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1.
Int J Soc Psychiatry ; 69(4): 916-927, 2023 06.
Article in English | MEDLINE | ID: covidwho-20238631

ABSTRACT

BACKGROUND: Returning to social life after the lifting of COVID-19 lockdown may increase risk of social anxiety, which is highly co-morbid with depression. However, few studies have reported the association between them. AIMS: To explore the complex relationship between social anxiety and depression symptoms in left-behind children after the lifting of the COVID-19 lockdown. METHODS: A cross-sectional survey was conducted 6 months after the lockdown removal. A total of 3,107 left-behind children completed the survey with a mean age of 13.33 and a response rate of 87.77%. Depression and social anxiety severity were assessed by the DSM-5 Patient Health Questionnaire for Adolescents and the DSM-5 Social Anxiety Disorder Questionnaire, respectively. The symptom-level association between the two disorders was examined using network analysis. RESULTS: After the lifting of COVID-19 lockdown, the prevalence of depression and social anxiety in left-behind children was 19.57% and 12.36%, respectively, with a co-morbidity rate of 8.98%. Network analysis showed that "Social tension" and "Social avoidance" had the greatest expected influence; "Humiliation" and "Motor" were bridge symptom nodes in the network. The directed acyclic graph indicated that "Social fright" was at the upstream of all symptoms. CONCLUSION: Attention should be paid to social anxiety symptoms in left-behind children after the lifting of COVID-19 lockdown. Prevention and intervention measures should be taken promptly to reduce the comorbidity of social anxiety and depression symptoms in the left-behind children after the lifting of lockdown.


Subject(s)
COVID-19 , Adolescent , Humans , Child , COVID-19/epidemiology , COVID-19/prevention & control , SARS-CoV-2 , Depression/epidemiology , Cross-Sectional Studies , East Asian People , Communicable Disease Control , Anxiety/epidemiology
2.
J Funct Biomater ; 14(2)2023 Feb 10.
Article in English | MEDLINE | ID: covidwho-2307869

ABSTRACT

Rapid, accurate, and portable on-site detection is critical in the face of public health emergencies. Infectious disease control and public health emergency policymaking can both be aided by effective and trustworthy point of care tests (POCT). A very promising POCT method appears to be the clustered regularly interspaced short palindromic repeats and associated protein (CRISPR/Cas)-based molecular diagnosis. For on-site detection, CRISPR/Cas-based detection can be combined with multiple signal sensing methods and integrated into smart devices. In this review, sensing methods for CRISPR/Cas-based diagnostics are introduced and the advanced strategies and recent advances in CRISPR/Cas-based POCT are reviewed. Finally, the future perspectives of CRISPR and POCT are summarized and prospected.

3.
Biomed Signal Process Control ; 85: 104896, 2023 Aug.
Article in English | MEDLINE | ID: covidwho-2287635

ABSTRACT

The automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images is helpful in establishing a quantitative model to diagnose and treat COVID-19. To this end, this study proposes a lightweight segmentation network called the SuperMini-Seg. We propose a new module called the transformer parallel convolution module (TPCB), which introduces both transformer and convolution operations in one module. SuperMini-seg adopts the structure of a double-branch parallel to downsample the image and designs a gated attention mechanism in the middle of the two parallel branches. At the same time, the attentive hierarchical spatial pyramid (AHSP) module and criss-cross attention module are adopted, and more than 100K parameters are present in the model. At the same time, the model is scalable, and the parameter quantity of SuperMini-seg-V2 reaches more than 70K. Compared with other advanced methods, the segmentation accuracy was almost reached the state-of-art method. The calculation efficiency was high, which is convenient for practical deployment.

4.
Comput Methods Programs Biomed ; 230: 107348, 2023 Mar.
Article in English | MEDLINE | ID: covidwho-2237242

ABSTRACT

BACKGROUND AND OBJECTIVE: COVID-19 is a serious threat to human health. Traditional convolutional neural networks (CNNs) can realize medical image segmentation, whilst transformers can be used to perform machine vision tasks, because they have a better ability to capture long-range relationships than CNNs. The combination of CNN and transformers to complete the task of semantic segmentation has attracted intense research. Currently, it is challenging to segment medical images on limited data sets like that on COVID-19. METHODS: This study proposes a lightweight transformer+CNN model, in which the encoder sub-network is a two-path design that enables both the global dependence of image features and the low layer spatial details to be effectively captured. Using CNN and MobileViT to jointly extract image features reduces the amount of computation and complexity of the model as well as improves the segmentation performance. So this model is titled Mini-MobileViT-Seg (MMViT-Seg). In addition, a multi query attention (MQA) module is proposed to fuse the multi-scale features from different levels of decoder sub-network, further improving the performance of the model. MQA can simultaneously fuse multi-input, multi-scale low-level feature maps and high-level feature maps as well as conduct end-to-end supervised learning guided by ground truth. RESULTS: The two-class infection labeling experiments were conducted based on three datasets. The final results show that the proposed model has the best performance and the minimum number of parameters among five popular semantic segmentation algorithms. In multi-class infection labeling results, the proposed model also achieved competitive performance. CONCLUSIONS: The proposed MMViT-Seg is tested on three COVID-19 segmentation datasets, with results showing that this model has better performance than other models. In addition, the proposed MQA module, which can effectively fuse multi-scale features of different levels further improves the segmentation accuracy.


Subject(s)
COVID-19 , Humans , Algorithms , Neural Networks, Computer , Electric Power Supplies , Semantics , Image Processing, Computer-Assisted
5.
Front Public Health ; 10: 1038296, 2022.
Article in English | MEDLINE | ID: covidwho-2224930

ABSTRACT

Background: The COVID-19 pandemic had a major impact on people's mental health. As the SAS-Cov-2 evolves to become less virulent, the number of asymptomatic patients increases. It remains unclear if the mild symptoms are associated with mild perceived stress and mental illness, and the interventions to improve the mental health of the patients are rarely reported. Methods: This cross-sectional study investigated the level of depression, anxiety and perceived stress of 1,305 COVID-19 patients who received treatment in the Fangcang shelter hospitals in Shanghai, China. Network analysis was used to explore the relationship among depression, anxiety and perceived stress. Results: The prevalence of depression, anxiety and perceived stress in the patients with Omicron infection were 9.03, 4.60, and 17.03%, respectively, lower than the prevalence reported during the initial outbreak of COVID-19. "Restlessness (A5)," "Uncontrollable worry (A2)," "Trouble relaxing (A4)" and "Fatigue (D4)" had the highest expected influence values. "Irritability (A6)" and "Uncontrollable (S1)" were bridge symptoms in the network. Comparative analysis of the network identified differences in the network structures between symptomatic and asymptomatic patients. Conclusion: This study investigated the prevalence of depression, anxiety and perceived stress and the correlation among them in Omicron-infected patients in Fangcang shelter hospital, in Shanghai, China. The core symptoms identified in the study provide insight into targeted clinical prevention and intervention of mental health in non-severe Omicron-infected patients.


Subject(s)
COVID-19 , Mental Health , Humans , COVID-19/epidemiology , Cross-Sectional Studies , Hospitals, Special , Pandemics , China/epidemiology , Mobile Health Units
6.
Front Public Health ; 10: 1034119, 2022.
Article in English | MEDLINE | ID: covidwho-2199505

ABSTRACT

Background: The relationship between different dimensions of empathy and individual symptoms of depression during the COVID-19 pandemic remains unclear, despite the established link between empathy and depression. The network analysis offers a novel framework for visualizing the association between empathy and depression as a complex system consisting of interacting nodes. In this study, we investigated the nuanced associations between different dimensions of empathy and individual symptoms of depression using a network model during the pandemic. Methods: 1,177 students completed the Chinese version of the Interpersonal Reactivity Index (IRI), measuring dimensions of empathy, and the Chinese version of the Patient Health Questionnaire-9 (PHQ-9), measuring symptoms of depression. First, we investigated the nuanced associations between different dimensions of empathy and individual depressive symptoms. Then, we calculated the bridge expected influence to examine how different dimensions of empathy may activate or deactivate the symptoms of depression cluster. Finally, we conducted a network comparison test to explore whether network characteristics such as empathy-depression edges and bridge nodes differed between genders. Results: First, our findings showed that personal distress was positively linked to symptoms of depression. These symptoms involved psychomotor agitation or retardation (edge weight = 0.18), sad mood (edge weight = 0.12), trouble with concentrating (edge weight = 0.11), and guilt (edge weight = 0.10). Perspective-taking was found to be negatively correlated with trouble with concentrating (edge weight = -0.11). Empathic concern was negatively associated with suicidal thoughts (edge weight = -0.10) and psychomotor agitation or retardation (edge weight = -0.08). Fantasy was not connected with any symptoms of depression. Second, personal distress and empathic concern were the most positive and negative influential nodes that bridged empathy and depression (values of bridge expected influence were 0.51 and -0.19 and values of predictability were 0.24 and 0.24, respectively). The estimates of the bridge expected influence on the nodes were adequately stable (correlation stability coefficient = 0.75). Finally, no sex differences in the studied network characteristics were observed. Conclusions: This study applied network analysis to reveal potential pathways between different dimensions of empathy and individual symptoms of depression. The findings supported the existing theoretical system and contribute to the theoretical mechanism. We have also made efforts to suggest interventions and preventions based on personal distress and empathic concern, the two most important dimensions of empathy for depressive symptoms. These efforts may help Chinese university students to adopt better practical methods to overcome symptoms of depression during the COVID-19 pandemic.


Subject(s)
COVID-19 , Pandemics , Humans , Male , Female , Depression/epidemiology , Empathy , Psychomotor Agitation , Universities , COVID-19/epidemiology , Students
7.
Frontiers in public health ; 10, 2022.
Article in English | EuropePMC | ID: covidwho-2147420

ABSTRACT

Background The COVID-19 pandemic had a major impact on people's mental health. As the SAS-Cov-2 evolves to become less virulent, the number of asymptomatic patients increases. It remains unclear if the mild symptoms are associated with mild perceived stress and mental illness, and the interventions to improve the mental health of the patients are rarely reported. Methods This cross-sectional study investigated the level of depression, anxiety and perceived stress of 1,305 COVID-19 patients who received treatment in the Fangcang shelter hospitals in Shanghai, China. Network analysis was used to explore the relationship among depression, anxiety and perceived stress. Results The prevalence of depression, anxiety and perceived stress in the patients with Omicron infection were 9.03, 4.60, and 17.03%, respectively, lower than the prevalence reported during the initial outbreak of COVID-19. “Restlessness (A5),” “Uncontrollable worry (A2),” “Trouble relaxing (A4)” and “Fatigue (D4)” had the highest expected influence values. “Irritability (A6)” and “Uncontrollable (S1)” were bridge symptoms in the network. Comparative analysis of the network identified differences in the network structures between symptomatic and asymptomatic patients. Conclusion This study investigated the prevalence of depression, anxiety and perceived stress and the correlation among them in Omicron-infected patients in Fangcang shelter hospital, in Shanghai, China. The core symptoms identified in the study provide insight into targeted clinical prevention and intervention of mental health in non-severe Omicron-infected patients.

8.
Front Psychiatry ; 13: 993814, 2022.
Article in English | MEDLINE | ID: covidwho-2099250

ABSTRACT

Background: The relations between depression and intolerance of uncertainty (IU) have been extensively investigated during the COVID-19 pandemic. However, there is a lack of understanding on how each component of IU may differentially affect depression symptoms and vice versa. The current study used a network approach to reveal the component-to-symptom interplay between IU and depression and identify intervention targets for depression during the COVID-19 pandemic. Methods: A total of 624 college students participated in the current study. An IU-Depression network was estimated using items from the 12-item Intolerance of Uncertainty Scale and the Patient Health Questionnaire-9. We examined the network structure, node centrality, and node bridge centrality to identify component-to-symptom pathways, central nodes, and bridge nodes within the IU-Depression network. Results: Several distinct pathways (e.g., "Frustration when facing uncertainty" and "Feelings of worthlessness") emerged between IU and Depression. "Fatigue" and "Frustration when facing uncertainty" were identified as the central nodes in the estimated network. "Frustration when facing uncertainty," "Psychomotor agitation/retardation," and "Depressed or sad mood" were identified as bridging nodes between the IU and Depression communities. Conclusion: By delineating specific pathways between IU and depression and highlighting the influential role of "Frustration when facing uncertainty" in maintaining the IU-Depression co-occurrence, current findings may inform targeted prevention and interventions for depression during the COVID-19 pandemic.

9.
Psychiatry Res ; 317: 114863, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2042097

ABSTRACT

Existing research proposed that moving from a disorder-level analysis to a symptom-level analysis may provide a more fine-grained understanding of psychopathology. This study aimed to explore the relations between two dimensions (i.e., cognitive reappraisal, CR; expressive suppression, ES) of emotion regulation and individual symptoms of depression and anxiety among medical staff during the late stage of COVID-19 pandemic. We examined depression symptoms, anxiety symptoms and emotion regulation among 420 medical staff during the late stage of COVID-19 pandemic via network analysis. Two networks (i.e. emotion regulation-depression network and emotion regulation-anxiety network) were constructed in the present study. Bridge centrality index was calculated for each variable within the two networks. Among the present sample, the prevalences of depression and anxiety are 39.5% and 26.0%. CR and ES showed distinct connections to symptoms of depression and anxiety. Results of bridge centrality showed that in both networks, CR had a negative bridge expected influence value while ES had a positive bridge expected influence value. The results revealed the specific role of CR and ES in relation to depression and anxiety at a symptom level. Implications for clinical preventions and interventions are discussed.


Subject(s)
COVID-19 , Emotional Regulation , Humans , Depression/psychology , Pandemics , Emotions/physiology , Anxiety/psychology , Medical Staff
10.
Front Public Health ; 10: 919692, 2022.
Article in English | MEDLINE | ID: covidwho-2022946

ABSTRACT

Background: Although poor mental well-being (MW) has been documented among individuals experiencing burnout during the coronavirus-19 (COVID-19) pandemic, little is known about the complex interrelationship between different components of MW and burnout. This study investigates this relationship among medical staff during the COVID-19 pandemic through network analysis. Methods: A total of 420 medical staff were recruited for this study. Components of MW were measured by the 14-item Warwick-Edinburgh Mental Well-being Scale (WEMWBS), and components of burnout were measured by a 15-item Maslach Burnout Inventory-General Survey (MBI-GS) Questionnaire. Network structure was constructed via network analysis. Bridge variables were identified via the bridge centrality index. Results: The edges across two communities (i.e., MW community and burnout community) are almost negative, such as edge MW2 ("Useful") - B14 ("Worthwhile") and edge MW1 ("Optimistic about future") - B13 ("Happy"). The edges within each community are nearly positive. In the MW community, components MW1 ("Optimistic about future") and MW6 ("Dealing with problems") have the lowest bridge centrality. And in the community of burnout, components B13 ("Happy") and B14 ("Worthwhile") have the lowest bridge expected influence. Conclusion: We present the first study to apply the network approach to model the potential pathways between distinct components of MW and burnout. Our findings suggest that promoting optimistic attitudes and problem-solving skills may help reduce burnout among medical staff during the pandemic.


Subject(s)
Burnout, Professional , COVID-19 , Humans , Medical Staff , Pandemics , Surveys and Questionnaires
11.
Environ Res ; 211: 112984, 2022 08.
Article in English | MEDLINE | ID: covidwho-1906997

ABSTRACT

The Coronavirus Disease 2019 (COVID-19) lockdown policy reduced anthropogenic emissions and impacted the atmospheric chemical characteristics in Chinese urban cities. However, rare studies were conducted at the high mountain site. In this work, in-situ measurements of light absorption by carbonaceous aerosols and carbon dioxide (CO2) concentrations were conducted at Waliguan (WLG) over the northeastern Tibetan Plateau of China from January 3 to March 30, 2020. The data was employed to explore the influence of the COVID-19 lockdown on atmospheric chemistry in the background-free troposphere. During the sampling period, the light absorption near-infrared (>470 nm) was mainly contributed by BC (>72%), however, BC and brown carbon (BrC) contributed equally to light absorption in the short wavelength (∼350 nm). The average BC concentrations in the pre-, during and post-lockdown were 0.28 ±â€¯0.25, 0.18 ±â€¯0.16, and 0.28 ±â€¯0.20 µg m-3, respectively, which decreased by approximately 35% during the lockdown period. Meanwhile, CO2 also showed slight decreases during the lockdown period. The declined BC was profoundly attributed to the reduced emissions (∼86%), especially for the combustion of fossil fuels. Moreover, the declined light absorption of BC, primary and secondary BrC decreased the solar energy absorbance by 35, 15, and 14%, respectively. The concentration weighted trajectories (CWT) analysis suggested that the decreased BC and CO2 at WLG were exclusively associated with the emission reduction in the eastern region of WLG. Our results highlighted that the reduced anthropogenic emissions attributed to the lockdown in the urban cities did impact the atmospheric chemistry in the free troposphere of the Tibetan Plateau.


Subject(s)
Air Pollutants , COVID-19 , Aerosols/analysis , Air Pollutants/analysis , COVID-19/epidemiology , COVID-19/prevention & control , Carbon Dioxide/analysis , China/epidemiology , Communicable Disease Control , Environmental Monitoring , Humans , Particulate Matter/analysis , Soot/analysis
12.
Comput Biol Med ; 139: 104887, 2021 12.
Article in English | MEDLINE | ID: covidwho-1482517

ABSTRACT

The 2019 novel severe acute respiratory syndrome coronavirus 2-SARS-CoV2, commonly known as COVID-19, is a highly infectious disease that has endangered the health of many people around the world. COVID-19, which infects the lungs, is often diagnosed and managed using X-ray or computed tomography (CT) images. For such images, rapid and accurate classification and diagnosis can be performed using deep learning methods that are trained using existing neural network models. However, at present, there is no standardized method or uniform evaluation metric for image classification, which makes it difficult to compare the strengths and weaknesses of different neural network models. This paper used eleven well-known convolutional neural networks, including VGG-16, ResNet-18, ResNet-50, DenseNet-121, DenseNet-169, Inception-v3, Inception-v4, SqueezeNet, MobileNet, ShuffeNet, and EfficientNet-b0, to classify and distinguish COVID-19 and non-COVID-19 lung images. These eleven models were applied to different batch sizes and epoch cases, and their overall performance was compared and discussed. The results of this study can provide decision support in guiding research on processing and analyzing small medical datasets to understand which model choices can yield better outcomes in lung image classification, diagnosis, disease management and patient care.


Subject(s)
COVID-19 , Deep Learning , Humans , Lung/diagnostic imaging , Neural Networks, Computer , RNA, Viral , SARS-CoV-2
13.
researchsquare; 2020.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-40285.v1

ABSTRACT

Background: Stress caused by the COVID-19 pandemic is highly correlated with depression and anxiety disorders, and there is currently a lack of understanding of the comorbidity network of these disorders. The purpose of this study is to explore the comorbidity network of depression, anxiety and stress during the COVID-19 pandemic through network analysis.Method: 887 participants are conducted a DASS 21 mental state survey across the country from February 18 to 22 in the outbreak of the COVID-19 pandemic in China. The network analysis method was used to explore the network relationship between these disorders, including the use of indicators of expected influence and bridge expected influence to explain the centrality of the network.Results: The strongest six edges were the connections between the symptoms within each group, including three depressive symptom edges initiative-anhedonia, hopeless-meaningless and worthless-meaningless, one anxiety symptom edge dyspneic-heart sick and two stress symptom edges over reactive-touchy and agitated-relax. Centrality indicators show that symptoms blue, relax, and intolerable have the strongest expected influence centrality. The results show that symptoms intolerable, sad mood and blue have the strongest bridge expected influence centrality.Conclusion: We found that symptoms blue, intolerable and relax are the core ones in the network, while dry and heartsick are less important ones. In addition, symptoms intolerable, sad mood and blue were also found to have the strongest bridge symptoms. Interventions against the core symptoms in this study will be more precise.


Subject(s)
COVID-19 , Anxiety Disorders , Depressive Disorder , Heart Diseases
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