Perceived Stress Positively Relates to Insomnia Symptoms: The Moderation of Resilience in Chinese Pregnant Women During COVID-19.

Background: The government's COVID-19 pandemic response lockdown strategy had a negative psychological and physical impact on individuals, which necessitated special care to pregnant women's mental health. There has been no large-scale research on the underlying relationship between perceived stress and insomnia symptoms in pregnant Chinese women up to this point. During the COVID-19 pandemic, we wanted to see if there was an association between perceived stress and insomnia symptoms, as well as the moderating impact of resilience for Chinese pregnant women. Methods: This cross-sectional study examined 2115 pregnant women from central and western China using multi-stage sampling methodologies. A systematic questionnaire was used to collect information on sleep quality, perceived stress, and resilience using the Insomnia Severity Index, Perceptual Stress Scale, and Connor and Davidson Resilience Scale. To assess the moderating influence of resilience, hierarchical regressions were used. Results: During the COVID-19 pandemic, 18.53% of respondents (N = 2115) reported experiencing sleeplessness. In pregnant women, perceived stress was positively linked with insomnia symptoms (p < 0.001). Furthermore, resilience significantly attenuated the influence of perceived stress on insomnia symptoms in Chinese expectant mother (ßinteraction = -0.0126, p < 0.001). Conclusion: Pregnant women with strong resilience were less influenced by perceived stress than those with poor resilience. The findings of this study might give empirical proof that health care professionals should identify the relevance of reducing perceived stress in pregnant women with poor resilience and provide better treatment and support when necessary.

Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic.

BACKGROUND: With outbreaks of COVID-19 around the world, lockdown restrictions are routinely imposed to limit the spread of the virus. During periods of lockdown, social media has become the main channel for citizens to exchange information with others. Public emotions are being generated and shared rapidly online with citizens using internet platforms to reduce anxiety and stress, and stay connected while isolated. OBJECTIVES: This study aims to explore the regularity of emotional evolution by examining public emotions expressed in online discussions about the Wuhan lockdown event in January 2020. METHODS: Data related to the Wuhan lockdown was collected from Sina Weibo by web crawler. In this study, the Ortony, Clore, and Collins (OCC) model, Word2Vec, and Bi-directional Long Short-Term Memory model were employed to determine emotional types, train vectorization of words, and identify each text emotion for the training set. Latent Dirichlet Allocation models were also employed to mine the various topic categories, while topic emotional evolution was visualized. RESULTS: Seven types of emotions and four phases were categorized to describe emotional evolution on the Wuhan lockdown event. The study found that negative emotions such as blame and fear dominated in the early days, and public attitudes towards the lockdown gradually alleviated and reached a balance as the situation improved. Emotional expression about Wuhan lockdown event were significantly related to users' gender, location, and whether or not their account was verified. There were statistically significant correlations between different emotions within the subtle emotional categories. In addition, the evolution of emotions presented a different path due to different topics. CONCLUSIONS: Multiple emotional categories were determined in our study, providing a detailed and explainable emotion analysis to explored emotional appeal of citizen. The public emotions were gradually easing related to the Wuhan lockdown event, there yet exists regional discrimination and post-traumatic stress disorder in this process, which would lead us to pay continuous attention to citizens lives and psychological status post-pandemic. In addition, this study provided an appropriate method and reference case for the government's public opinion control and emotional appeasement.

AFCM-LSMA: New Intelligent Model based on Lévy Slime Mould Algorithm and Adaptive Fuzzy C-means for Identification of COVID-19 Infection from Chest X-ray Images

Problem: A worldwide challenge is to provide medical resources required for COVID-19 detection. They must be effective tools for fast detection and diagnose of the virus using a large number of tests;besides, they should be low-cost developments. While a chest x-ray scan is a powerful candidate tool, if several tests are carried out, the images produced by the devices must be interpreted accurately and rapidly. COVID-19 induces longitudinal pulmonary parenchymal ground-glass and consolidates pulmonary opacity, in some cases with rounded morphology and peripheral lung distribution, which is very difficult to predict in an early stage. Aim In this paper, we aim to develop a robust model to extract high-level features of COVID-19 from chest x-ray (CXR) images to help in rapid diagnosis. In specific, this paper proposes an optimization model for COVID-19 diagnosis based on adaptive Fuzzy C-means (AFCM) and improved Slime Mould Algorithm (SMA) based on Lévy distribution, namely AFCM-LSMA. Methods The SMA optimizer is proposed to adapt weights in oscillation mode and to mimic the process of generating positive and negative feedback from the propagation wave to shape the optimum path for food connectivity. Lévy motion is used as a permutation to perform a local search and to adapt SMA optimizer (LSMA) by generating several solutions that are apart from current candidates. Furthermore, it permits the optimizer to escape from local minima, examine large search areas and reach optimal solutions in fewer iterations with high convergence speed. The FCM algorithm is used to segment pulmonary regions from CXR images and is adapted to reduce time and amount of computations using histogram of the image intensities during the clustering process. Results The performance of the proposed AFCM-LSMA has been validated on CXR images and compared with different conventional machine learning and deep learning techniques, meta-heuristics methods, and different chaotic maps. The accuracies achieved by the proposed model are around (ACC=0.96, RMSE=0.23, Prec.=0.98, F1_score=0.98, MCC=0.79, and Kappa=0.79). Conclusion The experimental findings indicate that the proposed new method outperforms all other methods, which will be beneficial to the clinical practitioner for the early identification of infected COVID-19 patients.

Lopinavir/ritonavir is associated with pneumonia resolution in COVID-19 patients with influenza coinfection: A retrospective matched-pair cohort study.

During the early stages of the pandemic, some coronavirus disease (COVID-19) patients were misdiagnosed as having influenza, which aroused the concern that some deaths attributed to influenza were actually COVID-19-related. However, little is known about whether coinfection with influenza contributes to severity of COVID-19 pneumonia, and the optimal therapeutic strategy for these patients. We retrospectively studied 128 hospitalized patients with COVID-19 pneumonia. All patients were positive severe acute respiratory syndrome coronavirus 2 positive by nucleic acid detection. Sixty-four cases were coinfected with influenza A/B and the other 64 were influenza negative, matched by age, sex, and days from onset of symptoms. Among the 64 coinfected patients, 54 (84.4%) were coinfected with influenza A, and 10 (15.6%) with influenza B. The median duration of viral shedding time from admission was longer for patients with influenza coinfection (17.0 days) than for those without influenza coinfection (12.0 days) (P < .001). The multivariable Cox proportional hazards model showed that the hazards ratio of resolution in lung involvement was 1.878 (P = .020) for patients administered lopinavir/ritonavir, compared with those not administered lopinavir/ritonavir (95% confidence interval: 1.103-3.196). Among influenza coinfected patients, those treated with lopinavir/ritonavir exhibited faster pneumonia resolution within 2 weeks after symptom onset (37% vs 1%; P = .001). There was no difference in lung involvement between influenza coinfected and noninfected groups. Lopinavir/ritonavir eliminated the difference of lung involvement between influenza coinfected and noninfected groups, indicating that lopinavir/ritonavir is associated with pneumonia resolution in COVID-19.

Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data.

BACKGROUND: Wuhan, China, the epicenter of the COVID-19 pandemic, imposed citywide lockdown measures on January 23, 2020. Neighboring cities in Hubei Province followed suit with the government enforcing social distancing measures to restrict the spread of the disease throughout the province. Few studies have examined the emotional attitudes of citizens as expressed on social media toward the imposed social distancing measures and the factors that affected their emotions. OBJECTIVE: The aim of this study was twofold. First, we aimed to detect the emotional attitudes of different groups of users on Sina Weibo toward the social distancing measures imposed by the People's Government of Hubei Province. Second, the influencing factors of their emotions, as well as the impact of the imposed measures on users' emotions, was studied. METHODS: Sina Weibo, one of China's largest social media platforms, was chosen as the primary data source. The time span of selected data was from January 21, 2020, to March 24, 2020, while analysis was completed in late June 2020. Bi-directional long short-term memory (Bi-LSTM) was used to analyze users' emotions, while logistic regression analysis was employed to explore the influence of explanatory variables on users' emotions, such as age and spatial location. Further, the moderating effects of social distancing measures on the relationship between user characteristics and users' emotions were assessed by observing the interaction effects between the measures and explanatory variables. RESULTS: Based on the 63,169 comments obtained, we identified six topics of discussion-(1) delaying the resumption of work and school, (2) travel restrictions, (3) traffic restrictions, (4) extending the Lunar New Year holiday, (5) closing public spaces, and (6) community containment. There was no multicollinearity in the data during statistical analysis; the Hosmer-Lemeshow goodness-of-fit was 0.24 (χ28=10.34, P>.24). The main emotions shown by citizens were negative, including anger and fear. Users located in Hubei Province showed the highest amount of negative emotions in Mainland China. There are statistically significant differences in the distribution of emotional polarity between social distancing measures (χ220=19,084.73, P<.001), as well as emotional polarity between genders (χ24=1784.59, P<.001) and emotional polarity between spatial locations (χ24=1659.67, P<.001). Compared with other types of social distancing measures, the measures of delaying the resumption of work and school or travel restrictions mainly had a positive moderating effect on public emotion, while traffic restrictions or community containment had a negative moderating effect on public emotion. CONCLUSIONS: Findings provide a reference point for the adoption of epidemic prevention and control measures, and are considered helpful for government agencies to take timely actions to alleviate negative emotions during public health emergencies.

Delayed-phase thrombocytopenia in patients with coronavirus disease 2019 (COVID-19).

Coronavirus disease 2019 (COVID-19) can affect the haematopoietic system. Thrombocytopenia at admission was prevalent, while late-phase or delayed-phase thrombocytopenia (occurred 14 days after symptom onset) is rare. This retrospective, single-centre study screened 450 COVID-19 patients and enrolled 271 patients at the Union Hospital, Wuhan, China, from January 25 to March 9, 2020. COVID-19-associated delayed-phase thrombocytopenia occurred in 11·8% of enrolling patients. The delayed-phase thrombocytopenia in COVID-19 is prone to develop in elderly patients or patients with low lymphocyte count on admission. The delayed-phase thrombocytopenia is significantly associated with increased length of hospital stay and higher mortality rate. Delayed-phase nadir platelet counts demonstrated a significantly negative correlation with B cell percentages. We also provided and described bone marrow aspiration pathology of three patients with delayed-phase thrombocytopenia, showing impaired maturation of megakaryocytes. We speculated that immune-mediated platelet destruction might account for the delayed-phase thrombocytopenia in a group of patients. In addition, clinicians need to pay attention to the delayed-phase thrombocytopenia especially at 3-4 weeks after symptom onset.

Renal Involvement and Early Prognosis in Patients with COVID-19 Pneumonia.

BACKGROUND: Some patients with COVID-19 pneumonia also present with kidney injury, and autopsy findings of patients who died from the illness sometimes show renal damage. However, little is known about the clinical characteristics of kidney-related complications, including hematuria, proteinuria, and AKI. METHODS: In this retrospective, single-center study in China, we analyzed data from electronic medical records of 333 hospitalized patients with COVID-19 pneumonia, including information about clinical, laboratory, radiologic, and other characteristics, as well as information about renal outcomes. RESULTS: We found that 251 of the 333 patients (75.4%) had abnormal urine dipstick tests or AKI. Of 198 patients with renal involvement for the median duration of 12 days, 118 (59.6%) experienced remission of pneumonia during this period, and 111 of 162 (68.5%) patients experienced remission of proteinuria. Among 35 patients who developed AKI (with AKI identified by criteria expanded somewhat beyond the 2012 Kidney Disease: Improving Global Outcomes definition), 16 (45.7%) experienced complete recovery of kidney function. We suspect that most AKI cases were intrinsic AKI. Patients with renal involvement had higher overall mortality compared with those without renal involvement (28 of 251 [11.2%] versus one of 82 [1.2%], respectively). Stepwise multivariate binary logistic regression analyses showed that severity of pneumonia was the risk factor most commonly associated with lower odds of proteinuric or hematuric remission and recovery from AKI. CONCLUSIONS: Renal abnormalities occurred in the majority of patients with COVID-19 pneumonia. Although proteinuria, hematuria, and AKI often resolved in such patients within 3 weeks after the onset of symptoms, renal complications in COVID-19 were associated with higher mortality.

Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan.

BACKGROUND: In December 2019, the coronavirus disease 2019 (COVID-19) outbreak occurred in Wuhan. Data on the clinical characteristics and outcomes of patients with severe COVID-19 are limited. OBJECTIVE: We sought to evaluate the severity on admission, complications, treatment, and outcomes of patients with COVID-19. METHODS: Patients with COVID-19 admitted to Tongji Hospital from January 26, 2020, to February 5, 2020, were retrospectively enrolled and followed-up until March 3, 2020. Potential risk factors for severe COVID-19 were analyzed by a multivariable binary logistic model. Cox proportional hazard regression model was used for survival analysis in severe patients. RESULTS: We identified 269 (49.1%) of 548 patients as severe cases on admission. Older age, underlying hypertension, high cytokine levels (IL-2R, IL-6, IL-10, and TNF-α), and high lactate dehydrogenase level were significantly associated with severe COVID-19 on admission. The prevalence of asthma in patients with COVID-19 was 0.9%, markedly lower than that in the adult population of Wuhan. The estimated mortality was 1.1% in nonsevere patients and 32.5% in severe cases during the average 32 days of follow-up period. Survival analysis revealed that male sex, older age, leukocytosis, high lactate dehydrogenase level, cardiac injury, hyperglycemia, and high-dose corticosteroid use were associated with death in patients with severe COVID-19. CONCLUSIONS: Patients with older age, hypertension, and high lactate dehydrogenase level need careful observation and early intervention to prevent the potential development of severe COVID-19. Severe male patients with heart injury, hyperglycemia, and high-dose corticosteroid use may have a high risk of death.