusions: Our research shows that most parents experienced mild anxiety during the Coronavirus Disease 2019 epidemic. Our findings provide strong evidence for investigating and focusing on the mental health of this population during the COVID-19 epidemic. Therefore, governments and healthcare departments at all levels should actively provide psychological counseling services to relieve their anxiety symptoms.

Parenting and parenting resources among Chinese parents with children under three years of age: rural and urban differences.

BACKGROUND: Parenting is essential for children's development and preventing child abuse and neglect. Providing parenting services within the primary health care settings demonstrated effectiveness in improving parenting quality. However, little is known about the status of parenting and parenting resources in rural areas and whether they differ between rural and urban areas in Mainland China. OBJECTIVE: This study aimed to examine the rural-urban differences in parenting and availability of, utilization of, and need for parenting resources among Chinese parents with children under three years of age. PARTICIPANTS AND SETTING: A total of 425 parents of children under three years of age participated in an online survey between March and May 2020. METHODS: The Parenting and Family Adjustment Scale and Child Adjustment and Parenting Efficacy Scale were used to assess parenting, family adjustment, and parenting efficacy. The availability of, utilization of, and need for parenting resources were measured using self-developed questions based on literature. Chi-square tests, t tests, and Wilcoxon rank-sum test were used to examine the differences in responses between parents in rural and urban areas. RESULTS: Compared with their urban counterparts, rural parents reported a higher level of negative parenting and more limited parenting resources. Both rural and urban parents reported low availability and utilization of parenting resources as well as a great need for parenting support services. CONCLUSIONS: Rural parents faced more parenting challenges and limited parenting resources compared with urban parents. Both rural and urban parents with children under three years of age reported great needs for parenting resources. These findings highlight the potential of delivering accessible, sustainable, and cost-effective parenting programs via the primary health care system for public welfare in both urban and rural areas, with more attention paid to rural parents to help them improve their parenting.

A summary of the ComParE COVID-19 challenges.

The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals' respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS).

A case study of HIV/AIDS services from community-based organizations during COVID-19 lockdown in China.

INTRODUCTION: People living with HIV (PLHIV) relied on community-based organizations (CBOs) in accessing HIV care and support during the COVID-19 pandemic in China. However, little is known about the impact of, and challenges faced by Chinese CBOs supporting PLHIV during lockdowns. METHODS: A survey and interview study was conducted among 29 CBOs serving PLHIV in China between November 10 and November 23, 2020. Participants were asked to complete a 20-minute online survey on their routine operations, organizational capacity building, service provided, and challenges during the pandemic. A focus group interview was conducted with CBOs after the survey to gather CBOs' policy recommendations. Survey data analysis was conducted using STATA 17.0 while qualitative data was examined using thematic analysis. RESULTS: HIV-focused CBOs in China serve diverse clients including PLHIV, HIV high-risk groups, and the public. The scope of services provided is broad, ranging from HIV testing to peer support. All CBOs surveyed maintained their services during the pandemic, many by switching to online or hybrid mode. Many CBOs reported adding new clients and services, such as mailing medications. The top challenges faced by CBOs included service reduction due to staff shortage, lack of PPE for staff, and lack of operational funding during COVID-19 lockdowns in 2020. CBOs considered the ability to better network with other CBOs and other sectors (e.g., clinics, governments), a standard emergency response guideline, and ready strategies to help PLHIV build resilience to be critical for future emergency preparation. CONCLUSION: Chinese CBOs serving vulnerable populations affected by HIV/AIDS are instrumental in building resilience in their communities during the COVID-19 pandemic, and they can play significant roles in providing uninterrupted services during emergencies by mobilizing resources, creating new services and operation methods, and utilizing existing networks. Chinese CBOs' experiences, challenges, and their policy recommendations can inform policy makers on how to support future CBO capacity building to bridge service gaps during crises and reduce health inequalities in China and globally.

In silico analyses of diversity and dissemination of antimicrobial resistance genes and mobile genetics elements, for plasmids of enteric pathogens.

Introduction: The antimicrobial resistance (AMR) mobilome plays a key role in the dissemination of resistance genes encoded by mobile genetics elements (MGEs) including plasmids, transposons (Tns), and insertion sequences (ISs). These MGEs contribute to the dissemination of multidrug resistance (MDR) in enteric bacterial pathogens which have been considered as a global public health risk. Methods: To further understand the diversity and distribution of AMR genes and MGEs across different plasmid types, we utilized multiple sequence-based computational approaches to evaluate AMR-associated plasmid genetics. A collection of 1,309 complete plasmid sequences from Gammaproteobacterial species, including 100 plasmids from each of the following 14 incompatibility (Inc) types: A/C, BO, FIA, FIB, FIC, FIIA, HI1, HI2, I1, K, M, N, P except W, where only 9 sequences were available, was extracted from the National Center for Biotechnology Information (NCBI) GenBank database using BLAST tools. The extracted FASTA files were analyzed using the AMRFinderPlus web-based tools to detect antimicrobial, disinfectant, biocide, and heavy metal resistance genes and ISFinder to identify IS/Tn MGEs within the plasmid sequences. Results and Discussion: In silico prediction based on plasmid replicon types showed that the resistance genes were diverse among plasmids, yet multiple genes were widely distributed across the plasmids from enteric bacterial species. These findings provide insights into the diversity of resistance genes and that MGEs mediate potential transmission of these genes across multiple plasmid replicon types. This notion was supported by the observation that many IS/Tn MGEs and resistance genes known to be associated with them were common across multiple different plasmid types. Our results provide critical insights about how the diverse population of resistance genes that are carried by the different plasmid types can allow for the dissemination of AMR across enteric bacteria. The results also highlight the value of computational-based approaches and in silico analyses for the assessment of AMR and MGEs, which are important elements of molecular epidemiology and public health outcomes.

Important revelations of different degrees of COVID-19 lockdown on improving regional air quality: a case study of Shijiazhuang, China.

To control the spread of COVID-19, Shijiazhuang implemented two lockdowns of different magnitudes in 2020 (lockdown I) and 2021 (lockdown II). We analyzed the changes in air quality index (AQI), PM2.5, O3, and VOCs during the two lockdowns and the same period in 2019 and quantified the effects of anthropogenic sources during the lockdowns. The results show that AQI decreased by 13.2% and 32.4%, and PM2.5 concentrations decreased by 12.9% and 42.4% during lockdown I and lockdown II, respectively, due to the decrease in urban traffic mobility and industrial activity levels. However, the sudden and unreasonable emission reductions led to an increase in O3 concentrations by 160.6% and 108.4%, respectively, during the lockdown period. To explore the causes of the O3 surge, the major precursors NOx and VOCs were studied separately, and the main VOCs species affecting ozone formation during the lockdown period and the source variation of VOCs were identified, and it is important to note that the relationship between diurnal variation characteristics of VOCs and cooking became apparent during the lockdown period. These findings suggest that regional air quality can be improved by limiting production, but attention should be paid to the surge of O3 caused by unreasonable emission reductions, clarifying the control priorities for urban O3 management.

Transformer for one stop interpretable cell type annotation.

Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated. A series of useful single cell analysis tools based on autoencoder architecture have been developed but these struggle to strike a balance between depth and interpretability. Here, we present TOSICA, a multi-head self-attention deep learning model based on Transformer that enables interpretable cell type annotation using biologically understandable entities, such as pathways or regulons. We show that TOSICA achieves fast and accurate one-stop annotation and batch-insensitive integration while providing biologically interpretable insights for understanding cellular behavior during development and disease progressions. We demonstrate TOSICA's advantages by applying it to scRNA-seq data of tumor-infiltrating immune cells, and CD14+ monocytes in COVID-19 to reveal rare cell types, heterogeneity and dynamic trajectories associated with disease progression and severity.

Biparatopic antibody BA7208/7125 effectively neutralizes SARS-CoV-2 variants including Omicron BA.1-BA.5.

SARS-CoV-2 Omicron subvariants have demonstrated extensive evasion from monoclonal antibodies (mAbs) developed for clinical use, which raises an urgent need to develop new broad-spectrum mAbs. Here, we report the isolation and analysis of two anti-RBD neutralizing antibodies BA7208 and BA7125 from mice engineered to produce human antibodies. While BA7125 showed broadly neutralizing activity against all variants except the Omicron sublineages, BA7208 was potently neutralizing against all tested SARS-CoV-2 variants (including Omicron BA.1-BA.5) except Mu. By combining BA7208 and BA7125 through the knobs-into-holes technology, we generated a biparatopic antibody BA7208/7125 that was able to neutralize all tested circulating SARS-CoV-2 variants. Cryo-electron microscopy structure of these broad-spectrum antibodies in complex with trimeric Delta and Omicron spike indicated that the contact residues are highly conserved and had minimal interactions with mutational residues in RBD of current variants. In addition, we showed that administration of BA7208/7125 via the intraperitoneal, intranasal, or aerosol inhalation route showed potent therapeutic efficacy against Omicron BA.1 and BA.2 in hACE2-transgenic and wild-type mice and, separately, effective prophylaxis. BA7208/7125 thus has the potential to be an effective candidate as an intervention against COVID-19.

Liver injury in COVID-19: Holds ferritinophagy-mediated ferroptosis accountable.

Even in patients without a history of liver disease, liver injury caused by coronavirus disease 2019 (COVID-19) is gradually becoming more common. However, the precise pathophysiological mechanisms behind COVID-19's liver pathogenicity are still not fully understood. We hypothesize that inflammation may become worse by cytokine storms caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Elevated ferritin levels can initiate ferritinophagy mediated by nuclear receptor coactivator 4 (NCOA4), which leads to iron elevation, and ferroptosis. In COVID-19 patients, ferroptosis can be restricted to reduce disease severity and liver damage by targeting NCOA4-mediated ferritinophagy. To confirm the role of ferritinophagy-mediated ferroptosis in SARS-CoV-2 infection, further research is required.

Paxlovid administration in elderly patient with COVID-19 caused by Omicron BA.2.0: A case report.

RATIONALE: Paxlovid has shown the potential decreasing the hospitalization rate of mild or moderate coronavirus disease 2019 (COVID-19) and death in few of clinical trials, and is expected to the most promising medicine targeting Severe Acute Respiratory Syndrome Coronavirus 2 (SRAS-COV-2). However, there are no enough evidences to show it effectiveness for all patients with SARS-COV-2, especially among elderly patients and newest Omicron variant. PATIENT CONCERNS AND DIAGNOSIS: A 79 year's old female patient was admitted to hospital because of the moderate COVID-19 caused by the Omicron variant BA2.0. He presented the initial syndromes including Xerostomia, cough and fever. Chest computed tomography (CT) scanning at admission showed the exudation lesions on lung. The laboratory examination revealed that there are increased C-reactive protein (CRP), Ferritin and erythrocytesedimentationrate (ESR) and decreased white blood cells. INTERVENTIONS: The oral Paxlovid (Nirmatrelvir/Ritonavir) was administrated on second day after admission. OUTCOMES: The syndromes of Xerostomia, cough and fever was improved on third day after use of Paxlovid. The levels of CRP, ESR and counts of white blood cells returned the normal after three days of admission. The chest CT scanned on the third and sixth day after Paxlovid used showed the absorption of lesions. The examination of SARS-COVS viral nucleic acid turned negative at fifth day of admission. LESSONS: As a result, we would consider that Paxlovid is a suitable oral drug for elderly patients with SARS-COV2 even Omicron variant, it's benefit to improve patient's symptom and signs and can prevents COVID-19 with the high-risk factors from severe disease, although it didn't shorten the time for viral nucleic acid to turn negative.

Lung ultrasonography findings of coronavirus disease 2019 patients: Comparison between primary and secondary regions of China.

BACKGROUND: An unexplained pneumonia occurred in Wuhan, China in December 2019, later identified and named coronavirus disease 2019 (COVID-19). This study aimed to compare the ultrasonographic features of the lung between patients with COVID-19 in Wuhan (the primary region) and those in Beijing (the secondary region) and to find the value of applying ultrasound in COVID-19. METHODS: A total of 248 COVID-19 cases were collected, including long-term residents in Wuhan (138), those who had a short-term stay in Wuhan (72), and those who had never visited Wuhan (38). Ultrasound examination was performed daily; the highest lung ultrasound score (LUS) was the first comparison point, while the LUS of the fifth day thereafter was the second comparison point. The differences between overall treatment and ultrasonography of left and right lungs among groups were compared. RESULTS: The severity decreased significantly after treatment. The scores of the groups with long-term residence and short-term stay in Wuhan were higher than those of the group that had never been to Wuhan. CONCLUSION: Ultrasonography is effective for dynamic monitoring of COVID-19. The ultrasonographic features of patients in the Wuhan area indicated relatively severe disease. Thus, Wuhan was the main affected area of china.

The Cholesterol-Binding Sequence in Monomeric C-Reactive Protein Binds to the SARS-CoV-2 Spike Receptor-Binding Domain and Blocks Interaction With Angiotensin-Converting Enzyme 2.

The receptor-binding domain (RBD) of the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to the human angiotensin-converting enzyme 2 (ACE2) receptor, which is a prerequisite for the virus to enter the cell. C-reactive protein (CRP) is an important marker of inflammation and is a putative soluble pattern recognition receptor. Clinical elevation of CRP levels in patients with COVID-19 is one of the characteristics of the disease; however, whether CRP is involved in COVID-19 pathogenesis is unknown. Here, we report that monomeric CRP (mCRP) can bind to the SARS-CoV-2 spike RBD and competitively inhibit its binding to ACE2. Furthermore, truncated mutant peptide competition assays and surface plasmon resonance binding experiments showed that the cholesterol-binding sequence (CBS, amino acids 35-47) in mCRP was critical for mediating the binding of mCRP to spike RBD. In a cell model of spike RBD and ACE2 interaction, the CBS motif effectively reduced the binding of spike RBD to ACE2 overexpressed on the cell surface. Thus, this study highlights the pattern recognition function of mCRP in innate immunity and provides a preliminary theoretical basis for the development of the CBS motif in mCRP into a functional peptide with both diagnostic significance and potential therapeutic capabilities.

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues.

Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we describe publicly available audio databases that can be used for experiments, illustrate the developed ML methods proposed to date, and flag some under-considered issues which still need attention. Compared to existing surveys on the topic, we cover the latest literature, especially those audio-based COVID-19 detection studies which have gained extensive attention in the last two years. This work can help to facilitate the application of artificial intelligence in the respiratory auscultation field.

The Cholesterol-Binding Sequence in Monomeric C-Reactive Protein Binds to the SARS-CoV-2 Spike Receptor-Binding Domain and Blocks Interaction With Angiotensin-Converting Enzyme 2

The receptor-binding domain (RBD) of the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to the human angiotensin-converting enzyme 2 (ACE2) receptor, which is a prerequisite for the virus to enter the cell. C-reactive protein (CRP) is an important marker of inflammation and is a putative soluble pattern recognition receptor. Clinical elevation of CRP levels in patients with COVID-19 is one of the characteristics of the disease;however, whether CRP is involved in COVID-19 pathogenesis is unknown. Here, we report that monomeric CRP (mCRP) can bind to the SARS-CoV-2 spike RBD and competitively inhibit its binding to ACE2. Furthermore, truncated mutant peptide competition assays and surface plasmon resonance binding experiments showed that the cholesterol-binding sequence (CBS, amino acids 35-47) in mCRP was critical for mediating the binding of mCRP to spike RBD. In a cell model of spike RBD and ACE2 interaction, the CBS motif effectively reduced the binding of spike RBD to ACE2 overexpressed on the cell surface. Thus, this study highlights the pattern recognition function of mCRP in innate immunity and provides a preliminary theoretical basis for the development of the CBS motif in mCRP into a functional peptide with both diagnostic significance and potential therapeutic capabilities.

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation.

BACKGROUND: Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems. OBJECTIVE: The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. METHODS: Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning-enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95% CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels. RESULTS: We first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1%) of 21 COVID-19-positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery. CONCLUSIONS: An audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general.

Effect of a group-based acceptance and commitment therapy programme on the mental health of clinical nurses during the COVID-19 sporadic outbreak period.

AIM: To develop and implement of a group-based acceptance and commitment therapy programme in helping clinical nurses with mental health problems during the sporadic COVID-19 outbreak period. BACKGROUND: In the face of the continuing COVID-19 pandemic, clinical nurses have a high risk of mental health issues. METHODS: A quasi-experimental design was used. Two hundred twenty-six nurses were recruited from four general hospitals to receive 10 sessions of acceptance and commitment therapy programme. The Symptom Checklist-90, Perceived Stress Scale and Connor-Davidson Resilience Scale were used to assess nurses' mental health symptom, perceived stress and psychological resilience at pre-intervention and 4-week post-intervention. RESULTS: The mean attendance sessions was 5.78. The Symptom Checklist-90 score was significantly lower at post-intervention than pre-intervention (P < 0.01), and there were no significant changes of perceived stress and psychological resilience. There were significant correlations among the changed rates of mental health, perceived stress and psychological resilience (P < 0.01). CONCLUSION: The acceptance and commitment therapy programme was effective in relieving mental health symptoms for clinical nurses and could protect clinical nurses' perceived stress and psychological resilience. However, a randomized controlled trial is needed to confirm the findings. IMPLICATION FOR NURSING MANAGEMENT: To facilitate clinical nurses' psychological health in crisis situation, nursing management team should provide and allocated appropriate resources to support the healthcare providers.