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1.
Clin Exp Optom ; : 1-6, 2021 Nov 09.
Article in English | MEDLINE | ID: covidwho-1506717

ABSTRACT

CLINICAL RELEVANCE: Understanding the impact of the COVID-19 virus on the retinochoroidal vasculature can provide valuable information regarding potential multi-organ ischaemic sequelae in COVID-19 patients, and can thus be a useful tool for optometrists, ophthalmologists, pulmonologists, infectious disease specialists and others. BACKGROUND: Assessment of retinochoroidal vasculature alterations in recovered mild COVID-19 patients using optical coherence tomography angiography (OCTA) when compared to age and ethnic matched controls. METHODS: Multimodal imaging was performed using OCTA, spectral domain (SD)-OCT (Optovue RTVue XR Avanti; Optovue, Inc, Fremont, CA), and colour fundus photography (Compass; iCare Inc, Raleigh, NC). Vessel flow density, foveal avascular zone, foveal perimeter circumference and retinal thickness were calculated automatically by the OCTA software on 6 × 6mm angiograms. Morphologic changes in the retinochoroidal vasculature on OCTA were assessed and compared with the findings on fundoscopy, SD-OCT and fundus photography and were evaluated by two trained graders. RESULTS: Mean vessel parafoveal density, superior and inferior hemispheric vessel density and perifoveal temporal vessel density on 6 × 6 angiograms of the superficial capillary plexus were lower among the COVID-19 patients when compared to their age and ethnic matched controls. Vessel flow density of the deep capillary plexus, foveal avascular zone size and circumference and retinal thickness did not illustrate statistical significance between the groups. CONCLUSION: OCTA provides non-invasive high-resolution imaging of the retinochoroidal vascular network. Compared with conventional imaging, OCTA can demonstrate precise microvascular structural alterations in the retinal vessels before visible on SD-OCT or fundus examination. When matched for age and ethnicity, patients with a history of mild COVID illness manifested alterations in vessel density.

2.
Front Psychol ; 12: 741821, 2021.
Article in English | MEDLINE | ID: covidwho-1450838

ABSTRACT

Background: In the face of the 2019 Coronavirus Disease (COVID-19) outbreak, Chinese medical students worried about their future studies which might make them more susceptible to academic anxiety. Previous studies have shown that academic anxiety is an important risk factor for self-handicapping, but there are few studies to explore the relationship between the two which may be mediated or moderated by other variables. Therefore, this study investigated how Chinese medical students' academic anxiety is correlated to their self-handicapping in time of COVID-19 epidemic, and explored the moderating and mediating effects of hardiness and procrastination. Methods: In this study, 320 Chinese medical students' psychological traits were measured with Academic Anxiety Questionnaire, Self-Handicapping Scale, General Procrastination Scale and Hardiness Scale to explore the potential associations between these variables. Results: The most obvious finding to emerge from this study was that self- handicapping had a positive correlation with academic anxiety and procrastination, but had a negative correlation with hardiness; hardiness had a negative association with academic anxiety and procrastination; and academic anxiety and procrastination were positively correlated. In addition, the relationship between academic anxiety and self-handicapping of Chinese medical students was not only partially mediated by procrastination, but also moderated by hardiness. Furthermore, medical students who had lower hardiness had stronger direct effect, while the indirect effect was strong at high and low conditions of hardiness. Conclusion: In time of the COVID-19 epidemic, the academic anxiety and self-handicapping of medical students are influenced by procrastination and hardiness to a great extent. Thus, in addition to suggesting that more attention should be paid to the academic anxiety and procrastination of medical students, in the future, more attention should be paid to cultivating the hardiness of medical students and exerting its interventional role in self-handicapping.

3.
Nat Microbiol ; 6(10): 1245-1258, 2021 10.
Article in English | MEDLINE | ID: covidwho-1380902

ABSTRACT

Respiratory failure is associated with increased mortality in COVID-19 patients. There are no validated lower airway biomarkers to predict clinical outcome. We investigated whether bacterial respiratory infections were associated with poor clinical outcome of COVID-19 in a prospective, observational cohort of 589 critically ill adults, all of whom required mechanical ventilation. For a subset of 142 patients who underwent bronchoscopy, we quantified SARS-CoV-2 viral load, analysed the lower respiratory tract microbiome using metagenomics and metatranscriptomics and profiled the host immune response. Acquisition of a hospital-acquired respiratory pathogen was not associated with fatal outcome. Poor clinical outcome was associated with lower airway enrichment with an oral commensal (Mycoplasma salivarium). Increased SARS-CoV-2 abundance, low anti-SARS-CoV-2 antibody response and a distinct host transcriptome profile of the lower airways were most predictive of mortality. Our data provide evidence that secondary respiratory infections do not drive mortality in COVID-19 and clinical management strategies should prioritize reducing viral replication and maximizing host responses to SARS-CoV-2.


Subject(s)
Bronchoalveolar Lavage Fluid/microbiology , COVID-19/therapy , Respiration, Artificial , SARS-CoV-2/pathogenicity , Adaptive Immunity , Adult , Aged , Bacteria/classification , Bacteria/genetics , Bacteria/isolation & purification , Bacterial Load , Bronchoalveolar Lavage Fluid/immunology , Bronchoalveolar Lavage Fluid/virology , COVID-19/immunology , COVID-19/microbiology , COVID-19/mortality , Critical Illness , Female , Hospitalization , Humans , Immunity, Innate , Male , Microbiota , Middle Aged , Odds Ratio , Prognosis , Prospective Studies , Respiratory System/immunology , Respiratory System/microbiology , Respiratory System/virology , SARS-CoV-2/immunology , Viral Load
4.
Pers Individ Dif ; 185: 111222, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1364395

ABSTRACT

This present study aimed to examine the mediating role of rumination and the moderating role of self-control in the link between perceived stress and mobile phone addiction during the COVID-19 epidemic. A total of 628 college students completed Depression-Anxiety-Stress Scale, Smartphone Addiction Scale, Ruminative Responses Scale and Self-Control Scale. Mediation analysis highlighted that rumination mediated the association between perceived stress and mobile phone addiction. Moderated mediation analysis indicated that the indirect association between perceived stress and mobile phone addiction were moderated by self-control. Between the COVID affected group and the unaffected group, some differences also be observed in the moderating effect of self-control. This study emphasize the importance of rumination and self-control in understanding the possible mechanisms underlying the relationship between perceived stress and mobile phone addiction, which can be used to develop interventions to reduce the problematic behavior among college students during the COVID-19 pandemic.

5.
IEEE Trans Neural Netw Learn Syst ; 32(9): 3786-3797, 2021 09.
Article in English | MEDLINE | ID: covidwho-1348109

ABSTRACT

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.


Subject(s)
COVID-19/diagnostic imaging , Machine Learning , Research Report/standards , Algorithms , Artificial Intelligence , China , Humans , Image Interpretation, Computer-Assisted , Terminology as Topic , Tomography, X-Ray Computed , Transfer, Psychology , Writing
6.
Front Psychol ; 12: 687165, 2021.
Article in English | MEDLINE | ID: covidwho-1337674

ABSTRACT

Background: The effectiveness of computerized cognitive behavioral therapy (CCBT) has been proven for mild and moderate anxiety and depression. In 2016, the first official Chinese CCBT system was launched by Chinese Cognitive Behavior Therapy Professional Organizations and included four items: getting out of depression, overcoming anxiety, staying away from insomnia and facing Obsessive-compulsive disorder. During the COVID-19 epidemic, Chinese CCBT system served the public for free. This study explored the effects of CCBT on anxiety and depression by comparing the use of the platform during the epidemic and during the same period in 2019. Methods: Users were divided into a depression group or an anxiety group according to their own discretion. The subjects used the self-rating anxiety scale (SAS) and self-rating depression scale (SDS) before each training. Each training group completed the corresponding CCBT training project, which had 5-6 training sessions, an average of once every 5 days. The training content in 2019 and 2020 was identical. This study compared the demographic characteristics, depression, and anxiety levels of CCBT platform users during the lockdown period in Wuhan (LP2020), where the outbreak was concentrated in China, from January 23 to July 23, 2020 and the same period in 2019 (SP2019). Result: (1) There were significant differences in gender (χ2 = 7.215, P = 0.007), region (χ2 = 4.225, P = 0.040) and duration of illness (χ2 = 7.867, P = 0.049) between the two periods. (2) There was a positive Pearson correlation between the number of users of CCBT platform during LP2020 and number of confirmed cases of COVID-19 in each province (r = 0.9429, P < 0.001). (3) In LP2020, the SAS (t = 2.579, P = 0.011) and SDS (t = 2.894, P = 0.004) scores at T0 in Hubei were significantly higher than those in other regions. (4) The CCBT platform has an obvious effect on anxiety (F = 4.74, P = 0.009) and depression on users (F = 4.44, P = 0.009). Conclusion: This study showed women, students and people who are more seriously affected by the epidemic were more likely to accept the CCBT training. The CCBT platform made a significant contribution toward alleviating the anxiety and depression symptoms of users during the epidemic. When face-to-face psychotherapy is not available during the epidemic, CCBT can be used as an effective alternative.

7.
J Transl Int Med ; 9(2): 131-142, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1332092

ABSTRACT

Background and Objectives: The majority of coronavirus disease 2019 (COVID-19) cases are nonsevere, but severe cases have high mortality and need early detection and treatment. We aimed to develop a nomogram to predict the disease progression of nonsevere COVID-19 based on simple data that can be easily obtained even in primary medical institutions. Methods: In this retrospective, multicenter cohort study, we extracted data from initial simple medical evaluations of 495 COVID-19 patients randomized (2:1) into a development cohort and a validation cohort. The progression of nonsevere COVID-19 was recorded as the primary outcome. We built a nomogram with the development cohort and tested its performance in the validation cohort. Results: The nomogram was developed with the nine factors included in the final model. The area under the curve (AUC) of the nomogram scoring system for predicting the progression of nonsevere COVID-19 into severe COVID-19 was 0.875 and 0.821 in the development cohort and validation cohort, respectively. The nomogram achieved a good concordance index for predicting the progression of nonsevere COVID-19 cases in the development and validation cohorts (concordance index of 0.875 in the development cohort and 0.821 in the validation cohort) and had well-fitted calibration curves showing good agreement between the estimates and the actual endpoint events. Conclusions: The proposed nomogram built with a simplified index might help to predict the progression of nonsevere COVID-19; thus, COVID-19 with a high risk of disease progression could be identified in time, allowing an appropriate therapeutic choice according to the potential disease severity.

8.
Nano Today ; 40: 101243, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1300951

ABSTRACT

The outbreak of SARS-coronavirus 2 (SARS-CoV2) has become a global health emergency. Although enormous efforts have been made, there is still no effective treatment against the new virus. Herein, a TiO2 supported single-atom nanozyme containing atomically dispersed Ag atoms (Ag-TiO2 SAN) is designed to serve as a highly efficient antiviral nanomaterial. Compared with traditional nano-TiO2 and Ag, Ag-TiO2 SAN exhibits higher adsorption (99.65%) of SARS-CoV2 pseudovirus. This adsorption ability is due to the interaction between SAN and receptor binding domain (RBD) of spike 1 protein of SARS-CoV2. Theoretical calculation and experimental evidences indicate that the Ag atoms of SAN strongly bind to cysteine and asparagine, which are the most abundant amino acids on the surface of spike 1 RBD. After binding to the virus, the SAN/virus complex is typically phagocytosed by macrophages and colocalized with lysosomes. Interestingly, Ag-TiO2 SAN possesses high peroxidase-like activity responsible for reactive oxygen species production under acid conditions. The highly acidic microenvironment of lysosomes could favor oxygen reduction reaction process to eliminate the virus. With hACE2 transgenic mice, Ag-TiO2 SAN showed efficient anti-SARS-CoV2 pseudovirus activity. In conclusion, Ag-TiO2 SAN is a promising nanomaterial to achieve effective antiviral effects for SARS-CoV2.

9.
Nat Sci Sleep ; 13: 703-712, 2021.
Article in English | MEDLINE | ID: covidwho-1262570

ABSTRACT

Introduction: The prevalence rate and related factors of insomnia remained unknown after the COVID-19 epidemic had been under control. Therefore, we conducted this survey to investigate the prevalence rate and related factors of insomnia symptoms in the Chinese general public after the COVID-19 had been initially control. Methods: An online survey was conducted among Chinese citizens through the JD Health APP. The questionnaire was used for collecting demographic data and self-designed questions related to the COVID-19 outbreak. Insomnia Severity Index, Patient Health Questionnaire-9, Somatic Symptom Scale-8 and Impact of Events Scale-Revised were used for measuring psychological symptoms. To examine the associations of sociodemographic and psychological factors with insomnia symptoms, a binary logistic regression was used. Results: In total, there were 14,894 eligible participants, and 4601 (30.9%) participants were found to have insomnia symptoms. The regression model revealed that a higher risk of insomnia symptoms was associated with being over the age of 40 years, having history of psychiatric disorders, smoking, having infected friends or colleagues, having depressive or somatic symptoms, experiencing psychological distress and feeling estranged from family members. Meanwhile a lower risk of insomnia symptoms was associated with being female, having closer family relationships, not feeling alienated from others and being satisfied with the available information. Conclusion: In our study, 30.9% of the participants in the general public reported insomnia symptoms after the COVID-19 epidemic had been initially controlled. When providing precise interventions for insomnia, extra attention should be paid to the individuals who are male, elderly and smokers, and those with psychiatric disorder history, with infected friends or colleagues, with psychological symptoms and with poor social support.

10.
Epidemiologia ; 2(2):207-226, 2021.
Article in English | MDPI | ID: covidwho-1259455

ABSTRACT

The COVID-19 pandemic has placed an unprecedented burden on public health and strained the worldwide economy. The rapid spread of COVID-19 has been predominantly driven by aerosol transmission, and scientific research supports the use of face masks to reduce transmission. However, a systematic and quantitative understanding of how face masks reduce disease transmission is still lacking. We used epidemic data from the Diamond Princess cruise ship to calibrate a transmission model in a high-risk setting and derive the reproductive number for the model. We explain how the terms in the reproductive number reflect the contributions of the different infectious states to the spread of the infection. We used that model to compare the infection spread within a homogeneously mixed population for different types of masks, the timing of mask policy, and compliance of wearing masks. Our results suggest substantial reductions in epidemic size and mortality rate provided by at least 75% of people wearing masks (robust for different mask types). We also evaluated the timing of the mask implementation. We illustrate how ample compliance with moderate-quality masks at the start of an epidemic attained similar mortality reductions to less compliance and the use of high-quality masks after the epidemic took off. We observed that a critical mass of 84% of the population wearing masks can completely stop the spread of the disease. These results highlight the significance of a large fraction of the population needing to wear face masks to effectively reduce the spread of the epidemic. The simulations show that early implementation of mask policy using moderate-quality masks is more effective than a later implementation with high-quality masks. These findings may inform public health mask-use policies for an infectious respiratory disease outbreak (such as one of COVID-19) in high-risk settings.

11.
Front Public Health ; 9: 671400, 2021.
Article in English | MEDLINE | ID: covidwho-1256411

ABSTRACT

The prevalence and related factors of mental health impact among medical staffs who experienced the second wave of the COVID-19 pandemic in China is unknown. Therefore, this survey was conducted to investigate the prevalence and related factors of depressive, anxiety, acute stress, and insomnia symptoms in medical staffs in Kashi, Xinjiang, China during the second wave of the COVID-19 pandemic. A cross-sectional online survey was conducted among medical staffs working in First People's Hospital of Kashi, Xinjiang. The questionnaire collected demographic data and self-design questions related to the COVID-19 pandemic. The Impact of Events Scale-6, the Insomnia Severity Index, the Patient Health Questionnaire-9, the Generalized Anxiety Disorder Scale-7, the Perceived Social Support Scale, the Chinese Big Five Personality Inventory-15, and the Trait Coping Style Questionnaire were used to measure psychological symptoms or characteristics. Binary logistic regression was carried out to examine the associations between socio-demographic factors and symptoms of depression, anxiety, stress, and insomnia. In total, data from 123 participants were finally included, among which the prevalence rate of depressive, anxiety, acute stress, and insomnia symptoms is 60.2, 49.6, 43.1, and 41.1%, respectively. The regression model revealed that minority ethnicity, being worried about infection, spending more time on following pandemic information, and neurotic personality were positively associated with the mental health symptoms, while extraversion personality, higher education level, and better social support were negatively associated. In our study, the prevalence of mental health impact was high among medical staffs in Kashi, China who experienced the second wave of the COVID-19 pandemic. Several factors were found to be associated with mental health conditions. These findings could help identify medical staffs at risk for mental health problems and be helpful for making precise mental health intervention policies during the resurgence. Our study may pave way for more research into Xinjiang during the COVID-19 pandemic.


Subject(s)
COVID-19 , Sleep Initiation and Maintenance Disorders , Anxiety/epidemiology , China/epidemiology , Cross-Sectional Studies , Depression/epidemiology , Humans , Medical Staff , Pandemics , Prevalence , SARS-CoV-2 , Sleep Initiation and Maintenance Disorders/epidemiology
12.
Front Med (Lausanne) ; 8: 630802, 2021.
Article in English | MEDLINE | ID: covidwho-1211821

ABSTRACT

Purpose: This study aimed to compare the clinical characteristics, laboratory findings, and chest computed tomography (CT) findings of familial cluster (FC) and non-familial (NF) patients with coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective study included 178 symptomatic adult patients with laboratory-confirmed COVID-19. The 178 patients were divided into FC (n = 108) and NF (n = 70) groups. Patients with at least two confirmed COVID-19 cases in their household were classified into the FC group. The clinical and laboratory features between the two groups were compared and so were the chest CT findings on-admission and end-hospitalization. Results: Compared with the NF group, the FC group had a longer period of exposure (13.1 vs. 8.9 days, p < 0.001), viral shedding (21.5 vs. 15.9 days, p < 0.001), and hospital stay (39.2 vs. 22.2 days, p < 0.001). The FC group showed a higher number of involved lung lobes on admission (3.0 vs. 2.3, p = 0.017) and at end-hospitalization (3.6 vs. 1.7, p < 0.001) as well as higher sum severity CT scores at end-hospitalization (4.6 vs. 2.7, p = 0.005) than did the NF group. Conversely, the FC group had a lower lymphocyte count level (p < 0.001) and a significantly lower difference in the number of involved lung lobes (Δnumber) between admission and discharge (p < 0.001). Notably, more cases of severe or critical illness were observed in the FC group than in the NF group (p = 0.036). Conclusions: Patients in the FC group had a worse clinical course and outcome than those in the NF group; thus, close monitoring during treatment and follow-ups after discharge would be beneficial for patients with familial infections.

13.
Br J Radiol ; 94(1122): 20201007, 2021 Jun 01.
Article in English | MEDLINE | ID: covidwho-1197360

ABSTRACT

OBJECTIVES: To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19). METHODS: Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve. RESULTS: A total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%. CONCLUSIONS: Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation. ADVANCES IN KNOWLEDGE: Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.


Subject(s)
COVID-19/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Decision Trees , Disease Progression , Female , Humans , Logistic Models , Male , Middle Aged , Pandemics , Pneumonia, Viral/virology , Predictive Value of Tests , SARS-CoV-2 , Sensitivity and Specificity , Support Vector Machine
14.
Front Med (Lausanne) ; 8: 643917, 2021.
Article in English | MEDLINE | ID: covidwho-1178002

ABSTRACT

Objectives: Visual chest CT is subjective with interobserver variability. We aimed to quantify the dynamic changes of lung and pneumonia on three-dimensional CT (3D-CT) images in coronavirus disease 2019 (COVID-19) patients during hospitalization. Methods: A total of 110 laboratory-confirmed COVID-19 patients who underwent chest CT from January 3 to February 29, 2020 were retrospectively reviewed. Pneumonia lesions were classified as four stages: early, progressive, peak, and absorption stages on chest CT. A computer-aided diagnostic (CAD) system calculated the total lung volume (TLV), the percentage of low attenuation areas (LAA%), the volume of pneumonia, the volume of ground-glass opacities (GGO), the volume of consolidation plus the GGO/consolidation ratio. The CT score was visually assessed by radiologists. Comparisons of lung and pneumonia parameters among the four stages were performed by one-way ANOVA with post-hoc tests. The relationship between the CT score and the volume of pneumonia, and between LAA% and the volume of pneumonia in four stages was assessed by Spearman's rank correlation analysis. Results: A total of 534 chest CT scans were performed with a median interval of 4 days. TLV, LAA%, and the GGO/consolidation ratio were significantly decreased, while the volume of pneumonia, GGO, and consolidation were significantly increased in the progressive and peak stages (for all, P < 0.05). The CT score was significantly correlated with the pneumonia volume in the four stages (r = 0.731, 0.761, 0.715, and 0.669, respectively, P < 0.001). Conclusion: 3D-CT could be used as a useful quantification method in monitoring the dynamic changes of COVID-19 pneumonia.

15.
J Psychiatr Res ; 137: 393-400, 2021 05.
Article in English | MEDLINE | ID: covidwho-1135471

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has imposed both physical and psychological burdens on healthcare workers (HCWs). What is more, few studies have focused on the gender differences in mental health problems (MHPs) among HCWs during such an outbreak. Thus, the current study investigated the prevalence and gender differences of various MHPs among HCWs in China during the COVID-19 outbreak. This nationwide survey was conducted online from January 29 to February 3, 2020. General information was collected by questions about socio-demographics, work-related factors, and living situations. Depressive, anxiety, stress, and insomnia symptoms were assessed by the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Impact of Event Scale-Revised, and the Insomnia Severity Index, respectively. Among the 2198 contacted HCWs, 1563 (71.1%) responded with valid data, of whom 1293 (82.7%) were females. The prevalences of depressive, anxiety, stress, and insomnia symptoms in participants were 50.7%, 44.7%, 52.5%, and 36.1%, respectively. Female HCWs had significantly higher scores in all four scales (p < 0.001) and higher prevalences in all MHPs involved (range, odds ratio [OR] 1.55-1.97). After adjusting for potential confounders, female HCWs still had higher risks for all MHPs involved than males (range, adjusted OR 1.36-1.96). HCWs present high prevalences of depressive, anxiety, stress, and insomnia symptoms during the COVID-19 outbreak. Furthermore, female HCWs are more vulnerable to all MHPs involved. These findings highlight the need for timely, special care and support for HCWs during the outbreak, especially for females.


Subject(s)
COVID-19/epidemiology , Health Personnel/psychology , Health Personnel/statistics & numerical data , Mental Health/statistics & numerical data , Adolescent , Adult , Anxiety/epidemiology , China/epidemiology , Cross-Sectional Studies , Depression/epidemiology , Disease Outbreaks , Female , Humans , Male , Middle Aged , Sex Factors , Sleep Initiation and Maintenance Disorders/epidemiology , Stress, Psychological/epidemiology , Young Adult
16.
J Thorac Dis ; 13(2): 1215-1229, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1134641

ABSTRACT

Background: To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness. Methods: A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC). Results: The mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913-1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757-1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906-1.000). Conclusions: A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.

17.
Int J Biol Sci ; 17(2): 539-548, 2021.
Article in English | MEDLINE | ID: covidwho-1090199

ABSTRACT

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction. Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians. Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnostic imaging , Influenza, Human/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic , Aged , COVID-19/epidemiology , COVID-19/physiopathology , COVID-19/virology , Deep Learning , Diagnosis, Differential , Humans , Influenza A virus/isolation & purification , Influenza B virus/isolation & purification , Influenza, Human/physiopathology , Influenza, Human/virology , Male , Middle Aged , Pandemics , Pneumonia , Pneumonia, Viral/physiopathology , Pneumonia, Viral/virology , ROC Curve , Retrospective Studies , SARS-CoV-2/isolation & purification , Sensitivity and Specificity
18.
Sleep Med ; 2021 Jan 18.
Article in English | MEDLINE | ID: covidwho-1065597

ABSTRACT

OBJECTIVE: Rapidly increasing numbers of confirmed cases and deaths during the 2019 coronavirus disease outbreak (COVID-19) resulted in widespread psychological problems in the Chinese population. The purpose of this study was to investigate the sleep quality and changes in sleep patterns before and during the outbreak in the general population in China and to determine factors related to sleep quality. METHODS: This cross-sectional study was conducted using an online questionnaire from 20 February to 29 February 2020 in China. Socio-demographic data, self-designed COVID-19-related characteristics, sleep patterns, and Pittsburgh Sleep Quality Index (PSQI) scores were obtained. Single factor analysis and multivariate binary logistic regression analysis were used. RESULTS: A total of 1897 individuals were included in our study, and 30.0% of participants reported suffering poor sleep quality (PSQI≥8). Logistic regression analysis found that the factors related to sleep quality included poor physical health (OR = 3.382, p < 0.001), respiratory disease (OR = 1.629, p = 0.008), other diseases (OR = 2.504, p = 0.012), suspected case of COVID-19 in the same community (OR = 1.928, p = 0.002), confirmed case of COVID-19 in the same community (OR = 2.183, p = 0.007), worry about being infected (OR = 2.336, p < 0.001), ≥1 h/day spent hearing COVID-19 information (OR = 1.960, p < 0.001), time difference in midpoint time in bed (OR = 1.230, p < 0.001), and time difference in time in bed (OR = 0.711, p < 0.001). CONCLUSIONS: Our study revealed that more than one-fourth of the participants suffered poor sleep quality during the COVID-19 outbreak. In addition to the poor health status and COVID-19-related anxiety, delayed sleep phase and reduced time in bed impacted sleep quality in the general population in China.

19.
Front Neurosci ; 14: 622749, 2020.
Article in English | MEDLINE | ID: covidwho-1044856

ABSTRACT

Objective: In the current global home confinement due to COVID-19, most individuals are facing unprecedented stress which can induce situational insomnia. We explored the efficacy of self-guided online cognitive behavioral treatment for insomnia (CBTI) on situational insomnia during the COVID-19 outbreak. Methods: Participants were recruited from March to April in 2020 in Guangzhou, China. A 1-week Internet CBTI intervention was performed for all individuals with situational insomnia. The Pre-sleep Arousal Scale (PSAS), Insomnia Severity Index (ISI), and Hospital Anxiety and Depression Scale (HADS) were measured before and after the intervention and compared between individuals who completed the intervention and those who did not. Results: One hundred and ninety-four individuals with situational insomnia were included. For PSAS score, significant group effects were found on total score (p = 0.003), somatic score (p = 0.014), and cognitive score (p = 0.009). Time effect was significant on total score (p = 0.004) and cognitive score (p < 0.001). There was a significant group × time effect of the somatic score (p = 0.025). For ISI total score, there were significant time effect (p < 0.001) and group × time effect (p = 0.024). For the HADS score, a significant group effect was found on the anxiety score (p = 0.045). The HADS had significant time effects for anxiety and depressive symptoms (all p < 0.001). Conclusion: Our study suggests good efficacy of CBTI on situational insomnia during COVID-19 for adults in the community, as well as on pre-sleep somatic hyperarousal symptom. The CBTI intervention is not applied to improve pre-sleep cognitive hyperarousal, depression, and anxiety symptoms.

20.
Asian J Psychiatr ; 56: 102547, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1037163

ABSTRACT

OBJECTIVE: We aim to evaluate the prevalence of depression and anxiety among general public and healthcare workers during COVID-19 in China and the changes of prevalence before and after the peak of the epidemic occurred. METHODS: Studies were searched from following database: PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), WANGFANG DATA, from inception to 1 st May 2020. Random-effects model was applied to pool the prevalence. Comparative analysis was also applied to evaluate the changes of prevalence before and after the peak of the epidemic occurred. RESULTS: 34 articles were finally included. Prevalence of depression and anxiety was higher among healthcare worker than general public. Among general public, 26 % (95 %CI: 17 %-36 %) were suffering from depression and 22 % (95 %CI: 15 %-30 %) were having anxiety during COVID-19, while the prevalence of depression and anxiety among healthcare workers was 31 % (95 %CI: 25 %-37 %) and 40 % (95 %CI: 33 %-46 %) respectively. Comparative analysis showed healthcare workers (depression: 40 %, anxiety: 38 %) had higher percentage of having depression and anxiety than the general public (depression: 33 %, anxiety: 24 %) before the peak. Then a descended prevalence among healthcare workers (depression: 22 %, anxiety: 22 %) was detected compared with that before, while the prevalence among the general public raised (depression: 62 %, anxiety: 44 %) after the peak occurred. CONCLUSION: The COVID-19 epidemic had a potential psychiatric impact on general public and healthcare workers in China, which is more severer among healthcare workers. However, the psychiatric status of the general public trend to deteriorated, while healthcare workers trend to improve after the peak of epidemic.


Subject(s)
Anxiety Disorders/epidemiology , Anxiety/epidemiology , COVID-19/epidemiology , Depression/epidemiology , Depressive Disorder/epidemiology , Health Personnel/statistics & numerical data , China/epidemiology , Health Personnel/psychology , Humans , Prevalence , SARS-CoV-2
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