Prevalence, correlates, and network analysis of depression and its association with quality of life in survivors with myocardial infarction during the COVID-19 pandemic.

BACKGROUND: Depression is common among myocardial infarction (MI) survivors and is strongly associated with poor quality of life (QOL). The aim of this study was to examine the prevalence, correlates and the network structure of depression, and its association with QOL in MI survivors during the COVID-19 pandemic. METHODS: This cross-sectional study evaluated depression and QOL in MI survivors with the Chinese version of the nine-item Patient Health Questionnaire (PHQ-9) and the World Health Organization Quality of Life-BREF (WHOQOL-BREF), respectively. Univariable analyses, multivariable analyses, and network analyses were performed. RESULTS: The prevalence of depression (PHQ-9 total score ≥ 5) among 565 MI survivors during the COVID-19 pandemic was 38.1 % (95 % CI: 34.1-42.1 %), which was significantly associated with poor QOL. Patients with depression were less likely to consult a doctor regularly after discharge, and more likely to experience more severe anxiety symptoms and fatigue. Item PHQ4 "Fatigue" was the most central symptom in the network, followed by PHQ6 "Guilt" and PHQ2 "Sad mood". The flow network showed that PHQ4 "Fatigue" had the highest negative association with QOL. CONCLUSION: Depression was prevalent among MI survivors during the COVID-19 pandemic and was significantly associated with poor QOL. Those who failed to consult a doctor regularly after discharge or reported severe anxiety symptoms and fatigue should be screened for depression. Effective interventions for MI survivors targeting central symptoms, especially fatigue, are needed to reduce the negative impact of depression and improve QOL.

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

Summary Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection. Graphical abstract Highlights • We present a computational framework for alternative splicing (AS) diagnostic markers• Our AS biomarkers outperform gene-expression biomarkers in COVID-19 detection• Microfluidic PCR diagnostic assay of AS biomarkers achieves greater than 98% accuracy• We interpret the biological importance of identified AS biomarkers Motivation Host-based response assays (HRAs) can often diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) in HRAs remains unexplored, as existing HRAs are restricted to gene expression signatures. We report a computational framework for the identification, optimization, and evaluation of blood AS-based diagnostic assay development for infectious disease. Using SARS-CoV-2 infection as a case study, we demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis when compared against six reported transcriptome signatures and when implemented as a microfluidic PCR diagnostic assay. Host-based response assays can diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) remains unexplored. Zhang et al. present a computational framework for AS diagnostic biomarkers. Using SARS-CoV-2 as a case study, they demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis.

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease.

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.

Synthetic Biology-Powered Biosensors Based on CRISPR/Cas Mediated Cascade Signal Amplification for Precise RNA Detection

Synthetic biology enabling technologies have been harnessed to create new diagnostic technologies. However, most strategies involve error-prone amplification steps and limitations of accuracy in RNA detection. Here, a cell-free synthetic biology-powered biosensing strategy, termed as SHARK (Synthetic Enzyme Shift RNA Signal Amplifier Related Cas13a Knockdown Reaction), could efficiently and accurately amplify RNA signal by leveraging the collateral cleavage of activated Cas13a to regulate cell-free enzyme synthesis. Based on cascade amplification and tailored enzyme output, SHARK behaves broad compatibility in different scenarios. The portable device based on SHARK was successfully used as SARS-CoV-2 biosensors with high sensitivity and selectivity, and the results were highly consistent with Ct values of qRT-PCR. In addition, when combined with machine learning, SHARK performs bio-computations and thus for cancer diagnosis and staging based on 64 clinical samples. SHARK shows characteristics of precise recognition, cascade amplification and tailored signal outputting comparisons with established assays, presenting significant potential in developing next-generation RNA detection technology.

Risk assessment for acute kidney injury and death among new COVID-19 positive adult patients without chronic kidney disease: retrospective cohort study among three US hospitals.

OBJECTIVE: To develop simple but clinically informative risk stratification tools using a few top demographic factors and biomarkers at COVID-19 diagnosis to predict acute kidney injury (AKI) and death. DESIGN: Retrospective cohort analysis, follow-up from 1 February through 28 May 2020. SETTING: 3 teaching hospitals, 2 urban and 1 community-based in the Boston area. PARTICIPANTS: Eligible patients were at least 18 years old, tested COVID-19 positive from 1 February through 28 May 2020, and had at least two serum creatinine measurements within 30 days of a new COVID-19 diagnosis. Exclusion criteria were having chronic kidney disease or having a previous AKI within 3 months of a new COVID-19 diagnosis. MAIN OUTCOMES AND MEASURES: Time from new COVID-19 diagnosis until AKI event, time until death event. RESULTS: Among 3716 patients, there were 1855 (49.9%) males and the average age was 58.6 years (SD 19.2 years). Age, sex, white blood cell, haemoglobin, platelet, C reactive protein (CRP) and D-dimer levels were most strongly associated with AKI and/or death. We created risk scores using these variables predicting AKI within 3 days and death within 30 days of a new COVID-19 diagnosis. Area under the curve (AUC) for predicting AKI within 3 days was 0.785 (95% CI 0.758 to 0.813) and AUC for death within 30 days was 0.861 (95% CI 0.843 to 0.878). Haemoglobin was the most predictive component for AKI, and age the most predictive for death. Predictive accuracies using all study variables were similar to using the simplified scores. CONCLUSION: Simple risk scores using age, sex, a complete blood cell count, CRP and D-dimer were highly predictive of AKI and death and can help simplify and better inform clinical decision making.

Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis.

Imaging plays an important role in assessing the severity of COVID-19 pneumonia. Recent COVID-19 research indicates that the disease progress propagates from the bottom of the lungs to the top. However, chest radiography (CXR) cannot directly provide a quantitative metric of radiographic opacities, and existing AI-assisted CXR analysis methods do not quantify the regional severity. In this paper, to assist the regional analysis, we developed a fully automated framework using deep learning-based four-region segmentation and detection models to assist the quantification of COVID-19 pneumonia. Specifically, a segmentation model is first applied to separate left and right lungs, and then a detection network of the carina and left hilum is used to separate upper and lower lungs. To improve the segmentation performance, an ensemble strategy with five models is exploited. We evaluated the clinical relevance of the proposed method compared with the radiographic assessment of the quality of lung edema (RALE) annotated by physicians. Mean intensities of segmented four regions indicate a positive correlation to the regional extent and density scores of pulmonary opacities based on the RALE. Therefore, the proposed method can accurately assist the quantification of regional pulmonary opacities of COVID-19 pneumonia patients.

Metagenomic Next-Generation Sequencing in the Diagnosis of HHV-1 Reactivation in a Critically Ill COVID-19 Patient: A Case Report.

Background: Secondary infections pose tremendous challenges in Coronavirus disease 2019 (COVID-19) treatment and are associated with higher mortality rates. Clinicians face of the challenge of diagnosing viral infections because of low sensitivity of available laboratory tests. Case Presentation: A 66-year-old woman initially manifested fever and shortness of breath. She was diagnosed as critically ill with COVID-19 using quantitative reverse transcription PCR (RT-qPCR) and treated with antiviral therapy, ventilator and extracorporeal membrane oxygenation (ECMO). However, after the condition was relatively stabled for a few days, the patient deteriorated with fever, frequent cough, increased airway secretions, and increased exudative lesions in the lower right lung on chest X-rays, showing the possibility of a newly acquired infection, though sputum bacterial and fungal cultures and smears showed negative results. Using metagenomic next-generation sequencing (mNGS), we identified a reactivation of latent human herpes virus type 1 (HHV-1) in the respiratory tract, blood and gastrointestinal tract, resulting in a worsened clinical course in a critically ill COVID-19 patient on ECMO. Anti-HHV-1 therapy guided by these sequencing results effectively decreased HHV-1 levels, and improved the patient's clinical condition. After 49 days on ECMO and 67 days on the ventilator, the 66-year-old patient recovered and was discharged. Conclusions: This case report demonstrates the potential value of mNGS for evidence-based treatment, and suggests that potential reactivation of latent viruses should be considered in critically ill COVID-19 patients.

Effect of family stress on life satisfaction among female workers during the COVID-19 epidemic in China: Exploring the roles of anxiety symptoms and age.

The study aimed to investigate the level of life satisfaction (LS) among Chinese female workers after resuming work during the COVID-19 epidemic, and to further explore the potential mediating and moderating roles in the association between family stress and LS. Self-reported questionnaires were completed by 10,175 participants. Results showed that the level of LS decreased. The family stress had a negative effect on LS, and the effect was mediated by anxiety symptoms. Additionally, age moderated the direct and indirect effects within this relationship. Interventions aiming to improve LS should consider these aspects and younger workers should be given special attention.

Exploring the Spatiotemporal Characteristics of COVID-19 Infections among Healthcare Workers: A Multi-Scale Perspective

The outbreak of COVID-19 has constantly exposed health care workers (HCWs) around the world to a high risk of infection. To more accurately discover the infection differences among high-risk occupations and institutions, Hubei Province was taken as an example to explore the spatiotemporal characteristics of HCWs at different scales by employing the chi-square test and fitting distribution. The results indicate (1) the units around the epicenter of the epidemic present lognormal distribution, and the periphery is Poisson distribution. There is a clear dividing line between lognormal and Poisson distribution in terms of the number of HCWs infections. (2) The infection rates of different types of HCWs at multiple geospatial scales are significantly different, caused by the spatial heterogeneity of the number of HCWs. (3) With the increase of HCWs infection rate, the infection difference among various HCWs also gradually increases and the infection difference becomes more evident on a larger scale. The analysis of the multi-scale infection rate and statistical distribution characteristics of HCWs can help government departments rationally allocate the number of HCWs and personal protective equipment to achieve distribution on demand, thereby reducing the mental and physical pressure and infection rate of HCWs.

Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Saliva as a testing specimen with or without pooling for SARS-CoV-2 detection by multiplex RT-PCR test

Background: Sensitive and high throughput molecular detection assays are essential during the coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The vast majority of the SARS-CoV-2 molecular assays use nasopharyngeal swab (NPS) or oropharyngeal swab (OPS) specimens collected from suspected individuals. However, using NPS or OPS as specimens has apparent drawbacks, e.g. the collection procedures for NPS or OPS specimens can be uncomfortable to some people and may cause sneezing and coughing which in turn generate droplets and/or aerosol particles that are of risk to healthcare workers, requiring heavy use of personal protective equipment. There have been recent studies indicating that self-collected saliva specimens can be used for molecular detection of SARS-CoV-2 and provides more comfort and ease of use for the patients. Here we report the performance of QuantiVirusTM SARS-CoV-2 test using saliva as the testing specimens with or without pooling. Methods Development and validation studies were conducted following FDA-EUA and molecular assay validation guidelines. Using SeraCare Accuplex SARS-CoV-2 reference panel, the limit of detection (LOD) and clinical performance studies were performed with the QuantiVirusTM SARS-CoV-2 test. For clinical evaluation, 85 known positive and 90 known negative clinical NPS samples were tested. Additionally, twenty paired NPS and saliva samples collected from recovering COVID-19 patients were tested and the results were further compared to that of the Abbott m2000 SARS-CoV-2 PCR assay. Results of community collected 389 saliva samples for COVID-19 screening by QuantiVirusTM SARS-CoV-2 test were also obtained and analyzed. Additionally, testing of pooled saliva samples was evaluated.

Association Between Perceived Stress and Post-Traumatic Stress Disorder Among Medical Staff During the COVID-19 Epidemic in Wuhan City.

Objective: The study aimed to examine the relationship between perceived stress and post-traumatic stress disorder (PTSD) among frontline medical staff during the lockdown in Wuhan city, China, due to the COVID-19 outbreak. Methods: The study was conducted in August 2020, which included 516 medical staff between 21 to 65 years. The PTSD Checklist-Civilian, Perceived Stress Scale, Insomnia Severity Index, and Compassion Fatigue Short Scale were used. Results: The results indicated that 10.5% of the medical staff experienced PTSD symptoms, and insomnia severity mediated the effect of perceived stress on PTSD. In addition, compassion fatigue moderated the association between perceived stress and PTSD. Conclusion: The study elucidated the mechanisms underlying the association between perceived stress and PTSD. Moreover, it emphasized the importance of long-term monitoring of the mental health status of frontline medical staff who supported Wuhan. The results can serve as reference for relevant medical and health departments to formulate active interventions and preventive measures against PTSD for unsung heroes who put their lives on the line during difficult times.

Spatiotemporal Differences of COVID-19 Infection Among Healthcare Workers and Patients in China From January to March 2020.

Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.

PURPOSE: As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction. METHOD: We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction. RESULTS: For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort. CONCLUSION: The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.

In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.

Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels.

Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.

Analysis and reflection on the transmission routes of SARS-COV-2

The novel coronavirus disease 2019 (COVID-19) outbreak occurred in December last year and spread quickly in the world, causing great harm to people. The rapid progression of this epidemic makes scholars in various fields conduct research on the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). So far, the wildly accepted routes are droplets and close contact. Controversially, some researchers believe that other routes include aerosol diffusion, fecal-oral transmission, contacting urine, conjunctival infection, and mother-to-infant transmission may also infect people. In this article, combining the newest research and reports, the authors systematically analyzed the theoretical possibility and real-life probability of the transmission routes of the virus in order to help with the research and clinical judgment of the spread of infectious diseases in the future.

Post-traumatic stress disorder and psychological distress in Chinese youths following the COVID-19 emergency.

This study aims to explore the relationship between psychological distress and post-traumatic stress disorder among Chinese participants as the result of COVID-19 outbreak. This study was conducted within 1 month after COVID-19 appeared in China, it included 570 participants age from 14 to 35. The results indicated that 12.8% of all participants with the symptoms of post-traumatic stress disorder and the effects of psychological distress on post-traumatic stress disorder was mediated by negative coping style. Gender moderated the direct effect between psychological distress and post-traumatic stress disorder, which is a significant discovery for relevant departments to take further measures.

The Effect of COVID-19 on Youth Mental Health.

The purposes of this study was to assess the youth mental health after the coronavirus disease 19 (COVID-19) occurred in China two weeks later, and to investigate factors of mental health among youth groups. A cross-sectional study was conducted two weeks after the occurrence of COVID-19 in China. A total of 584 youth enrolled in this study and completed the question about cognitive status of COVID-19, the General Health Questionnaire(GHQ-12), the PTSD Checklist-Civilian Version (PCL-C) and the Negative coping styles scale. Univariate analysis and univariate logistic regression were used to evaluate the effect of COVID-19 on youth mental health. The results of this cross-sectional study suggest that nearly 40.4% the sampled youth were found to be prone to psychological problems and 14.4% the sampled youth with Post-traumatic stress disorder (PTSD) symptoms. Univariate logistic regression revealed that youth mental health was significantly related to being less educated (OR = 8.71, 95%CI:1.97-38.43), being the enterprise employee (OR = 2.36, 95%CI:1.09-5.09), suffering from the PTSD symptom (OR = 1.05, 95%CI:1.03-1.07) and using negative coping styles (OR = 1.03, 95%CI:1.00-1.07). Results of this study suggest that nearly 40.4% of the youth group had a tendency to have psychological problems. Thus, this was a remarkable evidence that infectious diseases, such as COVID-19, may have an immense influence on youth mental health. Therefor, local governments should develop effective psychological interventions for youth groups, moreover, it is important to consider the educational level and occupation of the youth during the interventions.