Chest CT Patterns from Diagnosis to 1 Year of Follow-up in Patients with COVID-19.

Background The chest CT manifestations of COVID-19 from hospitalization to convalescence after 1 year are unknown. Purpose To assess chest CT manifestations of COVID-19 up to 1 year after symptom onset. Materials and Methods Patients were enrolled if they were admitted to the hospital because of COVID-19 and underwent CT during hospitalization at two isolation centers between January 27, 2020, and March 31, 2020. In a prospective study, three serial chest CT scans were obtained at approximately 3, 7, and 12 months after symptom onset and were longitudinally analyzed. The total CT score of pulmonary lobe involvement, ranging from 0 to 25, was assessed (score of 1-5 for each lobe). Univariable and multivariable logistic regression analyses were performed to explore independent risk factors for residual CT abnormalities after 1 year. Results A total of 209 study participants (mean age, 49 years ± 13 [standard deviation]; 116 women) were evaluated. CT abnormalities had resolved in 61% of participants (128 of 209) at 3 months and in 75% of participants (156 of 209) at 12 months. Among participants with chest CT abnormalities that had not resolved, there were residual linear opacities in 25 of the 209 participants (12%) and multifocal reticular or cystic lesions in 28 of the 209 participants (13%). Age 50 years or older, lymphopenia, and severe or aggravation of acute respiratory distress syndrome were independent risk factors for residual CT abnormalities at 1 year (odds ratios = 15.9, 18.9, and 43.9, respectively; P < .001 for each comparison). In 53 participants with residual CT abnormalities at 12 months, reticular lesions (41 of 53 participants [77%]) and bronchial dilation (39 of 53 participants [74%]) were observed at discharge and were persistent in 28 (53%) and 24 (45%) of the 53 participants, respectively. Conclusion One year after COVID-19 diagnosis, chest CT scans showed abnormal findings in 53 of the 209 study participants (25%), with 28 of the 209 participants (13%) showing subpleural reticular or cystic lesions. Older participants with severe COVID-19 or acute respiratory distress syndrome were more likely to develop lung sequelae that persisted at 1 year. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lee and Wi et al in this issue.

Target-Based Virtual Screening and LC/MS-Guided Isolation Procedure for Identifying Phloroglucinol-Terpenoid Inhibitors of SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to more than 5 million deaths worldwide to date. Due to the limited therapeutic options so far available, target-based virtual screening with LC/MS support was applied to identify the novel and high-content compounds 1-4 with inhibitory effects on SARS-CoV-2 in Vero E6 cells from the plant Dryopteris wallichiana. These compounds were also evaluated against SARS-CoV-2 in Calu-3 cells and showed unambiguous inhibitory activity. The inhibition assay of targets showed that compounds 3 and 4 mainly inhibited SARS-CoV-2 3CLpro, with effective Kd values. Through docking and molecular dynamics modeling, the binding site is described, providing a comprehensive understanding of 3CLpro and interactions for 3, including hydrogen bonds, hydrophobic bonds, and the spatial occupation of the B ring. Compounds 3 and 4 represent new, potential lead compounds for the development of anti-SARS-CoV-2 drugs. This study has led to the development of a target-based virtual screening method for exploring the potency of natural products and for identifying natural bioactive compounds for possible COVID-19 treatment.

The association of obesity with the progression and outcome of COVID-19: The insight from an artificial-intelligence-based imaging quantitative analysis on computed tomography.

AIMS: To explore the association of obesity with the progression and outcome of coronavirus disease 2019 (COVID-19) at the acute period and 5-month follow-up from the perspectives of computed tomography (CT) imaging with artificial intelligence (AI)-based quantitative evaluation, which may help to predict the risk of obese COVID-19 patients progressing to severe and critical disease. MATERIALS AND METHODS: This retrospective cohort enrolled 213 hospitalized COVID-19 patients. Patients were classified into three groups according to their body mass index (BMI): normal weight (from 18.5 to <24 kg/m2 ), overweight (from 24 to <28 kg/m2 ) and obesity (≥28 kg/m2 ). RESULTS: Compared with normal-weight patients, patients with higher BMI were associated with more lung involvements in lung CT examination (lung lesions volume [cm3 ], normal weight vs. overweight vs. obesity; 175.5[34.0-414.9] vs. 261.7[73.3-576.2] vs. 395.8[101.6-1135.6]; p = 0.002), and were more inclined to deterioration at the acute period. At the 5-month follow-up, the lung residual lesion was more serious (residual total lung lesions volume [cm3 ], normal weight vs. overweight vs. obesity; 4.8[0.0-27.4] vs. 10.7[0.0-55.5] vs. 30.1[9.5-91.1]; p = 0.015), and the absorption rates were lower for higher BMI patients (absorption rates of total lung lesions volume [%], normal weight vs. overweight vs. obesity; 99.6[94.0-100.0] vs. 98.9[85.2-100.0] vs. 88.5[66.5-95.2]; p = 0.013). The clinical-plus-AI parameter model was superior to the clinical-only parameter model in the prediction of disease deterioration (areas under the ROC curve, 0.884 vs. 0.794, p < 0.05). CONCLUSIONS: Obesity was associated with severe pneumonia lesions on CT and adverse clinical outcomes. The AI-based model with combinational use of clinical and CT parameters had incremental prognostic value over the clinical parameters alone.

Novel nucleocapsid protein-targeting phenanthridine inhibitors of SARS-CoV-2.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented in human history. As a major structural protein, nucleocapsid protein (NPro) is critical to the replication of SARS-CoV-2. In this work, 17 NPro-targeting phenanthridine derivatives were rationally designed and synthesized, based on the crystal structure of NPro. Most of these compounds can interact with SARS-CoV-2 NPro tightly and inhibit the replication of SARS-CoV-2 in vitro. Compounds 12 and 16 exhibited the most potent anti-viral activities with 50% effective concentration values of 3.69 and 2.18 µM, respectively. Furthermore, site-directed mutagenesis of NPro and Surface Plasmon Resonance (SPR) assays revealed that 12 and 16 target N-terminal domain (NTD) of NPro by binding to Tyr109. This work found two potent anti-SARS-CoV-2 bioactive compounds and also indicated that SARS-CoV-2 NPro-NTD can be a target for new anti-virus agents.

Long-Term Effects of COVID-19 on Health Care Workers 1-Year Post-Discharge in Wuhan.

INTRODUCTION: To assess the long-term consequences of coronavirus disease (COVID-19) among health care workers (HCWs) in China (hereafter surviving HCWs). METHODS: A total of 303 surviving HCWs were included. Lung (pulmonary function test, 6-min walk test [6MWT], chest CT), physical (St. George's Respiratory Questionnaire [SGRQ], Modified Medical Research Council dyspnea scale [mMRC], and Borg scale), and psychiatric functions (Essen Trauma Inventory) were evaluated during the 1-year follow-up. RESULTS: Surviving HCWs had an abnormal diffusion capacity 1 year post-discharge. Participants with a reduced carbon monoxide diffusing capacity (DLCO) comprised 43.48%. The proportion of HCWs with a median 6MWT distance below the lower limit of the normal was 19.4%. An abnormal CT pattern was observed in 37.5% of the HCWs. The SGRQ, mMRC, and Borg scores of surviving HCWs, especially those with critical/severe disease, were significantly higher than those in the normal population. Probable post-traumatic stress disorder (PTSD) was reported in 21.9% of the surviving HCWs. Diffusion capacity impairment was associated with women. Critical/severe illness and nurses were associated with impaired physical function. CONCLUSIONS: Most surviving HCWs, especially female HCWs, still had an abnormal diffusion capacity at 1 year. The physical and psychiatric functions of surviving HCWs were significantly worse than those of the healthy population. Long-term follow-up of pulmonary, physical, and psychiatric functions for surviving HCWs is required.

Plasma Metabolomic Profiles in Recovered COVID-19 Patients without Previous Underlying Diseases 3 Months After Discharge.

BACKGROUND: It remains unclear whether discharged COVID-19 patients have fully recovered from severe complications, including the differences in the post-infection metabolomic profiles of patients with different disease severities. METHODS: COVID-19-recovered patients, who had no previous underlying diseases and were discharged from Wuhan Union Hospital for 3 months, and matched healthy controls (HCs) were recruited in this prospective cohort study. We examined the blood biochemical indicators, cytokines, lung computed tomography scans, including 39 HCs, 18 recovered asymptomatic (RAs), 34 recovered moderate (RMs), and 44 recovered severe/ critical patients (RCs). A liquid chromatography-mass spectrometry-based metabolomics approach was employed to profile the global metabolites of fasting plasma of these participants. RESULTS: Clinical data and metabolomic profiles suggested that RAs recovered well, but some clinical indicators and plasma metabolites in RMs and RCs were still abnormal as compared with HCs, such as decreased taurine, succinic acid, hippuric acid, some indoles, and lipid species. The disturbed metabolic pathway mainly involved the tricarboxylic cycle, purine, and glycerophospholipid metabolism. Moreover, metabolite alterations differ between RMs and RCs when compared with HCs. Correlation analysis revealed that many differential metabolites were closely associated with inflammation and the renal, pulmonary, heart, hepatic, and coagulation system functions. CONCLUSION: We uncovered metabolite clusters pathologically relevant to the recovery state in discharged COVID-19 patients which may provide new insights into the pathogenesis of potential organ damage in recovered patients.

Characteristics of mental health implications and plasma metabolomics in patients recently recovered from COVID-19.

This study aimed to explore the associations between cerebral white matter (WM) alterations, mental health status, and metabolism in recovered COVID-19 patients. We included 28 recovered COVID-19 patients and 27 healthy controls between April 2020 and June 2020. Demographic data, the mental health scores, diffusion-tensor imaging (DTI) data, and plasma metabolomics were collected and compared between the two groups. Tract-based spatial statistics and graph theory approaches were used for DTI data analysis. Untargeted metabolomics analysis of the plasma was performed. Correlation analyses were performed between these characteristics. Recovered COVID-19 patients showed decreased fractional anisotropy, increased mean diffusivity and radial diffusivity values in widespread brain regions, and significantly lower global efficiency, longer shortest path length, and less nodal local efficiency in superior occipital gyrus (all, P < 0.05, Bonferroni corrected). Our results also demonstrated significantly different plasma metabolic profiling in recovered COVID-19 patients even at 3 months after their hospital discharge, which was mainly related to purine pathways, amino acids, lipids, and amine metabolism. Certain regions with cerebral WM alterations in the recovered patients showed significant correlations with different metabolites and the mental health scores. We observed multiple alterations in both WM integrity and plasma metabolomics that may explain the deteriorated mental health of recovered COVID-19 patients. These findings may provide potential biomarkers for the mental health evaluation for the recovered COVID-19 patients and potential targets for novel therapeutics.

Protective Efficacy of Inactivated Vaccine against SARS-CoV-2 Infection in Mice and Non-Human Primates.

The ongoing coronavirus disease 2019 (COVID-19) pandemic caused more than 96 million infections and over 2 million deaths worldwide so far. However, there is no approved vaccine available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the disease causative agent. Vaccine is the most effective approach to eradicate a pathogen. The tests of safety and efficacy in animals are pivotal for developing a vaccine and before the vaccine is applied to human populations. Here we evaluated the safety, immunogenicity, and efficacy of an inactivated vaccine based on the whole viral particles in human ACE2 transgenic mouse and in non-human primates. Our data showed that the inactivated vaccine successfully induced SARS-CoV-2-specific neutralizing antibodies in mice and non-human primates, and subsequently provided partial (in low dose) or full (in high dose) protection of challenge in the tested animals. In addition, passive serum transferred from vaccine-immunized mice could also provide full protection from SARS-CoV-2 infection in mice. These results warranted positive outcomes in future clinical trials in humans.

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.

Glycemic status affects the severity of coronavirus disease 2019 in patients with diabetes mellitus: an observational study of CT radiological manifestations using an artificial intelligence algorithm.

AIMS: Increasing evidence suggests that poor glycemic control in diabetic individuals is associated with poor coronavirus disease 2019 (COVID-19) pneumonia outcomes and influences chest computed tomography (CT) manifestations. This study aimed to explore the impact of diabetes mellitus (DM) and glycemic control on chest CT manifestations, acquired using an artificial intelligence (AI)-based quantitative evaluation system, and COVID-19 disease severity and to investigate the association between CT lesions and clinical outcome. METHODS: A total of 126 patients with COVID-19 were enrolled in this retrospective study. According to their clinical history of DM and glycosylated hemoglobin (HbA1c) level, the patients were divided into 3 groups: the non-DM group (Group 1); the well-controlled blood glucose (BG) group, with HbA1c < 7% (Group 2); and the poorly controlled BG group, with HbA1c ≥ 7% (Group 3). The chest CT images were analyzed with an AI-based quantitative evaluation system. Three main quantitative CT features representing the percentage of total lung lesion volume (PLV), percentage of ground-glass opacity volume (PGV) and percentage of consolidation volume (PCV) in bilateral lung fields were used to evaluate the severity of pneumonia lesions. RESULTS: Patients in Group 3 had the highest percentage of severe or critical illness, with 12 (32%) cases, followed by 6 (11%) and 7 (23%) cases in Groups 1 and 2, respectively (p = 0.042). The composite endpoints, including death or using mechanical ventilation or admission to the intensive care unit (ICU), were 3 (5%), 5 (16%) and 10 (26%) in Groups 1, 2 and 3, respectively (p = 0.013). The PLV, PGV and PCV in bilateral lung fields were significantly different among the three groups (all p < 0.001): the median PLVs were 12.5% (Group 3), 3.8% (Group 2) and 2.4% (Group 1); the median PGVs were 10.2% (Group 3), 3.6% (Group 2) and 1.9% (Group 1); and the median PCVs were 1.8% (Group 3), 0.3% (Group 2) and 0.1% (Group 1). In the linear regression analyses, which were adjusted for age, sex, BMI, and comorbidities, HbA1c remained positively associated with PLV (ß = 0.401, p < 0.001), PGV (ß = 0.364, p = 0.001) and PCV (ß = 0.472, p < 0.001); this relationship was also observed between fasting blood glucose (FBG) and the three CT quantitative parameters. In the logistic regression analyses, PLV [OR 1.067 (1.032, 1.103)], PGV [OR 1.076 (1.034, 1.120)] and PCV [OR 1.280 (1.110, 1.476)] levels were independent predictors of the composite endpoints, as well as the areas under the ROC (AUCs) for PLV [AUC 0.796 (0.691, 0.900)], PGV [AUC 0.783 (0.678, 0.889)] and PCV [AUC 0.816 (0.722, 0.911)]; the ORs were still significant for CT lesions after adjusting for age, sex and poorly controlled diabetes. CONCLUSIONS: Increased blood glucose level was correlated with the severity of lung involvement, as evidenced by certain chest CT parameters, and clinical prognosis in diabetic COVID-19 patients. There was a positive correlation between blood glucose level (both HbA1c and FBG) on admission and lung lesions. Moreover, the CT lesion severity by AI quantitative analysis was correlated with clinical outcomes.

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.

Temporal changes of CT findings between non-severe and severe cases of COVID-19 pneumonia: a multi-center, retrospective, longitudinal Study.

Background and aim: To perform a longitudinal analysis of serial CT findings over time in patients with COVID-19 pneumonia. Methods: From February 5 to March 8, 2020, 73 patients (male to female, ratio of 43:30; mean age, 51 years) with COVID-19 pneumonia were retrospectively enrolled and followed up until discharge from three institutions in China. The patients were divided into the severe and non-severe groups according to treatment option. The patterns and distribution of lung abnormalities, total CT scores, single ground-glass opacity (GGO) CT scores, single consolidation CT scores, single reticular CT scores and the amounts of zones involved were reviewed by 2 radiologists. These features were analyzed for temporal changes. Results: In non-severe group, total CT scores (median, 9.5) and the amounts of zones involved were slowly increased and peaked in disease week 2. In the severe group, the increase was faster, with scores also peaking at 2 weeks (median, 20). In both groups, the later parameters began to decrease in week 4 (median values of 9 and 19 in the non-severe and severe groups, respectively). In the severe group, the dominant residual lung lesions were reticular (median single reticular CT score, 10) and consolidation (median single consolidation CT score, 7). In the non-severe group, the dominant residual lung lesions were GGO (median single GGO CT score, 7) and reticular (median single reticular CT score, 4). In both non-severe and severe groups, the GGO pattern was dominant in week 1, with a higher proportion in the severe group compared with the non-severe group (72% vs. 65%). The consolidation pattern peaked in week 2, with 9 (32%) and 19 (73%) in the non-severe and severe groups, respectively; the reticular pattern became dominant from week 4 (both group >40%). Conclusion: The extent of CT abnormalities in the severe and non-severe groups peaked in disease week 2. The temporal changes of CT manifestations followed a specific pattern, which might indicate disease progression and recovery.

Spontaneous pneumothorax and subpleural bullae in a patient with COVID-19: a 92-day observation.

This report describes a patient with COVID-19 who developed spontaneous pneumothorax and subpleural bullae during the course of the infection. Consecutive chest computed tomography images indicated that COVID-19-associated pneumonia had damaged the subpleural alveoli and distal bronchus. Coughing might have induced a sudden increase in intra-alveolar pressure, leading to the rupture of the subpleural alveoli and distal bronchus and resulting in spontaneous pneumothorax and subpleural bullae. At the 92-day follow-up, the pneumothorax and subpleural bullae had completely resolved, which indicated that these complications had self-limiting features.

Potential drugs for the treatment of the novel coronavirus pneumonia (COVID-19) in China.

The fight against the novel coronavirus pneumonia (namely COVID-19) that seriously harms human health is a common task for all mankind. Currently, development of drugs against the novel coronavirus (namely SARS-CoV-2) is quite urgent. Chinese medical workers and scientific researchers have found some drugs to play potential therapeutic effects on COVID-19 at the cellular level or in preliminary clinical trials. However, more fundamental studies and large sample clinical trials need to be done to ensure the efficacy and safety of these drugs. The adoption of these drugs without further testing must be careful. The relevant articles, news, and government reports published on the official and Preprint websites, PubMed and China National Knowledge Infrastructure (CNKI) databases from December 2019 to April 2020 were searched and manually filtered. The general pharmacological characteristics, indications, adverse reactions, general usage, and especially current status of the treatment of COVID-19 of those potentially effective drugs, including chemical drugs, traditional Chinese medicines (TCMs), and biological products in China were summarized in this review to guide reasonable medication and the development of specific drugs for the treatment of COVID-19.

Fangcang Shelter Hospital in Wuhan: A radiographic report on a cohort of 98 COVID-19 patients.

Objectives: To present the temporal changes of CT manifestations in COVID-19 patients from a single fangcang shelter hospital and to facilitate the understanding of the disease course. Materials and Methods: This retrospective study included 98 patients (males: females, 43:55, mean year, 49±12 years) with confirmed COVID-19 at Jianghan fangcang shelter hospital admitted between Feb 05, 2020, and Feb 09, 2020, who had initial chest CTs at our hospital. Radiographic features and CT scores were analyzed. Results: A total of 267 CT scans of 98 patients were evaluated. Our study showed a high median total CT score of 7 within the first week from symptom onset, peaked in the 2nd week at 10, followed by persistently high levels of CT score with 9.5, 7 and 7 for the week 3, 4, and >4, respectively, and a prolonged median disease course (30 days, the median interval between the onset of initial symptoms and discharge). Ground-glass opacity (GGO) (58%, 41/71) was the earliest and most frequent finding in week 1. Consolidation (26%, 14/53) and mixed pattern (40%, 21/53) were predominant patterns in 2nd week. GGO and reticular were the main patterns of later phase CT scans in patients with relatively advanced diseases who had longer illness duration (≥4 weeks). Among the 94 CT abnormalities obtained within 3 days from the twice RT-PCR test turned negative, the mixed pattern was mainly presented in patients with disease duration of 2-3 weeks, for GGO and reticular were common during the whole course. Conclusion: Discharged patients from fangcang shelter hospital demonstrated a high extent of lung abnormalities on CT within the first week from symptom onset, peaked at 2nd week, followed by persistence of high levels and a prolonged median disease course. GGO was the predominant pattern in week 1, consolidation and mixed pattern in 2nd week, whereas GGO and reticular patterns in later stages (≥4 weeks).

Different computed tomography patterns of Coronavirus Disease 2019 (COVID-19) between survivors and non-survivors.

This study aimed to compare the chest computed tomography (CT) findings between survivors and non-survivors with Coronavirus Disease 2019 (COVID-19). Between 12 January 2020 and 20 February 2020, the records of 124 consecutive patients diagnosed with COVID-19 were retrospectively reviewed and divided into survivor (83/124) and non-survivor (41/124) groups. Chest CT findings were qualitatively compared on admission and serial chest CT scans were semi-quantitively evaluated between two groups using curve estimations. On admission, significantly more bilateral (97.6% vs. 73.5%, p = 0.001) and diffuse lesions (39.0% vs. 8.4%, p < 0.001) with higher total CT score (median 10 vs. 4, p < 0.001) were observed in non-survivor group compared with survivor group. Besides, crazy-paving pattern was more predominant in non-survivor group than survivor group (39.0% vs. 12.0%, p < 0.001). From the prediction of curve estimation, in survivor group total CT score increased in the first 20 days reaching a peak of 6 points and then gradually decreased for more than other 40 days (R2 = 0.545, p < 0.001). In non-survivor group, total CT score rapidly increased over 10 points in the first 10 days and gradually increased afterwards until ARDS occurred with following death events (R2 = 0.711, p < 0.001). In conclusion, persistent progression with predominant crazy-paving pattern was the major manifestation of COVID-19 in non-survivors. Understanding this CT feature could help the clinical physician to predict the prognosis of the patients.

Infection with novel coronavirus (SARS-CoV-2) causes pneumonia in Rhesus macaques.

The 2019 novel coronavirus (SARS-CoV-2) outbreak is a major challenge for public health. SARS-CoV-2 infection in human has a broad clinical spectrum ranging from mild to severe cases, with a mortality rate of ~6.4% worldwide (based on World Health Organization daily situation report). However, the dynamics of viral infection, replication and shedding are poorly understood. Here, we show that Rhesus macaques are susceptible to the infection by SARS-CoV-2. After intratracheal inoculation, the first peak of viral RNA was observed in oropharyngeal swabs one day post infection (1 d.p.i.), mainly from the input of the inoculation, while the second peak occurred at 5 d.p.i., which reflected on-site replication in the respiratory tract. Histopathological observation shows that SARS-CoV-2 infection can cause interstitial pneumonia in animals, characterized by hyperemia and edema, and infiltration of monocytes and lymphocytes in alveoli. We also identified SARS-CoV-2 RNA in respiratory tract tissues, including trachea, bronchus and lung; and viruses were also re-isolated from oropharyngeal swabs, bronchus and lung, respectively. Furthermore, we demonstrated that neutralizing antibodies generated from the primary infection could protect the Rhesus macaques from a second-round challenge by SARS-CoV-2. The non-human primate model that we established here provides a valuable platform to study SARS-CoV-2 pathogenesis and to evaluate candidate vaccines and therapeutics.

Factors associated with death outcome in patients with severe coronavirus disease-19 (COVID-19): a case-control study.

Rationale: Up to date, the exploration of clinical features in severe COVID-19 patients were mostly from the same center in Wuhan, China. The clinical data in other centers is limited. This study aims to explore the feasible parameters which could be used in clinical practice to predict the prognosis in hospitalized patients with severe coronavirus disease-19 (COVID-19). Methods: In this case-control study, patients with severe COVID-19 in this newly established isolation center on admission between 27 January 2020 to 19 March 2020 were divided to discharge group and death event group. Clinical information was collected and analyzed for the following objectives: 1. Comparisons of basic characteristics between two groups; 2. Risk factors for death on admission using logistic regression; 3. Dynamic changes of radiographic and laboratory parameters between two groups in the course. Results: 124 patients with severe COVID-19 on admission were included and divided into discharge group (n=35) and death event group (n=89). Sex, SpO2, breath rate, diastolic pressure, neutrophil, lymphocyte, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), and D-dimer were significantly correlated with death events identified using bivariate logistic regression. Further multivariate logistic regression demonstrated a significant model fitting with C-index of 0.845 (p<0.001), in which SpO2≤89%, lymphocyte≤0.64×109/L, CRP>77.35mg/L, PCT>0.20µg/L, and LDH>481U/L were the independent risk factors with the ORs of 2.959, 4.015, 2.852, 3.554, and 3.185, respectively (p<0.04). In the course, persistently lower lymphocyte with higher levels of CRP, PCT, IL-6, neutrophil, LDH, D-dimer, cardiac troponin I (cTnI), brain natriuretic peptide (BNP), and increased CD4+/CD8+ T-lymphocyte ratio and were observed in death events group, while these parameters stayed stable or improved in discharge group. Conclusions: On admission, the levels of SpO2, lymphocyte, CRP, PCT, and LDH could predict the prognosis of severe COVID-19 patients. Systematic inflammation with induced cardiac dysfunction was likely a primary reason for death events in severe COVID-19 except for acute respiratory distress syndrome.

Association of clinical and radiographic findings with the outcomes of 93 patients with COVID-19 in Wuhan, China.

Rationale: To retrospectively analyze serial chest CT and clinical features in patients with coronavirus disease 2019 (COVID-19) for the assessment of temporal changes and to investigate how the changes differ in survivors and nonsurvivors. Methods: The consecutive records of 93 patients with confirmed COVID-19 who were admitted to Wuhan Union Hospital from January 10, 2020, to February 22, 2020, were retrospectively reviewed. A series of chest CT findings and clinical data were collected and analyzed. The serial chest CT scans were scored on a semiquantitative basis according to the extent of pulmonary abnormalities. Chest CT scores in different periods (0 - 5 days, 6 - 10 days, 11 - 15 days, 16 - 20 days, and > 20 days) since symptom onset were compared between survivors and nonsurvivors, and the temporal trend of the radiographic-clinical features was analyzed. Results: The final cohort consisted of 93 patients: 68 survivors and 25 nonsurvivors. Nonsurvivors were significantly older than survivors. For both survivors and nonsurvivors, the chest CT scores were not different in the first period (0 - 5 days) but diverged afterwards. The mortality rate of COVID-19 monotonously increased with chest CT scores, which positively correlated with the neutrophil-to-lymphocyte ratio, neutrophil percentage, D-dimer level, lactate dehydrogenase level and erythrocyte sedimentation rate, while negatively correlated with the lymphocyte percentage and lymphocyte count. Conclusions: Chest CT scores correlate well with risk factors for mortality over periods, thus they may be used as a prognostic indicator in COVID-19. While higher chest CT scores are associated with a higher mortality rate, CT images taken at least 6 days since symptom onset may contain more prognostic information than images taken at an earlier period.

Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19).

Background Chest CT is used to assess the severity of lung involvement in coronavirus disease 2019 (COVID-19). Purpose To determine the changes in chest CT findings associated with COVID-19 from initial diagnosis until patient recovery. Materials and Methods This retrospective review included patients with real-time polymerase chain reaction-confirmed COVID-19 who presented between January 12, 2020, and February 6, 2020. Patients with severe respiratory distress and/or oxygen requirement at any time during the disease course were excluded. Repeat chest CT was performed at approximately 4-day intervals. Each of the five lung lobes was visually scored on a scale of 0 to 5, with 0 indicating no involvement and 5 indicating more than 75% involvement. The total CT score was determined as the sum of lung involvement, ranging from 0 (no involvement) to 25 (maximum involvement). Results Twenty-one patients (six men and 15 women aged 25-63 years) with confirmed COVID-19 were evaluated. A total of 82 chest CT scans were obtained in these patients, with a mean interval (±standard deviation) of 4 days ± 1 (range, 1-8 days). All patients were discharged after a mean hospitalization period of 17 days ± 4 (range, 11-26 days). Maximum lung involved peaked at approximately 10 days (with a calculated total CT score of 6) from the onset of initial symptoms (R2 = 0.25, P < .001). Based on quartiles of chest CT scans from day 0 to day 26 involvement, four stages of lung CT findings were defined. CT scans obtained in stage 1 (0-4 days) showed ground-glass opacities (18 of 24 scans [75%]), with a mean total CT score of 2 ± 2; scans obtained in stage 2 (5-8 days) showed an increase in both the crazy-paving pattern (nine of 17 scans [53%]) and total CT score (mean, 6 ± 4; P = .002); scans obtained in stage 3 (9-13 days) showed consolidation (19 of 21 scans [91%]) and a peak in the total CT score (mean, 7 ± 4); and scans obtained in stage 4 (≥14 days) showed gradual resolution of consolidation (15 of 20 scans [75%]) and a decrease in the total CT score (mean, 6 ± 4) without crazy-paving pattern. Conclusion In patients recovering from coronavirus disease 2019 (without severe respiratory distress during the disease course), lung abnormalities on chest CT scans showed greatest severity approximately 10 days after initial onset of symptoms. © RSNA, 2020.