Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models (preprint)

Online hate is an escalating problem that negatively impacts the lives of Internet users, and is also subject to rapid changes due to evolving events, resulting in new waves of online hate that pose a critical threat. Detecting and mitigating these new waves present two key challenges: it demands reasoning-based complex decision-making to determine the presence of hateful content, and the limited availability of training samples hinders updating the detection model. To address this critical issue, we present a novel framework called HATEGUARD for effectively moderating new waves of online hate. HATEGUARD employs a reasoning-based approach that leverages the recently introduced chain-of-thought (CoT) prompting technique, harnessing the capabilities of large language models (LLMs). HATEGUARD further achieves prompt-based zero-shot detection by automatically generating and updating detection prompts with new derogatory terms and targets in new wave samples to effectively address new waves of online hate. To demonstrate the effectiveness of our approach, we compile a new dataset consisting of tweets related to three recently witnessed new waves: the 2022 Russian invasion of Ukraine, the 2021 insurrection of the US Capitol, and the COVID-19 pandemic. Our studies reveal crucial longitudinal patterns in these new waves concerning the evolution of events and the pressing need for techniques to rapidly update existing moderation tools to counteract them. Comparative evaluations against state-of-the-art tools illustrate the superiority of our framework, showcasing a substantial 22.22% to 83.33% improvement in detecting the three new waves of online hate. Our work highlights the severe threat posed by the emergence of new waves of online hate and represents a paradigm shift in addressing this threat practically.

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.

Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a Hierarchical Reinforcement Learning decision framework for multi-mode Epidemic Control with multiple interventions called HRL4EC. We devise an epidemiological model, referred to as MID-SEIR, to describe multiple interventions' impact on transmission explicitly, and use it as the environment for HRL4EC. Besides, to address the complexity introduced by multiple interventions, this work transforms the multi-mode intervention decision problem into a multi-level control problem, and employs hierarchical reinforcement learning to find the optimal strategies. Finally, extensive experiments are conducted with real and simulated epidemic data to validate the effectiveness of our proposed method. We further analyze the experiment data in-depth, conclude a series of findings on epidemic intervention strategies, and make a visualization accordingly, which can provide heuristic support for policymakers' pandemic response.

Do Residents Living in Transit-Oriented Development Station Catchment Areas Travel More Sustainably? The Impacts of Life Events

Transit-oriented development (TOD) is an urban designed model aimed at attracting more sustainable travellers. However, not all TOD projects succeed in maintaining a high rate of sustainable travel behaviour. To examine the impacts of TOD on residents' travel behaviour, this paper applies binary logistic regression to analyse survey data for 1,298 residents living in the TOD areas in Hangzhou collected in 2020. The results show that socioeconomic characteristics, built environment factors, and travel attitudes play important roles in influencing their travel mode choices. Furthermore, the number of children in households and higher levels of car ownership significantly influence residents' sustainable travel behaviours. However, it appears that only a limited number of factors can convince car users to shift to sustainable modes of travel, such as their workplace being accessible by metro and attitudes towards changes in accessibility. This research study contributes to the existing literature in terms of enhancing the understanding of travel mode choice behaviours, particularly with regard to people who live near public transport infrastructure, as well as formulating evidence-based TOD policies to achieve more sustainable transport systems.

Safety and Effectiveness of SA58 Nasal Spray against SARS-CoV-2 family transmission: an exploratory single-arm trial (preprint)

Background: This study has assessed the protective effect of a new Anti-COVID-19 SA58 Nasal Spray (SA58 Nasal Spray) against SARS-CoV-2 infection under continuous exposure. Methods: This is an exploratory open-label, single-arm trial. To evaluate the safety and effectiveness of SA58 against SARS-CoV-2 family transmission, SA58 was administered to all enrolled family contacts at 3~6-hour intervals. The frequency of administration and adverse events (AEs) were self-reported by online questionnaire, and RT-PCR tests were used to diagnose SARS-CoV-2 infection. The effectiveness was assessed in comparison to a contemporaneous control group whose information was collected through three follow-up visits. Total effectiveness and single-day effectiveness were calculated. Results: The incidence of SARS-CoV-2 infection was 62.9% (44/70) in the experimental group and 94.8% (343/362) in the control group. Using SA58 nasal spray at least three times per day could possibly reduce the risk of household transmission of SARS-CoV-2 by 46.7%~56.5%. The incidence of AEs was 41.4% and the severity of all AEs was mild. Conclusion: Even under the scenario of continuous exposure to SARS-CoV-2, SA58 nasal spray remained effective in blocking viral transmission and was well tolerated.

The Sustainable Response Strategy to COVID-19: Pandemic Urban Zoning Based on Multimodal Transport Data (preprint)

Since the outbreak of COVID-19, it has rapidly evolved into a sudden and major public health emergency globally. With the variants of COVID-19, the difficulty of pandemic control continues to increase, which has brought significant costs to the society. The existing pandemic control zoning method ignores the impact on residents'lives. In this study, we propose a refined and low-cost pandemic control method by scientifically delineating zoning areas. First, a spatial interaction network is built up based on the multimodal transport travel data in Nanjing, China, and an improved Leiden community detection method based on the gravity model is used to obtain a preliminary zoning scheme. Then, we use spatial constraints to correct the results with the discrete spatial distribution. Finally, reasonable zones for pandemic control are obtained. The modularity of the algorithm results is 0.4185, proving that the proposed method is suitable for pandemic control zoning. The proposed method is also demonstrated to be able to minimize traffic flows between pandemic control areas and only 24.8% of travel connections are cut off, thus reducing the impact of pandemic control on residents'daily life and reducing the cost of pandemic control. The findings can help to inform sustainable strategies and suggestions for the pandemic control.

Does COVID-19 Vaccination Cause Storage Lower Urinary Tract Symptoms?

We investigated the storage lower urinary tract symptoms (LUTS) before and after the first dose of coronavirus disease 2019 (COVID-19) vaccine and the association between pre-vaccinated overactive bladder (OAB) and the worsening of storage LUTS following COVID-19 vaccination. This cross-sectional study in a third-level hospital in Taiwan used the validated pre- and post-vaccinated Overactive Bladder Symptom Score (OABSS). Diagnosis of OAB was made using pre-vaccinated OABSS. The deterioration of storage LUTS was assessed as the increased score of OABSS following vaccination. Of 889 subjects, up to 13.4% experienced worsened storage LUTS after vaccination. OAB was significantly associated with an increased risk of worsening urinary urgency (p = 0.030), frequency (p = 0.027), and seeking medical assistance due to urinary adverse events (p < 0.001) after vaccination. The OAB group faced significantly greater changes in OABSS-urgency (p = 0.003), OABSS-frequency (p = 0.025), and total OABSS (p = 0.014) after vaccination compared to those observed in the non-OAB group. Multivariate regression revealed that pre-vaccinated OAB (p = 0.003) was a risk for the deterioration of storage LUTS. In conclusion, storage LUTS may deteriorate after vaccination. OAB was significantly associated with higher risk and greater changes in worsening storage LUTS. Storage LUTS should be closely monitored after COVID-19 vaccination, especially in those OAB patients.

Neighborhood-based Inference and Restricted Boltzmann Machine for microbe and drug associations prediction (preprint)

Efficient identification of microbe-drug associations is critical for drug development and solving problem of antimicrobial resistance. Traditional wet-lab method requires a lot of money and labor in identifying potential microbe-drug associations. With development of machine learning and large amounts of biological data, computational methods become feasible. In this paper, we proposed a computational model of Neighborhood-based Inference (NI) and Restricted Boltzmann Machine (RBM) to predict potential Microbe-Drug Association (NIRBMMDA) by using multisource data. First, NI was used to predict potential microbe-drug associations by using different thresholds to find similar neighbors for drug or microbe. Then, RBM was also employed to predict potential microbe-drug associations based on contrastive divergence algorithm and sigmoid function. Because generalization ability of individual method is poor, we used an ensemble learning to integrate the two predicted microbe-drug associations. Especially, NI can fully utilize similar (neighbor) information of drug or microbe and RBM can learn potential probability distribution hid in known microbe-drug associations. Finally, ensemble learning was used to integrate individual predictor for obtaining a stronger predictor. To evaluate the performance of NIRBMMDA, global leave-one-out cross validation (LOOCV), local LOOCV and five-fold cross validations were implemented to evaluate the performance of NIRBMMDA based on three datasets of DrugVirus, MDAD and aBiofilm. In global LOOCV, NIRBMMDA gained the area under the receiver operating characteristics curve (AUC) of 0.8666, 0.9413 and 0.9557 for datasets of DrugVirus, MDAD and aBiofilm, respectively. In local LOOCV, AUCs of 0.8512, 0.9204 and 0.9414 were obtained for NIRBMMDA based on datasets of DrugVirus, MDAD and aBiofilm, respectively. For five-fold cross validation, NIRBMMDA acquired AUC and standard deviation of 0.8569 0.0027, 0.9248 0.0014 and 0.9369 0.0020 on the basis of datasets of DrugVirus, MDAD and aBiofilm, respectively. Moreover, case study for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) showed that 13 out of the top 20 predicted drugs were verified by searching literature. The other two case studies indicated that 17 and 15 out of the top 20 predicted microbes for the drug of ciprofloxacin and minocycline were confirmed by published literature, respectively. The results demonstrated NIRBMMDA is an effective model in predicting microbe-drug associations.

A tale of two tails among carbon prices, green and non-green cryptocurrencies

This paper studies the tail dependence among carbon prices, green and non-green cryptocurrencies. Using daily closing prices of carbon, green and non-green cryptocurrencies from 2017 to 2021 and a quantile connectedness framework, we find evidence of asymmetric tail dependence among these markets, with stronger dependence during highly volatile periods. Moreover, carbon prices are largely disconnected from cryptocurrencies during periods of low volatilities, while Bitcoin and Ethereum exhibit time-varying spillovers to other markets. Our results also show that green cryptocurrencies are weakly connected to Bitcoin and Ethereum, and their net connectedness are close to 0, except during the COVID-19 pandemic. Finally, we find a significant influence of macroeconomic and financial factors on the tail dependence among carbon, green and non-green cryptocurrency markets. Our results highlight the time-varying diversification benefits across carbon, green and non-green cryptocurrencies and have important implications for investors and policymakers.

Impact of COVID-19 on Pregnancy.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is an emerging disease. There has been a rapid increase in cases and deaths since it was identified in Wuhan, China, in early December 2019, with over 4,000,000 cases of COVID-19 including at least 250,000 deaths worldwide as of May 2020. However, limited data about the clinical characteristics of pregnant women with COVID-19 have been reported. Given the maternal physiologic and immune function changes during pregnancy, pregnant women may be at a higher risk of being infected with SARS-CoV-2 and developing more complicated clinical events. Information on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) may provide insights into the effects of COVID-19's during pregnancy. Even though SARS and MERS have been associated with miscarriage, intrauterine death, fetal growth restriction and high case fatality rates, the clinical course of COVID-19 pneumonia in pregnant women has been reported to be similar to that in non-pregnant women. In addition, pregnant women do not appear to be at a higher risk of catching COVID-19 or suffering from more severe disease than other adults of similar age. Moreover, there is currently no evidence that the virus can be transmitted to the fetus during pregnancy or during childbirth. Babies and young children are also known to only experience mild forms of COVID-19. The aims of this systematic review were to summarize the possible symptoms, treatments, and pregnancy outcomes of women infected with COVID-19 during pregnancy.

Viral Drug prediction with Matrix Decomposition and Two-layer Heterogeneous Graph Inference (preprint)

Recently, the association prediction between viruses and drugs has drawn more and more attention. A growing number of studies have shown that the problem of antiviral drug resistance is increasing and has become a major problem plaguing the medical community. Moreover, the development cycle of new drugs is long and requires a lot of funds. If new viruses emerge, effective antiviral drugs are urgently needed. Therefore, effective calculation methods are required to predict potential antiviral drugs. In this paper, we developed a computational model of Matrix Decomposition and Heterogeneous Graph based Inference for Drug-Virus Association (MDHGIVDA) to predict potential drug-virus associations. MDHGIVDA integrated virus sequence similarity, drug chemical structure similarity, drug side effect similarity, Gaussian interaction profile kernel similarity for drugs and viruses, new drug-virus associations matrix obtained by matrix decomposition to discover new drug-virus associations. Due to the use of matrix factorization and heterogeneous graphs, our model has a high prediction accuracy compared with the previous four models. In the global and local leave-one-out cross validation (LOOCV), MDHGIVDA obtained area under the receiver operating characteristics curve (AUC) of 0.8528 and AUC of 0.8532, respectively. In addition, in the five-fold cross validation, the AUC and the standard deviation is 0.8299 0.0037, which shows that MDHGIVDA has stability and high prediction accuracy. In the case studies of three important viruses, 18, 14, and 16 out of the top 20 predicted drugs for Zika virus (ZIKV), Severe Acute Respiratory Syndrome Coronavirus 2 ( SARS-COV-2 ), Human Immunodeficiency Virus-1 (HIV-1) were verified respectively by searching the literature on PubMed. These results showed that MDHGIVDA is effective in predicting potential drug-virus associations.

Past and future trends of civil airport emissions in China, from 2010 to 2030 (preprint)

This paper aims to study the trend of aircraft emission in China. The multiyear emission inventories of HC, CO, NO X , SO 2 and PM 2.5 in the period 2010–2030 were developed. Results show that the total amount of all targeted pollutants from China's civil airports climbed from approximately 82407 tons in 2010 to 164275 tons in 2019. It is expected that the total amount of pollutants will reach 400845 tons by 2030. Pollutant emissions had the lowest growth rate in 2019 and the highest growth rate in 2013 (4.1% and 13.3%, respectively). From 2013 to 2019, the rate of increase in airport pollutant emissions began to decline. In 2019, the emissions of HC, CO, NO X , SO 2 and PM 2.5 are 6251, 53614, 97059, 6248 and 1102 tons, respectively. COVID-19 had a significant impact on airport emissions. By comparing the statistical value and the predicted value of airport emissions in 2020, we found that COVID-19 reduced the emissions of ZHHH and national by 46.33% and 30.45% respectively. In 2019, the East has the highest contribution rate of 27.6%, and Xinjiang has the lowest contribution rate of 3.6%. The emissions of the seven aviation regions were in the order of east > central south > southwest > north > northeast > northwest > Xinjiang.

DNA damage contributes to age-associated differences in SARS-CoV-2 infection (preprint)

Coronavirus disease 2019 (COVID-19), caused by coronavirus SARS-CoV-2, is known to disproportionately affect older individuals1,2. How aging processes affect the disease progression remains largely unknown. Here we found that DNA damage, one of the major causes of aging3, promoted susceptibility to SARS-CoV-2 infection in cells and intestinal organoids. SARS-CoV-2 entry was facilitated by DNA damage caused by telomere attrition or extrinsic genotoxic stress and hampered by inhibition of DNA damage response (DDR). Mechanistic analysis revealed that DDR increased expression of ACE2, the receptor of SARS-CoV-2, by activation of transcription factor c-Jun in vitro and in vivo. Expression of ACE2 was elevated in the older tissues and positively correlated with γH2Ax and phosphorylated c-Jun (p-c-Jun). Finally, targeting DNA damage by increasing the DNA repair capacity, alleviated cell susceptibility to SARS-CoV-2. Our data provide insights into the age-associated differences in SARS-CoV-2 infection and a novel target for anti-viral intervention.

Development of multivalent nanobodies blocking SARS-CoV-2 infection by targeting RBD of spike protein.

BACKGROUND: The outbreak and pandemic of coronavirus SARS-CoV-2 caused significant threaten to global public health and economic consequences. It is extremely urgent that global people must take actions to develop safe and effective preventions and therapeutics. Nanobodies, which are derived from single­chain camelid antibodies, had shown antiviral properties in various challenge viruses. In this study, multivalent nanobodies with high affinity blocking SARS-CoV-2 spike interaction with ACE2 protein were developed. RESULTS: Totally, four specific nanobodies against spike protein and its RBD domain were screened from a naïve VHH library. Among them, Nb91-hFc and Nb3-hFc demonstrated antiviral activity by neutralizing spike pseudotyped viruses in vitro. Subsequently, multivalent nanobodies were constructed to improve the neutralizing capacity. As a result, heterodimer nanobody Nb91-Nb3-hFc exhibited the strongest RBD-binding affinity and neutralizing ability against SARS-CoV-2 pseudoviruses with an IC50 value at approximately 1.54 nM. CONCLUSIONS: The present study indicated that naïve VHH library could be used as a potential resource for rapid acquisition and exploitation of antiviral nanobodies. Heterodimer nanobody Nb91-Nb3-hFc may serve as a potential therapeutic agent for the treatment of COVID-19.

Should bike sharing continue operating during the COVID-19 pandemic? Empirical findings from Nanjing, China (preprint)

Coronavirus disease 2019 (COVID-19) has triggered a worldwide outbreak of pandemic, and transportation services have played a key role in coronavirus transmission. Although not crowded in a confined space like a bus or a metro car, bike sharing users will be exposed to the bike surface and take the transmission risk. During the COVID-19 pandemic, how to meet user demand and avoid virus spreading has become an important issue for bike sharing. Based on the trip data of bike sharing in Nanjing, China, this study analyzes the travel demand and operation management before and after the pandemic outbreak from the perspective of stations, users, and bikes. Semi-logarithmic difference-in-differences model, visualization methods, and statistic indexes are applied to explore the transportation service and risk prevention of bike sharing during the pandemic. The results show that pandemic control strategies sharply reduced user demand, and commuting trips decreased more significantly. Some stations around health and religious places become more important. Men and older adults are more dependent on bike sharing systems. Besides, the trip decrease reduces user contact and increases idle bikes. And a new concept of user distancing is proposed to avoid transmission risk and activate idle bikes. This study evaluates the role of shared micro-mobility during the COVID-19 pandemic, and also inspires the blocking of viral transmission within the city.

Dalbavancin binds ACE2 to block its interaction with SARS-CoV-2 spike protein and is effective in inhibiting SARS-CoV-2 infection in animal models.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic worldwide. Currently, however, no effective drug or vaccine is available to treat or prevent the resulting coronavirus disease 2019 (COVID-19). Here, we report our discovery of a promising anti-COVID-19 drug candidate, the lipoglycopeptide antibiotic dalbavancin, based on virtual screening of the FDA-approved peptide drug library combined with in vitro and in vivo functional antiviral assays. Our results showed that dalbavancin directly binds to human angiotensin-converting enzyme 2 (ACE2) with high affinity, thereby blocking its interaction with the SARS-CoV-2 spike protein. Furthermore, dalbavancin effectively prevents SARS-CoV-2 replication in Vero E6 cells with an EC50 of ~12 nM. In both mouse and rhesus macaque models, viral replication and histopathological injuries caused by SARS-CoV-2 infection are significantly inhibited by dalbavancin administration. Given its high safety and long plasma half-life (8-10 days) shown in previous clinical trials, our data indicate that dalbavancin is a promising anti-COVID-19 drug candidate.

Global analysis of daily new COVID-19 cases reveals many static-phase countries including the United States potentially with unstoppable epidemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is hitting many countries. It is hypothesized the epidemic is differentially progressing in different countries. AIM: To investigate how the COVID-19 epidemic is going on in different countries by analyzing representative countries. METHODS: The status of COVID-19 epidemic in over 60 most affected countries was characterized. The data of daily new cases of each country were collected from Worldometer. The data of daily tests for the United States, Italy, and South Korea were collected from the Website of One World Data. Levels of daily positive COVID-19 tests in the two most affected states of the United States (New York and New Jersey) were collected from the website of the COVID Tracking Project. Statistics were analyzed using Microcal Origin software with ANOVA algorithm, and significance level was set at a P value of 0.05. RESULTS: The COVID-19 epidemic was differentially progressing in different countries. Comparative analyses of daily new cases as of April 19, 2020 revealed that 61 most affected countries can be classified into four types: Downward (22), upward (20), static-phase (12), and uncertain ones (7). In particular, the 12 static-phase countries including the United States were characterized by largely constant numbers of daily new cases in the past over 14 d. Furthermore, these static-phase countries were overall significantly lower in testing density (P = 0.016) but higher in the level of positive COVID-19 tests than downward countries (P = 0.028). These findings suggested that the testing capacity in static-phase countries was lagging behind the spread of the outbreak, i.e., daily new cases (confirmed) were likely less than daily new infections and the remaining undocumented infections were thus still expanding, resulting in unstoppable epidemic. CONCLUSION: Increasing the testing capacity and/or reducing the COVID-19 transmission are urgently needed to stop the potentially unstoppable, severing crisis in static-phase countries.

Two mechanically ventilated cases of COVID-19 successfully managed with a sequential ventilation weaning protocol: Two case reports.

BACKGROUND: Patients with critical coronavirus disease 2019 (COVID-19), characterized by respiratory failure requiring mechanical ventilation (MV), are at high risk of mortality. An effective and practical MV weaning protocol is needed for these fragile cases. CASE SUMMARY: Here, we present two critical COVID-19 patients who presented with fever, cough and fatigue. COVID-19 diagnosis was confirmed based on blood cell counts, chest computed tomography (CT) imaging, and nuclei acid test results. To address the patients' respiratory failure, they first received noninvasive ventilation (NIV). When their condition did not improve after 2 h of NIV, each patient was advanced to MV [tidal volume (Vt), 6 mL/kg ideal body weight (IBW); 8-10 cmH2O of positive end-expiratory pressure; respiratory rate, 20 breaths/min; and 40%-80% FiO2] with prone positioning for 12 h/day for the first 5 d of MV. Extensive infection control measures were conducted to minimize morbidity, and pharmacotherapy consisting of an antiviral, immune-enhancer, and thrombosis prophylactic was administered in both cases. Upon resolution of lung changes evidenced by CT, the patients were sequentially weaned using a weaning screening test, spontaneous breathing test, and airbag leak test. After withdrawal of MV, the patients were transitioned through NIV and high-flow nasal cannula oxygen support. Both patients recovered well. CONCLUSION: A MV protocol attentive to intubation/extubation timing, prone positioning early in MV, infection control, and sequential withdrawal of respiratory support, may be an effective regimen for patients with critical COVID-19.

Imaging features of the initial chest thin-section CT scans from 110 patients after admission with suspected or confirmed diagnosis of COVID-19.

BACKGROUND: In December 2019, an outbreak of a novel coronavirus pneumonia, now called COVID-19, occurred in Wuhan, Hubei Province, China. COVID-19, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread quickly across China and the rest of the world. This study aims to evaluate initial chest thin-section CT findings of COVID-19 patients after their admission at our hospital. METHODS: Retrospective study in a tertiary referral hospital in Anhui, China. From January 22, 2020 to February 16, 2020, 110 suspected or confirmed COVID-19 patients were examined using chest thin-section CT. Patients in group 1 (n = 51) presented with symptoms of COVID-19 according to the diagnostic criteria. Group 2 (n = 29) patients were identified as a high degree of clinical suspicion. Patients in group 3 (n = 30) presented with mild symptoms and normal chest radiographs. The characteristics, positions, and distribution of intrapulmonary lesions were analyzed. Moreover, interstitial lesions, pleural thickening and effusion, lymph node enlargement, and other CT abnormalities were reviewed. RESULTS: CT abnormalities were found only in groups 1 and 2. The segments involved were mainly distributed in the lower lobes (58.3%) and the peripheral zone (73.8%). The peripheral lesions, adjacent subpleural lesions, accounted for 51.8%. Commonly observed CT patterns were ground-glass opacification (GGO) (with or without consolidation), interlobular septal thickening, and intralobular interstitial thickening. Compared with group 1, patients in group 2 presented with smaller lesions, and all lesions were distributed in fewer lung segments. Localized pleural thickening was observed in 51.0% of group 1 patients and 48.2% of group 2 patients. The prevalence of lymph node enlargement in groups 1 and 2 combined was extremely low (1 of 80 patients), and no significant pleural effusion or pneumothorax was observed (0 of 80 patients). CONCLUSION: The common features of chest thin-section CT of COVID-19 are multiple areas of GGO, sometimes accompanied by consolidation. The lesions are mainly distributed in the lower lobes and peripheral zone, and a large proportion of peripheral lesions are accompanied by localized pleural thickening adjacent to the subpleural region.