The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis.

OBJECTIVE: COVID-19 is a novel, severely contagious disease with enormous negative impact on humanity as well as the world economy. An expeditious, feasible tool for detecting COVID-19 remains yet elusive. Recently, there has been a surge of interest in applying machine learning techniques to predict COVID-19 using non-image data. We have therefore undertaken a meta-analysis to quantify the diagnostic performance of machine learning models facilitating the prediction of COVID-19. MATERIALS AND METHODS: A comprehensive electronic database search for the period between January 1st, 2021 and December 3rd, 2021 was undertaken in order to identify eligible studies relevant to this meta-analysis. Summary sensitivity, specificity, and the area under receiver operating characteristic curves were used to assess potential diagnostic accuracy. Risk of bias was assessed by means of a revised Quality Assessment of Diagnostic Studies. RESULTS: A total of 30 studies, including 34 models, met all of the inclusion criteria. Summary sensitivity, specificity, and area under receiver operating characteristic curves were 0.86, 0.86, and 0.91, respectively. The purpose of machine learning models, class imbalance, and feature selection are significant covariates useful in explaining the between-study heterogeneity, in terms of both sensitivity and specificity. CONCLUSIONS: Our study findings show that non-image data can be used to predict COVID-19 with an acceptable performance. Further, class imbalance and feature selection are suggested to be incorporated whenever building models for the prediction of COVID-19, thus improving further diagnostic performance.

Antecedents and consequences of shared decision making for patients with chronic kidney diseases: A cross-sectional survey.

BACKGROUND: Chronic kidney disease (CKD) has a strong negative impact on patients. Finding ways to improve CKD patients' conditions by shared decision-making is receiving much attention. However, little attention has been paid to influencing antecedents and effects of shared decision-making. Meanwhile, as advanced technologies bring in new communication devices, effects of different types of communications used in shared decision-making need to be addressed. OBJECTIVE: This study proposes a research framework to determine the influencing antecedents of shared decision-making, and to evaluate the effects of shared decision-making on patient outcomes when they are computer-mediated and when the decision-makers communicate face-to-face. METHODS: A cross-section survey was conducted and a total of 48 valid samples were obtained. The participants were CKD Stage III, IV, or V patients who had received medical treatment in a hospital in Taiwan. The collected data were subjected to an independent t-test and partial least squares analysis to validate the research framework. RESULTS: Doctor-patient communication (DPC) and doctor-patient relationship (DPR) have no significant direct impact on patient outcomes. Nevertheless, both DPC and DPR significantly impact shared decision-making which in turn impacts patient outcomes. Moreover, patients who use computer-mediated communication were found to have significantly higher perceptions of shared decision-making than those who did not. CONCLUSIONS: The incidence and prevalence of end-stage renal disease in Taiwan are among the highest in the world. The results of this study can serve as a reference for hospitals to improve CKD patients' outcomes. Meanwhile, during the COVID-19 pandemic, this study suggested hospitals should encourage shared decision-making with computer-mediated communication to ensure that patients receive proper treatment and have the desired outcomes.

Efficacy of umbilical cord mesenchymal stromal cells for COVID-19: A systematic review and meta-analysis.

Objectives: A major challenge for COVID-19 therapy is dysregulated immune response associated with the disease. Umbilical cord mesenchymal stromal cells (UC-MSCs) may be a promising candidate for COVID-19 treatment owing to their immunomodulatory and anti-inflammatory functions. Therefore, this study aimed to evaluate the effectiveness of UC-MSCs inpatients with COVID-19. Method: Medline, Embase, PubMed, Cochrane Library, and Web of Science databases were searched to collect clinical trials concerning UC-MSCs for the treatment of COVID-19. After literature screening, quality assessment, and data extraction, a systematic review and meta-analysis of the included study were performed. Results: This systematic review and meta-analysis were prospectively registered on PROSPERO, and the registration number is CRD42022304061. After screening, 10 studies involving 293 patients with COVID-19 were eventually included. Our meta-analysis results showed that UC-MSCs can reduce mortality (relative risk [RR] =0.60, 95% confidence interval [CI]: [0.38, 0.95], P=0.03) in COVID-19 patients. No significant correlation was observed between adverse events and UC-MSC treatment (RR=0.85, 95% CI: [0.65, 1.10], P=0.22; RR=1.00, 95%CI: [0.64, 1.58], P=1.00). In addition, treatment with UC-MSCs was found to suppress inflammation and improve pulmonary symptoms. Conclusions: UC-MSCs hold promise as a safe and effective treatment for COVID-19. Systematic Review Registartion: PROSPERO, identifier CRD42022304061.

Effectiveness of Digital Cognitive Behavior Therapy for the Treatment of Insomnia: Spillover Effects of dCBT.

The effects of digital Cognitive Behavior Therapy for insomnia (dCBT-i) on sleep quality have been previously demonstrated but the spillover effects on fatigue, flow (a state of immersion in activities of interest), and cognitive flexibility remain unclear. The current study examined the effectiveness of dCBT-i. A total of 97 college students (20.96 ± 1.87 years, 73.1% female students) were randomly selected from a shortlist and divided into sleep intervention (n = 39), conventional education (n = 37), and healthy control (n = 21) groups. Task switching paradigm, Fatigue Severity Scale (FSS), Flow Experience Scale (FES), and the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) were measured pre- and post-intervention. Results show that the sleep quality of the intervention group improved, and fatigue was relieved. Participants in the sleep intervention group had increased flow experience scores post-intervention and improved cognitive flexibility. The control group's sleep quality deteriorated and fatigue level increased. dCBT-i can not only achieve a significant improvement in sleep quality and reduce fatigue, but also improve learning abilities, quality of life, flow, and cognitive flexibility. Future research should pay attention to indicators such as work efficiency, sedative use, and the durability and stability of such effects.

The Accuracy of Machine Learning Approaches Using Non-image Data for the Prediction of COVID-19: A Meta-Analysis

Objective COVID-19 is a novel, severely contagious disease with enormous negative impact on humanity as well as the world economy. An expeditious, feasible tool for detecting COVID-19 remains yet elusive. Recently, there has been a surge of interest in applying machine learning techniques to predict COVID-19 using non-image data. We have therefore undertaken a meta-analysis to quantify the diagnostic performance of machine learning models facilitating the prediction of COVID-19. Materials and Methods A comprehensive electronic database search for the period between January 1st, 2021 and December 3rd, 2021 was undertaken in order to identify eligible studies relevant to this meta-analysis. Summary sensitivity, specificity, and the area under receiver operating characteristic curves were used to assess potential diagnostic accuracy. Risk of bias was assessed by means of a revised Quality Assessment of Diagnostic Studies. Results A total of 30 studies, including 34 models, met all of the inclusion criteria. Summary sensitivity, specificity, and area under receiver operating characteristic curves were 0.86, 0.86, and 0.91, respectively. The purpose of machine learning models, class imbalance, and feature selection are significant covariates useful in explaining the between-study heterogeneity, in terms of both sensitivity and specificity. Conclusions Our study findings show that non-image data can be used to predict COVID-19 with an acceptable performance. Further, class imbalance and feature selection are suggested to be incorporated whenever building models for the prediction of COVID-19, thus improving further diagnostic performance.

A wearable eddy current based pulmonary function sensor for continuous non-contact point-of-care monitoring during the COVID-19 pandemic.

Pulmonary function testing (PFT) allows for quantitative analysis of lung function. However, as a result of the coronavirus disease 2019 (COVID-19) pandemic, a majority of international medical societies have postponed PFTs in an effort to mitigate disease transmission, complicating the continuity of care in high-risk patients diagnosed with COVID-19 or preexisting lung pathologies. Here, we describe the development of a non-contact wearable pulmonary sensor for pulmonary waveform analysis, pulmonary volume quantification, and crude thoracic imaging using the eddy current (EC) phenomenon. Statistical regression analysis is performed to confirm the predictive validity of the sensor, and all data are continuously and digitally stored with a sampling rate of 6,660 samples/second. Wearable pulmonary function sensors may facilitate rapid point-of-care monitoring for high-risk individuals, especially during the COVID-19 pandemic, and easily interface with patient hospital records or telehealth services.

Specific CD8+ TCR Repertoire Recognizing Conserved Antigens of SARS-CoV-2 in Unexposed Population: A Prerequisite for Broad-Spectrum CD8+ T Cell Immunity.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has developed variants escaping neutralization antibody immunity established against the original virus. An understanding of broad-spectrum adaptive immunity, including CD8+ T cell immunity to wide range of epitopes, could help translational efforts to improve coronavirus disease 2019 (COVID-19) prevention and therapy. However, there have been few direct studies in which such immunity exists in a population. METHODS: We selected SARS-CoV-2-conserved structural peptides that are not prone to mutation as antigens for broad-spectrum CD8+ T cell immunity. Peripheral blood mononuclear cells (PBMCs) from unexposed healthy donors were stimulated with these peptides in vitro and CD8+ T cell-specific response was monitored. The conserved peptide-specific CD8+ T cells were sorted for T cell receptor (TCR) repertoire sequencing. The presence of specific complementary determining region 3 (CDR3) clones was analyzed in a healthy cohort. RESULTS: For each structural protein, including S, E, M, N, the conserved peptides could potentially provide the largest number of major histocompatibility complex-I (MHC-I) epitopes in the Oriental and Caucasian populations. For conserved peptides from spike (S), envelope (E), membrane (M), nucleocapsid (N) proteins, we found that there were no cross-reactive memory T cells in the unexposed individuals. Instead, their T cells contain naïve TCR repertoire recognizing these conserved peptides. Using TCR sequencing and CDR3 clustering for the conserved peptides specific T cells, we found that the recovered patients had a higher proportion of TCR repertoire similar with that of specific CD8+ T cells in unexposed individuals. Meanwhile, CDR3 clones of the above T cells were widely present in the healthy population. CONCLUSIONS: This study provides evidence of broad-spectrum SARS-CoV-2 specific CD8+ TCR repertoire in unexposed healthy population, which is implicated in the development and implementation of broad-spectrum vaccines against COVID-19.

Use of personal protective equipment against coronavirus disease 2019 by healthcare professionals in Wuhan, China: cross sectional study.

OBJECTIVE: To examine the protective effects of appropriate personal protective equipment for frontline healthcare professionals who provided care for patients with coronavirus disease 2019 (covid-19). DESIGN: Cross sectional study. SETTING: Four hospitals in Wuhan, China. PARTICIPANTS: 420 healthcare professionals (116 doctors and 304 nurses) who were deployed to Wuhan by two affiliated hospitals of Sun Yat-sen University and Nanfang Hospital of Southern Medical University for 6-8 weeks from 24 January to 7 April 2020. These study participants were provided with appropriate personal protective equipment to deliver healthcare to patients admitted to hospital with covid-19 and were involved in aerosol generating procedures. 77 healthcare professionals with no exposure history to covid-19 and 80 patients who had recovered from covid-19 were recruited to verify the accuracy of antibody testing. MAIN OUTCOME MEASURES: Covid-19 related symptoms (fever, cough, and dyspnoea) and evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, defined as a positive test for virus specific nucleic acids in nasopharyngeal swabs, or a positive test for IgM or IgG antibodies in the serum samples. RESULTS: The average age of study participants was 35.8 years and 68.1% (286/420) were women. These study participants worked 4-6 hour shifts for an average of 5.4 days a week; they worked an average of 16.2 hours each week in intensive care units. All 420 study participants had direct contact with patients with covid-19 and performed at least one aerosol generating procedure. During the deployment period in Wuhan, none of the study participants reported covid-19 related symptoms. When the participants returned home, they all tested negative for SARS-CoV-2 specific nucleic acids and IgM or IgG antibodies (95% confidence interval 0.0 to 0.7%). CONCLUSION: Before a safe and effective vaccine becomes available, healthcare professionals remain susceptible to covid-19. Despite being at high risk of exposure, study participants were appropriately protected and did not contract infection or develop protective immunity against SARS-CoV-2. Healthcare systems must give priority to the procurement and distribution of personal protective equipment, and provide adequate training to healthcare professionals in its use.

Educational needs in the COVID-19 pandemic: a Delphi study among doctors and nurses in Wuhan, China.

OBJECTIVE: To identify theoretical and technical aspects regarding treatment, prevention of spread and protection of staff to inform the development of a comprehensive training curriculum on COVID-19 management. DESIGN: Cross-sectional study. SETTING: Nine hospitals caring for patients with COVID-19 in Wuhan, China. PARTICIPANTS: 134 Chinese healthcare professionals (74 doctors and 60 nurses) who were deployed to Wuhan, China during the COVID-19 epidemic were included. A two-round Delphi process was initiated between March and May 2020. In the first round, the participants identified knowledge, technical and behavioural (ie, non-technical) skills that are needed to treat patients, prevent spread of the virus and protect healthcare workers. In round 2, the participants rated each item according to its importance to be included in a training curriculum on COVID-19. Consensus for inclusion in the final list was set at 80%. PRIMARY OUTCOME MEASURES: Knowledge, technical and behavioural (ie, non-technical) skills that could form the basis of a training curriculum for COVID-19 management. RESULTS: In the first round 1398 items were suggested by the doctors and reduced to 67 items after content analysis (treatment of patients: n=47; infection prevention and control: n=20). The nurses suggested 1193 items that were reduced to 70 items (treatment of patients: n=49; infection prevention and control: n=21). In round 2, the response rates were 82% in doctors and 93% in nurses. Fifty-eight items of knowledge, technical and behavioural skills were agreed on by the doctors to include in the final list. For the nurses, 58 items were agreed on. CONCLUSIONS: This needs assessment process resulted in a comprehensive list of knowledge, technical and behavioural skills for COVID-19 management. Educators can use these to guide decisions regarding content of training curricula not only for COVID-19 management but also in preparation for future viral pandemic outbreaks.

Induction of alarmin S100A8/A9 mediates activation of aberrant neutrophils in the pathogenesis of COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic poses an unprecedented public health crisis. Evidence suggests that SARS-CoV-2 infection causes dysregulation of the immune system. However, the unique signature of early immune responses remains elusive. We characterized the transcriptome of rhesus macaques and mice infected with SARS-CoV-2. Alarmin S100A8 was robustly induced in SARS-CoV-2-infected animal models as well as in COVID-19 patients. Paquinimod, a specific inhibitor of S100A8/A9, could rescue the pneumonia with substantial reduction of viral loads in SARS-CoV-2-infected mice. Remarkably, Paquinimod treatment resulted in almost 100% survival in a lethal model of mouse coronavirus infection using the mouse hepatitis virus (MHV). A group of neutrophils that contributes to the uncontrolled pathological damage and onset of COVID-19 was dramatically induced by coronavirus infection. Paquinimod treatment could reduce these neutrophils and regain anti-viral responses, unveiling key roles of S100A8/A9 and aberrant neutrophils in the pathogenesis of COVID-19, highlighting new opportunities for therapeutic intervention.

Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction.

A new coronavirus called SARS-CoV-2 is rapidly spreading around the world. Over 16,558,289 infected cases with 656,093 deaths have been reported by July 29th, 2020, and it is urgent to identify effective antiviral treatment. In this study, potential antiviral drugs against SARS-CoV-2 were identified by drug repositioning through Virus-Drug Association (VDA) prediction. 96 VDAs between 11 types of viruses similar to SARS-CoV-2 and 78 small molecular drugs were extracted and a novel VDA identification model (VDA-RLSBN) was developed to find potential VDAs related to SARS-CoV-2. The model integrated the complete genome sequences of the viruses, the chemical structures of drugs, a regularized least squared classifier (RLS), a bipartite local model, and the neighbor association information. Compared with five state-of-the-art association prediction methods, VDA-RLSBN obtained the best AUC of 0.9085 and AUPR of 0.6630. Ribavirin was predicted to be the best small molecular drug, with a higher molecular binding energy of -6.39 kcal/mol with human angiotensin-converting enzyme 2 (ACE2), followed by remdesivir (-7.4 kcal/mol), mycophenolic acid (-5.35 kcal/mol), and chloroquine (-6.29 kcal/mol). Ribavirin, remdesivir, and chloroquine have been under clinical trials or supported by recent works. In addition, for the first time, our results suggested several antiviral drugs, such as FK506, with molecular binding energies of -11.06 and -10.1 kcal/mol with ACE2 and the spike protein, respectively, could be potentially used to prevent SARS-CoV-2 and remains to further validation. Drug repositioning through virus-drug association prediction can effectively find potential antiviral drugs against SARS-CoV-2.

Strategy for treating vascular emergencies during the COVID-19 pandemic in China.

Coronavirus-caused pneumonia (COVID-19) broke out in December 2019. The virus soon proved to be extremely contagious and caused an international pandemic. Clinicians treating COVID-19 patients face considerable danger of occupational exposure because of the highly infectious nature of the virus, and precautions must be taken to prevent medical staff infections. This article lists important measures that may save the lives of patients and medical staff during the COVID-19 pandemic and help stop the transmission of COVID-19 on hospital grounds. The suggestions include establishing detailed infection control and prevention protocols in the operating room; expediting testing procedures and patient screening for COVID-19; using case-specific treatment planning for vascular patients with COVID-19, favoring minimally invasive methods; and establishing and reinforcing protective awareness of medical personnel.

Preparedness of medical education in China: Lessons from the COVID-19 outbreak.

The COVID-19 outbreak can be seen as a 'big test' for China; a summative assessment of its preparedness on multiple fronts, including medical education. Being intimately involved in the coordinated response, the First Affiliated Hospital of Sun Yat-sen University has been a first-hand witness to the strengths and weaknesses of the current medical education system in China. On the one hand, we believe that the distinguished contributions in disease containment efforts by healthcare professionals indicated that our medical education system has achieved its intended outcomes and is socially accountable. On the other hand, we have also identified three major issues that need to be addressed from an educational standpoint: insufficient emphasis on public health emergency preparedness; unsophisticated mechanisms for interdisciplinary cooperation; and inadequate guidance in medical ethics. Whilst these reflections might be seen in its summative form, we would suggest changing it to that of a formative process, where we learn from our assessment through observation and feedback of the gaps, upon which improvement of our present situation can be made. We hope that these lessons may be helpful to our colleagues in the rest of China and around the world, who are engaged in medical educational reform.