Pathway from interpersonal sensitivity to depression among Chinese college students during the COVID-19 campus lockdown.

BACKGROUND: Due to the continuous spread of the epidemic, some colleges and universities have implemented a campus lockdown management policy in China. In the context of the campus lockdown, this study aimed to explore whether anxiety mediated the association between interpersonal sensitivity and depression, and investigate whether psychological capital moderated the indirect or direct effect of mediation model. METHODS: A total of 12945 undergrad students were recruited in China from April 10 to 19, 2022. These participants were asked to complete the online questionnaires measuring interpersonal sensitivity, anxiety, psychological capital, and depression. A moderated mediation model was examined by using PROCESS macro for SPSS 25.0, in which anxiety was a mediating variable, and psychological capital was a moderating variable. RESULTS: Interpersonal sensitivity was positively associated with depression among Chinese college students (r = 0.47, P < 0.001). Anxiety partially mediated the association between interpersonal sensitivity and depression (indirect effect = 2.31, 95%CI [2.18, 2.44], accounting for 70% of the total effect). Moreover, the interaction effect of interpersonal sensitivity and psychological capital on anxiety (ß = -0.04, t = -17.36, P < 0.001) and the interaction effect of anxiety and psychological capital on depression (ß = 0.002, t = 1.99, P < 0.05) were statistically significant. CONCLUSIONS: The current study explained the mediation role of anxiety and the moderation role of psychological capital in the relation between interpersonal sensitivity and depression. The findings suggested that strict monitoring anxiety and promoting psychological capital may decrease the risk of depression among Chinese college students during the campus lockdown.

Effects of interpersonal sensitivity on depressive symptoms in postgraduate students during the COVID-19 pandemic: Psychological capital and sleep quality as mediators.

Background: This study aimed to examine depressive symptoms associated with interpersonal sensitivity, sleep quality, and psychological capital among postgraduate students during static campus management after the COVID-19 pandemic in China. Methods: Research data were obtained during static campus management (10-19 April 2022) after the reappearance of COVID-19 in cities in eastern China. We collected data through an online questionnaire, and the anonymous self-reported questionnaire included the Patient Health Questionnaire, the interpersonal sensitivity subscale of Symptom Checklist-90, the Psychological Capital Questionnaire, and the Pittsburgh Sleep Quality Index. analysis of variance was performed using t-test and ANOVA. The PROCESS macro was used to determine the relationship between interpersonal sensitivity and depression, together with the independent and serial mediating role of psychological capital and sleep quality. Results: A total of 2,554 postgraduate students were included in this study. The prevalence of mild, moderate, and severe depressive symptoms was 30.97, 6.58, and 1.45%, respectively. Interpersonal sensitivity was significantly associated with depressive symptoms (direct effect = 0.183, p < 0.001). Between interpersonal sensitivity and depressive symptoms, psychological capital and sleep quality played a single mediating role (indirect effect = 0.136 and 0.100, p < 0.001, respectively) and a chain mediating role together (indirect effect = 0.066, p < 0.001). Conclusion: Interpersonal sensitivity has a significant influence on depression among Chinese graduate students. Psychological capital and sleep quality may not only independently mediate the relationship between interpersonal sensitivity and depression, but also co-play a chain-mediating role in the pathway from interpersonal sensitivity to depression. Positive psychological interventions and sleep guidance may be beneficial in alleviating depressive symptoms.

Network connectivity between anxiety, depressive symptoms and psychological capital in Chinese university students during the COVID-19 campus closure.

BACKGROUND: In the context of the outbreak of COVID-19 within mainland China, to understand the mental health status of university students during campus closure, this study analyzes the relationship between anxiety, depressive symptoms, and psychological capital and to reveals their central symptoms. METHODS: A total of 12,945 university students were included in this study from April 10 to 19, 2022. Anxiety and depressive symptoms were measured by the seven-item Generalized Anxiety Disorder Scale (GAD-7) and two-item Patient Health Questionnaires (PHQ-2). Psychological capital was measured using the Psychological Capital Questionnaire (PCQ-24). The centrality and bridge centrality indexes were used to identify central and bridge symptoms, respectively. Network Comparison Test (NCT) was also administered to check whether network traits differed by gender and place of residence. RESULTS: The most influential node in this study was Trouble relaxing (GAD4), followed by Uncontrollable worry (GAD2) and Excessive worry (GAD3). The main bridging symptoms were Depressed mood (PHQ2), Psychological capital. There are no differences in the network structure of students by place of residence, while there are more significant differences in the network structure of students by gender. CONCLUSION: Central and bridging symptoms may be the core symptoms that trigger or maintain the development of anxiety and depression among university students during the COVID-19 campus closure. Timely and reasonable interventions targeting these symptoms may help reduce depression and anxiety in this population. In addition, improving university students' psychological capital may likewise contribute to the development of their good mental health.

A measurement method for mental health based on dynamic multimodal feature recognition.

Introduction: The number of college students with mental problems has increased significantly, particularly during COVID-19. However, the clinical features of early-stage psychological problems are subclinical, so the optimal intervention treatment period can easily be missed. Artificial intelligence technology can efficiently assist in assessing mental health problems by mining the deep correlation of multi-dimensional data of patients, providing ideas for solving the screening of normal psychological problems in large-scale college students. Therefore, we propose a mental health assessment method that integrates traditional scales and multimodal intelligent recognition technology to support the large-scale and normalized screening of mental health problems in colleges and universities. Methods: Firstly, utilize the psychological assessment scales based on human-computer interaction to conduct health questionnaires based on traditional methods. Secondly, integrate machine learning technology to identify the state of college students and assess the severity of psychological problems. Finally, the experiments showed that the proposed multimodal intelligent recognition method has high accuracy and can better proofread normal scale results. This study recruited 1,500 students for this mental health assessment. Results: The results showed that the incidence of moderate or higher stress, anxiety, and depression was 36.3, 48.1, and 23.0%, which is consistent with the results of our multiple targeted tests. Conclusion: Therefore, the interactive multimodality emotion recognition method proposed provides an effective way for large-scale mental health screening, monitoring, and intervening in college students' mental health problems.

The study of psychological traits among Chinese college students during the COVID-19 campus lockdown

To investigate the prevalence of interpersonal sensitivity, anxiety, depression symptoms and associated risk factors among a large-scale sample of college students in China during the COVID-19 campus lockdown. The survey was conducted among undergraduate students at a university in eastern part of China in April 2022. The Chi-square test was used to compare the different variable groups and multivariable analysis was performed for the risk factors associated with interpersonal sensitivity, anxiety, and depression symptoms. A total of 12,922 college students were included, with an average age of (20.96 ± 1.66) years. The prevalence of interpersonal sensitivity, anxiety and depression symptoms in this study was 58.1, 22.7, and 46.8%, respectively. Male (OR = 1.16, p < 0.001), 22–23 years (OR = 1.40, p < 0.001), freshman (OR = 1.35, p = 0.002), and non-only child (OR = 1.15, p < 0.001) were positively associated with interpersonal sensitivity. Male (OR = 1.20, p < 0.001), sophomores (OR = 1.27, p = 0.020) and seniors (OR = 1.20, p = 0.027) were positively associated with anxiety symptoms. Compared with female students, male students (OR = 0.89, p < 0.001) were less likely to have depression symptoms. 22–23 years (OR = 1.37, p < 0.001), sophomores (OR = 1.26, p = 0.009) and non-only child (OR = 1.11, p = 0.009) were positively associated with depression symptoms. In addition, college students aged 18–21 years, learning status, skipping breakfast, roommate relationship and sleep quality were associated with interpersonal sensitivity, anxiety and depression symptoms (all p < 0.05). The findings of this study suggest a high prevalence of interpersonal sensitivity, anxiety and depression symptoms among Chinese college students during the COVID-19 campus lockdown. Younger ages, low grades, poor dormitory relationship, negative learning status, skipping breakfast and poor sleep quality were the risk factors for college students’ mental health, which should be concerned by the relevant departments of school during the campus lockdown.

Resilience and Depressive Symptoms Mediated Pathways from Social Support to Suicidal Ideation Among Undergraduates During the COVID-19 Campus Lockdown in China.

Purpose: The COVID-19 pandemic has greatly affected people's mental health. The direct and indirect pathways between social support and suicidal ideation in the period are still unclear. This study explores the pathways from social support to suicidal ideation through resilience and depressive symptoms among undergraduates during the COVID-19 campus lockdown. Methods: During two weeks of the COVID-19 campus lockdown, a total of 12,945 undergraduates at a university in eastern China completed the questionnaire including sociodemographic characteristics, suicidal ideation, social support, resilience, and depressive symptoms. A structural equation modeling (SEM) approach was used to analyze the direct and indirect pathways from social support to suicidal ideation via the mediators of resilience and depressive symptoms. Results: Of the 12,917 undergraduates included in this study, 7.4% (n = 955) reported they sometimes had suicidal ideation, 0.8% (n = 109) reported they often had suicidal ideation, 0.9% (n = 122) reported they always had suicidal ideation, and 13.2% (n = 1704) reported they had depressive symptoms. Social support exerted significant direct (ß = -0.058), indirect (ß = -0.225), and total (ß = -0.283) effects on suicidal ideation; 20.5% of the total effect was direct, and 79.5% was indirect. Social support predicted suicidal ideation through resilience (ß = -0.038), and depressive symptoms (ß = -0.087), explaining 13.4%, and 30.7% of the total effect, respectively. Social support predicted suicidal ideation through the sequential mediation of resilience and depressive symptoms (ß = -0.099), explaining 35.0% of the total effect. Conclusion: This is the first study to provide the evidence of pathways from social support to suicidal ideation through resilience and depressive symptoms during the COVID-19 campus lockdown among undergraduates in China. Both direct and indirect pathways from social support to suicidal ideation were identified as intervention targets to reduce suicidal ideation.

Interpersonal sensitivity on college freshmen's depression: A moderated moderation model of psychological capital and family support.

Background: The prevalence of depressive symptoms has become very high among college freshmen, with interpersonal sensitivity serving as an important predictor of depression. Combining internal and external positive resources can effectively prevent and alleviate depression. This study explores the moderating role of psychological capital (PsyCap) in the relationship between interpersonal sensitivity and depression, as well as the moderating effect of familial support on the conditional influence of PsyCap among Chinese college freshmen. Methods: A cross-sectional mental health survey was performed and the anonymous self-reported questionnaires, including the Patient Health Questionnaire, interpersonal sensitivity subscale of Symptom Checklist-90, Psychological Capital Questionnaire 24, and Perceived Social Support from Family, were distributed to the freshmen. Pearson's coefficient was employed to describe correlations between variables. The PROCESS macro and slope difference tests were used to explore the moderating role of PsyCap and family support in the relationship between interpersonal sensitivity and depression. Results: The prevalence of depression among freshmen was 30.89% (694/2,247). The correlation analysis revealed that depression negatively related to PsyCap (r = -0.187, p < 0.001) and family support (r = -0.193, p < 0.001) and positively related to interpersonal sensitivity (r = 0.399, p < 0.001). The moderation analysis showed that PsyCap negatively moderated the positive relationship between interpersonal sensitivity and depression (ß = -0.159, p < 0.001). We also found that family support played a moderating role in the conditional influence of PsyCap (ß = 0.076, p < 0.01). The slope difference test further showed that family support weakened the effect of interpersonal sensitivity on depression in freshmen when they had low PsyCap. Conclusion: More attention should be paid to freshmen's mental health and interpersonal interaction problems. For freshmen with interpersonal sensitivity and depression, mental health departments can conduct PsyCap development interventions to alleviate psychological symptoms. Freshmen themselves should also seek family support in time, but those individuals with high PsyCap should seek an appropriate level of family support to maintain their autonomy.

Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study.

PURPOSE: Coronavirus disease 2019 (COVID-19) is a recently declared worldwide pandemic. Triaging of patients into severe and non-severe could further help in targeted management. "Potential severe patients" is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT-based radiomics signature for the prediction of these potential severe COVID-19 patients. METHODS: One hundred fifty COVID-19 patients were enrolled and randomly divided into cross-validation and independent test sets. First, their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the inter- and intra-class correlation coefficients (ICC) analysis, minimum-redundancy maximum-relevance (mRMR) selection, and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow test and Delong test. RESULTS: Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR, and LASSO methods resulted in the selection of ten radiomics features, which made up of the radiomics signature. The differences in the radiomics signature between the potential severe and non-severe groups in cross-validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models. CONCLUSIONS: A CT-based radiomics signature for the prediction of potential severe COVID-19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID-19 pandemic prevention and control.

Mix-and-Interpolate: A Training Strategy to Deal With Source-Biased Medical Data.

Till March 31st, 2021, the coronavirus disease 2019 (COVID-19) had reportedly infected more than 127 million people and caused over 2.5 million deaths worldwide. Timely diagnosis of COVID-19 is crucial for management of individual patients as well as containment of the highly contagious disease. Having realized the clinical value of non-contrast chest computed tomography (CT) for diagnosis of COVID-19, deep learning (DL) based automated methods have been proposed to aid the radiologists in reading the huge quantities of CT exams as a result of the pandemic. In this work, we address an overlooked problem for training deep convolutional neural networks for COVID-19 classification using real-world multi-source data, namely, the data source bias problem. The data source bias problem refers to the situation in which certain sources of data comprise only a single class of data, and training with such source-biased data may make the DL models learn to distinguish data sources instead of COVID-19. To overcome this problem, we propose MIx-aNd-Interpolate (MINI), a conceptually simple, easy-to-implement, efficient yet effective training strategy. The proposed MINI approach generates volumes of the absent class by combining the samples collected from different hospitals, which enlarges the sample space of the original source-biased dataset. Experimental results on a large collection of real patient data (1,221 COVID-19 and 1,520 negative CT images, and the latter consisting of 786 community acquired pneumonia and 734 non-pneumonia) from eight hospitals and health institutions show that: 1) MINI can improve COVID-19 classification performance upon the baseline (which does not deal with the source bias), and 2) MINI is superior to competing methods in terms of the extent of improvement.

Prognosis and antibody profiles in survivors of critical illness from COVID-19: a prospective multicentre cohort study.

BACKGROUND: There is a need to assess the long-term outcomes of survivors of critical illness from COVID-19. METHODS: Ninety-two survivors of critical illness from COVID-19 from four hospitals in Hubei Province, China participated in this prospective cohort study. Multiple characteristics, including lung function (lung volumes, diffusing capacity for carbon monoxide, chest computed tomography scores, and walking capacity); immune status (SARS-CoV-2-neutralising antibody and all subtypes of immunoglobulin (Ig) G against SARS-CoV-2, immune cells in response to ex vivo antigen peptide stimuli, and lymphocyte count and its subtypes); liver, coagulation, and kidney functions; quality of life; cognitive function; and mental status, were assessed after 3, 6, and 12 months of follow-up. RESULTS: Amongst the 92 enrolled survivors, 72 (78%) patients required mechanical ventilation. At 12 months, the predicted percentage diffusing capacity of lung for carbon monoxide was 82% (inter-quartile range [IQR]: 76-97%) with a residual volume of 77 (64-88)%. Other lung function parameters and the 6-min walk test improved gradually over time and were almost back to normal by 12 months. The titres of IgG and neutralising antibody to COVID-19 remained high at 12 months compared with those of controls who were not infected with COVID-19, although IgG titres decreased significantly from 34.0 (IQR: 23.8-74.3) to 15.0 (5.8-24.3) AU ml-1 (P<0.001), whereas neutralising antibodies decreased from 29.99 (IQR: 19.43-53.93) AU ml-1 at 6 months to 19.75 (13.1-29.8) AU ml-1 (P<0.001) at 12 months. In general, liver, kidney, physical, and mental functions also improved over time. CONCLUSIONS: Survivors of critical illness from COVID-19 show some persistent long-term impairments in lung function. However, a majority of these tests were normal by 12 months. These patients still had detectable levels of neutralising antibodies against SARS-CoV-2 and all types of IgG at 12 months, but the levels had declined over this time period. CLINICAL TRIAL REGISTRATION: None.

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.

Updated pharmacological effects of Lonicerae japonicae flos, with a focus on its potential efficacy on coronavirus disease-2019 (COVID-19).

Lonicerae japonicae flos (LJF), known as Jin Yin Hua in Chinese, is one of the most commonly used traditional Chinese herbs and nutraceuticals. Nowadays, LJF is broadly applied in an array of afflictions, such as fever, sore throat, flu infection, cough, and arthritis, with the action mechanism to be elucidated. Here, we strove to summarize the main phytochemical components of LJF and review its updated pharmacological effects, including inhibition of inflammation, pyrexia, viruses, and bacteria, immunoregulation, and protection of the liver, nervous system, and heart, with a focus on the potential efficacy of LJF on coronavirus disease-2019 based on network pharmacology so as to fully underpin the utilization of LJF as a medicinal herb and a favorable nutraceutical in daily life.

Analysis of Lymphocyte Subpopulations and Cytokines in COVID-19-Associated Pneumonia and Community-Acquired Pneumonia.

BACKGROUND: The 2019 novel coronavirus SARS-CoV-2 caused large outbreaks of COVID-19 worldwide. COVID-19 resembles community-acquired pneumonia (CAP). Our aim was to identify lymphocyte subpopulations to distinguish between COVID-19 and CAP. METHODS: We compared the peripheral blood lymphocytes and their subsets in 296 patients with COVID-19 and 130 patients with CAP. Parameters for independent prediction of COVID-19 were calculated by logistic regression. RESULTS: The main lymphocyte subpopulations (CD3+CD4+, CD16+CD56+, and CD4+/CD8+ ratio) and cytokines (TNF-α and IFN-γ) of COVID-19 patients were significantly different from that of CAP patients. CD16+CD56+%, CD4+/CD8+ratio, CD19+, and CD3+CD4+ were identified as predictors of COVID-19 diagnosis by logistic regression. In addition, the CD3+CD4+counts, CD3+CD8+ counts, andTNF-α are independent predictors of disease severity in patients. CONCLUSIONS: Lymphopenia is an important part of SARS-CoV-2 infection, and lymphocyte subsets and cytokines may be useful to predict the severity and clinical outcomes of the disease.

Clinical characteristics and outcomes of 2019 novel coronavirus disease patients presenting with initial gastrointestinal symptoms in Wuhan, China: A retrospective cohort study.

BACKGROUND AND AIM: Patients with 2019 novel coronavirus disease (COVID-19) could present with gastrointestinal symptoms without fever or respiratory manifestations, which could be overlooked by health-care providers. We aimed to evaluate the clinical characteristics of COVID-19 in patients presenting with initial gastrointestinal symptoms. METHODS: We evaluated all confirmed cases of COVID-19 in Zhongnan Hospital of Wuhan University between January 10 and February 29, 2020. We divided these patients into two groups: patients with initial gastrointestinal symptoms (group A, n = 183) and patients with respiratory syndrome and/or fever (group B, n = 1228). The clinical characteristics, radiological features, and laboratory data were assessed. RESULTS: The clinical procedures of both groups underwent 1-2 weeks rising period and were downward trend at 3 weeks; less than 5% of patients progressed to critical illness. In both groups, mean leukocyte count (P = 0.354) and lymphocyte count (P = 0.386) were below normal, and C-reactive protein level was elevated (P = 0.412). There was mild liver function injury (aspartate aminotransferase, 65.8 ± 12.7 vs 67.4 ± 9.3 U/L, P = 0.246; alanine aminotransferase, 66.4 ± 13.2 vs 69.6 ± 12.7 U/L, P = 0.352), and normal renal function was intact (blood urea nitrogen 6.4 ± 2.5 vs 5.6 ± 2.8 mmol/L P = 0.358; creatinine 85.7 ± 37.2, 91.2 ± 32.6 µmol/L, P = 0.297). After a series of treatment, 176 and 1169 were stable and alive in groups A and B, respectively. The survival rate did not differ significantly between the groups (P = 0.313). CONCLUSION: COVID-19 patients presented with initial gastrointestinal symptoms had similar clinical characteristics and outcomes, when compared with patients with fever and respiratory symptoms.

Relationship Between Serum Severe Acute Respiratory Syndrome Coronavirus 2 Nucleic Acid and Organ Damage in Coronavirus 2019 Patients: A Cohort Study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and has the ability to damage multiple organs. However, information on serum SARS-CoV-2 nucleic acid (RNAemia) in patients affected by coronavirus disease 2019 (COVID-19) is limited. METHODS: Patients who were admitted to Zhongnan Hospital of Wuhan University with laboratory-confirmed COVID-19 were tested for SARS-COV-2 RNA in serum from 28 January 2020 to 9 February 2020. Demographic data, laboratory and radiological findings, comorbidities, and outcomes data were collected and analyzed. RESULTS: Eighty-five patients were included in the analysis. The viral load of throat swabs was significantly higher than of serum samples. The highest detection of SARS-CoV-2 RNA in serum samples was between 11 and 15 days after symptom onset. Analysis to compare patients with and without RNAemia provided evidence that computed tomography and some laboratory biomarkers (total protein, blood urea nitrogen, lactate dehydrogenase, hypersensitive troponin I, and D-dimer) were abnormal and that the extent of these abnormalities was generally higher in patients with RNAemia than in patients without RNAemia. Organ damage (respiratory failure, cardiac damage, renal damage, and coagulopathy) was more common in patients with RNAemia than in patients without RNAemia. Patients with vs without RNAemia had shorter durations from serum testing SARS-CoV-2 RNA. The mortality rate was higher among patients with vs without RNAemia. CONCLUSIONS: In this study, we provide evidence to support that SARS-CoV-2 may have an important role in multiple organ damage. Our evidence suggests that RNAemia has a significant association with higher risk of in-hospital mortality.

Artificial intelligence-based approaches for COVID-19 patient management.

During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.

Cardiac Involvement in Recovered Patients From COVID-19: A Preliminary 6-Month Follow-Up Study.

Background: Accumulating evidence has revealed that coronavirus disease 2019 (COVID-19) patients may be complicated with myocardial injury during hospitalization. However, data regarding persistent cardiac involvement in patients who recovered from COVID-19 are limited. Our goal is to further explore the sustained impact of COVID-19 during follow-up, focusing on the cardiac involvement in the recovered patients. Methods: In this prospective observational follow-up study, we enrolled a total of 40 COVID-19 patients (20 with and 20 without cardiac injury during hospitalization) who were discharged from Zhongnan Hospital of Wuhan University for more than 6 months, and 27 patients (13 with and 14 without cardiac injury during hospitalization) were finally included in the analysis. Clinical information including self-reported symptoms, medications, laboratory findings, Short Form 36-item scores, 6-min walk test, clinical events, electrocardiogram assessment, echocardiography measurement, and cardiac magnetic resonance imaging was collected and analyzed. Results: Among 27 patients finally included, none of patients reported any obvious cardiopulmonary symptoms at the 6-month follow-up. There were no statistically significant differences in terms of the quality of life and exercise capacity between the patients with and without cardiac injury. No significant abnormalities were detected in electrocardiogram manifestations in both groups, except for nonspecific ST-T changes, premature beats, sinus tachycardia/bradycardia, PR interval prolongation, and bundle-branch block. All patients showed normal cardiac structure and function, without any statistical differences between patients with and without cardiac injury by echocardiography. Compared with patients without cardiac injury, patients with cardiac injury exhibited a significantly higher positive proportion in late gadolinium enhancement sequences [7/13 (53.8%) vs. 1/14 (7.1%), p = 0.013], accompanied by the elevation of circulating ST2 level [median (interquartile range) = 16.6 (12.1, 22.5) vs. 12.5 (9.5, 16.7); p = 0.044]. Patients with cardiac injury presented higher levels of aspartate aminotransferase, creatinine, high-sensitivity troponin I, lactate dehydrogenase, and N-terminal pro-B-type natriuretic peptide than those without cardiac injury, although these indexes were within the normal range for all recovered patients at the 6-month follow-up. Among patients with cardiac injury, patients with positive late gadolinium enhancement presented higher cardiac biomarker (high-sensitivity troponin I) and inflammatory factor (high-sensitivity C-reactive protein) on admission than the late gadolinium enhancement-negative subgroup. Conclusions: Our preliminary 6-month follow-up study with a limited number of patients revealed persistent cardiac involvement in 29.6% (8/27) of recovered patients from COVID-19 after discharge. Patients with cardiac injury during hospitalization were more prone to develop cardiac fibrosis during their recovery. Among patients with cardiac injury, those with relatively higher cardiac biomarkers and inflammatory factors on admission appeared more likely to have cardiac involvement in the convalescence phase.

Accurate and efficient pulmonary CT imaging workflow for COVID-19 patients by the combination of intelligent guided robot and automatic positioning technology.

BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic has put radiologists at a higher risk of infection during the computer tomography (CT) examination for the patients. To help settling these problems, we adopted a remote-enabled and automated contactless imaging workflow for CT examination by the combination of intelligent guided robot and automatic positioning technology to reduce the potential exposure of radiologists to 2019 novel coronavirus (2019-nCoV) infection and to increase the examination efficiency, patient scanning accuracy and better image quality in chest CT imaging . METHODS: From February 10 to April 12, 2020, adult COVID-19 patients underwent chest CT examinations on a CT scanner using the same scan protocol except with the conventional imaging workflow (CW group) or an automatic contactless imaging workflow (AW group) in Wuhan Leishenshan Hospital (China) were retrospectively and prospectively enrolled in this study. The total examination time in two groups was recorded and compared. The patient compliance of breath holding, positioning accuracy, image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and compared between the two groups. RESULTS: Compared with the CW group, the total positioning time of the AW group was reduced ((118.0 ± 20.0) s vs. (129.0 ± 29.0) s, P = 0.001), the proportion of scanning accuracy was higher (98% vs. 93%), and the lung length had a significant difference ((0.90±1.24) cm vs. (1.16±1.49) cm, P = 0.009). For the lesions located in the pulmonary centrilobular and subpleural regions, the image noise in the AW group was significantly lower than that in the CW group (centrilobular region: (140.4 ± 78.6) HU vs. (153.8 ± 72.7) HU, P = 0.028; subpleural region: (140.6 ± 80.8) HU vs. (159.4 ± 82.7) HU, P = 0.010). For the lesions located in the peripheral, centrilobular and subpleural regions, SNR was significantly higher in the AW group than in the CW group (centrilobular region: 6.6 ± 4.3 vs. 4.9 ± 3.7, P = 0.006; subpleural region: 6.4 ± 4.4 vs. 4.8 ± 4.0, P < 0.001). CONCLUSIONS: The automatic contactless imaging workflow using intelligent guided robot and automatic positioning technology allows for reducing the examination time and improving the patient's compliance of breath holding, positioning accuracy and image quality in chest CT imaging.

Joint segmentation and detection of COVID-19 via a sequential region generation network.

The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.