Determination of Anti-SARS-CoV-2 Activity of Compounds Based on Machine Learning

In 2020, the outbreak of pneumonia caused by novel coronavirus spread rapidly all over the world. In the absence of a specific drug, novel coronavirus is still pandemic all over the world. In this paper, we proposed an improved molecular activity prediction model by adding feature selection method on the basis of comparing different methods to extract molecular features and machine learning models. We first used the anti-SARS-CoV-2 compounds reported in recent literatures to construct the data set, and then constructed three machine learning models. In addition, we tried to use three methods to extract molecular features in each model. In order to further improve the performance of the model, we add three feature selection methods. Through the comparison of different models, finally, we used FCFP to extract molecular features and added lasso feature selection method to establish the SVM model. Its test set accuracy is 90.0%, and the AUC value is 0.961, which could well predict the anti-SARS-CoV-2 activity of the compound. Our model can be used to speed up the research and discovery of anti-SARS-CoV-2 drugs. © 2022 IEEE.

COVID-19 Fake News and Misinformation Detection using Transformer Learning

As COVID-19 spreads globally and generates an unprecedented pandemic, COVID-19 fake news is born and quickly disseminated on the internet. Misinformation and disinformation of COVID-19 can distort public perception of the virus and have a serious negative influence on society. To increase vaccine coverage rates and achieve herd immunity, eliminating fake news becomes an urgent need worldwide. Our research aims at using the Transformer model to implement COVID-19 fake news detection. We use the dataset of COVID-19 fake news, extract features through the embedding method of one hot representation, and construct the transformer model to implement text classification on the binary problem. Then we analyze results through loss curve and confusion matrix and show performance parameters, including accuracy, AUC score, and F1 score. We conclude that the model can achieve an accuracy of 72% for COVID-19 fake news detection. This research provides insight for transformer learning dealing with fake news detection of COVID-19. © 2022 IEEE.

Role of fiscal and monetary policies for economic recovery in China

After the pandemic, China's fiscal and monetary authorities implemented macroeconomic restructuring measures to combat the pandemic. Using a difference-in-difference model based on data collected during the COVID-19 phase, this study attempted to determine the economic recovery in China using the pandemic means for economic growth and energy consumption in other economies. A 0.21 percent increase in the western region's economic growth is comparable to a 0.15 percent increase in the growth of the southern central and northern regions during the pandemic period. Accordingly, we found evidence of actual provincial spillover effects in the clustering of high- and poor-performing regions. The impact of China's economic resurgence beyond the pandemic phase plays an important role in expanding power consumption in different regions. Since headwinds hamper economic development to aggregate output, fiscal policy is the sole option for maintaining pollution levels while simultaneously improving household well-being in terms of demand and employment.

Influences of obstacle factors on the transmission trends of respiratory infectious diseases in indoor public places

Public facilities are important transmission places for respiratory infectious diseases (e.g., COVID-19), due to the frequent crowd interactions inside. Usually, changes of obstacle factors can affect the movements of human crowds and result in different epidemic transmissions among individuals. However, most related studies only focus on the specific scenarios, but the common rules are usually ignored for the impacts of obstacles' spatial elements on epidemic transmission. To tackle these problems, this study aims to evaluate the impacts of three spatial factors of obstacles (i.e., size, quantity, and placement) on infection spreading trends in two-dimension, which can provide scientific and concise spatial design guidelines for indoor public places. Firstly, we used the obstacle area proportion as the indicator of the size factor, gave the mathematical expression of the quantity factor, and proposed the walkable-space distribution indicator to represent the placement factor by introducing the Space Syntax. Secondly, two spreading epidemic indicators (i.e., daily new cases and people's average exposure risk) were estimated based on the fundamental model named exposure risk with the virion-laden particles, which accurately forecasted the disease spreading between individuals. Thirdly, 120 indoor scenarios were built and simulated, based on which the value of independent and dependent variables can be measured. Besides, structural equation modeling was employed to examine the effects of obstacle factors on epidemic transmissions. Finally, three obstacle-related guidelines were provided for policymakers to mitigate the disease spreading: minimizing the size of obstacles, dividing the obstacle into more sub-ones, and placing obstacles evenly distributed in space. © 2022 Elsevier Ltd

A novel deep learning framework with a COVID-19 adjustment for electricity demand forecasting

Electricity demand forecasting is crucial for practical power system management. However, during the COVID-19 pandemic, the electricity demand system deviated from normal system, which has detrimental bias effect in future forecasts. To overcome this problem, we propose a deep learning framework with a COVID-19 adjustment for electricity demand forecasting. More specifically, we first designed COVID-19 related variables and applied a multiple linear regression model. After eliminating the impact of COVID-19, we employed an efficient deep learning algorithm, long short-term memory multiseasonal net deseasonalized approach, to model residuals from the linear model aforementioned. Finally, we demonstrated the merits of the proposed framework using the electricity demand in Taixing, Jiangsu, China, from May 13, 2018 to August 2, 2021. © 2023 The Author(s)

An Incremental Learning-based Framework for Non-stationary Traffic Representations Clustering and Forecasting

To curb the growth of COVID-19, many rules, including a work-from-home policy, were issued in 2020. While these limits successfully prevented the virus's transmission, they completely altered original mobility patterns, resulting in considerable reductions in travel time and vehicle miles traveled. Under this non-stationary data stream, the US Department of Transportation struggled to anticipate future traffic conditions. Obviously, two essential challenges need to be addressed immediately: 1) it is challenging for transportation agencies to learn representative traffic patterns from the continually changing traffic circumstances. And 2) when and how should the forecasting model be updated to learn new patterns without forgetting previous tasks? We proposed an incremental learning-based framework for non-stationary data clustering and forecasting in transportation scenarios to tackle the issues mentioned above. It is a dual-module architecture that includes a Temporal Neighborhood Clustering module and an Incremental Learning module. The objective of the first component is to dynamically detect the optimal boundary for clustering statistically similar neighbors by enlarging both the in-group similarity and between-group dissimilarity. The second module applies the online-EWC approach, which is commonly used in image classification tasks but rarely in regression models, to learn new tasks and avoid catastrophic forgetting, which is a typical occurrence while training neural networks with multiple tasks. Experiments on the Greater Seattle Area employed loop detector data collected in 2020 yielded reliable prediction performance in both robustness and accuracy. The dual-module framework can generate promising results from pre-COVID-19 to post-COVID-19 time frames. This framework would aid government agencies and the general public in developing long-term policies and strategies for post-pandemic intelligent transportation systems. © 2022 IEEE.

Procurement management mode of electrostatic protection materials in supply chain environment-Taking state-owned enterprises as an example

for every enterprise, material procurement is a very important link. It is not only related to cost control and production and management quality, but also determines the final economic benefits of the enterprise. Novel coronavirus pneumonia is a key issue for the enterprises. The overall management of material procurement is carried out throughout the whole business process. Especially in the current market economy environment, the new crown pneumonia epidemic continues to affect the enterprises and face more severe competition environment. If we want to gain more market share in the industry, we must innovate and adjust the original material procurement management mode and benchmark the international and domestic advanced standards. Nowadays, the shadow of Internet technology has penetrated into all walks of life, and new Internet technology has also been applied to the management of enterprise supply chain, which makes the relationship between enterprises closer and plays an important role in the process of enterprise development. Therefore, based on the supply chain environment, this paper gives some targeted measures and suggestions to strengthen the procurement management of electrostatic protection materials in state-owned enterprises for reference. © 2022 IEEE.

Recent Advances in Metal Chalcogenide Quantum Dots: From Material Design to Biomedical Applications

With the rapid progress in nanomaterials and biochemistry, there has been an explosion of interest in biomolecule-modified quantum dots (QDs) for biomedical applications. Metal chalcogenide quantum dots (MCQDs), as the most widely studied QDs, have attracted tremendous attention in the biomedical field on account of their unique and excellent optical properties and the ease of biomolecular modifications. Herein, important advances in MCQDs over recent years are reviewed, from materials design to biomedical applications. Especially, this review focuses on the challenges encountered in the applications of MCQDs in biomedical fields and how these problems can be solved by rational design of synthesis methods and modifications, which have opened a universal route to develop the functionalized MCQDs. Moreover, recent processes in bioimaging, biosensing, and cancer therapy based on MCQDs are examined, including the rapid detection and diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This review provides broad insights into MCQDs in the biomedical field and will inspire material researchers to develop MCQDs in the future.

[Application and reflection of laboratory-based monitoring in early warning of infectious diseases].

Despite the fact that our cognition towards infectious disease prevention, the advanced technology and the economic status of the whole society has made a great progress in the last decade, the outbreak of COVID-19 pneumonia has again enabled the public to acquire more about super-challenges of infectious diseases, epidemics and the relevant preventive measurements. In order to identify the epidemic signals in early stage or even before the onset of epidemic, the data research and utilization of a series of factors related to the occurrence and transmission of infectious diseases have played a significant role in research of prevention and control during the whole period of surveillance and early warning. Laboratory-based monitoring for the etiology has always been an important part of infectious disease warning system due to pathogens as the direct cause of such diseases. China has initially established a laboratory-based monitoring and early warning system for bacterial infectious diseases based on the Chinese Pathogen Identification Network with an aim to identify pathogens, outbreaks and sources. This network has played an essential role in early detection, tracking and precise prevention and control of bacterial infectious diseases, such as plague, cholera, and epidemic cerebrospinal meningitis. This issue focuses on the function of laboratory-based monitoring during the period of early warning, prevention, and control of bacterial infectious diseases, and conducted a wide range of researches based on the analysis of the epidemic and outbreak isolates, together with field epidemiological studies and normal monitoring systems. All of these could illustrate the effect of laboratory surveillance in the infectious disease risk assessment and epidemic investigation. At the same time, we have put forward our review and expectation of scenarios about laboratory-based monitoring and early warning technologies to provide innovative thoughts for promoting a leapfrog development of infectious disease monitoring and early warning system in China.

Should we continue breastfeeding after SARS-CoV-2 infection or mRNA vaccination?

The coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a potential threat to infant health. The World Health Organization recommended that the benefits of breastfeeding far outweigh the potential risk of transmission, but there is no denying that the current evidence is insufficient. Moreover, although the COVID-19 mRNA vaccine has played an effective role in protection against infection, individuals have increasing concerns about the safety of breastfeeding after vaccination, and which have caused some breastfeeding women to postpone vaccination or stop breastfeeding early. Thus, in this review, we provide an in-depth discussion of whether SARS-CoV-2 and the vaccine will affect babies through breast milk. On one hand, only a very small number of milk samples were identified positive for viral RNA and almost impossible to be live virus particles. The milk of most lactating women after vaccination did not contain vaccine-related mRNA and polyethylene glycol. On the other hand, the antibodies and biologically active molecules like lactoferrin are abundant in the milk of lactating women who have been infected or vaccinated, which can provide potential protection against infants' respiratory and gastrointestinal infections. Therefore, in terms of implications for clinical practice, the results of our study support that lactating women who have been infected or vaccinated should be encouraged to breastfeed their infants under the premise of taking appropriate sanitary measures. © 2022 Centro Regional de Invest. Cientif. y Tecn.. All rights reserved.

Boosting Fairness for Masked Face Recognition

Face recognition achieved excellent performance in recent years. However, its potential for unfairness is raising alarm. For example, the recognition rate for the special group of East Asian is quite low. Many efforts have spent to improve the fairness of face recognition. During the COVID-19 pandemic, masked face recognition is becoming a hot topic but brings new challenging for fair face recognition. For example, the mouth and nose are important to recognizing faces of Asian groups. Masks would further reduce the recognition rate of Asian faces. To this end, this paper proposes a fair masked face recognition system. First, an appropriate masking method is used to generate masked faces. Men, a data re-sampling approach is employed to balance the data distribution and reduce the bias based on the analysis of training data. Moreover, we propose an asymmetric-arc-loss which is a combination of arc-face loss and circle-loss, it is useful for increasing recognition rate and reducing bias. Integrating these techniques, this paper obtained fairer and better face recognition results on masked faces.

[Epidemiological characteristics of novel coronavirus pneumonia in Shijiazhuang, China].

Objective: To analyze the epidemiological characteristics of an outbreak of novel coronavirus pneumonia (COVID-19) in Shijiazhuang, Hebei Province in 2021 and to provide scientific basis for developing improved strategies to prevent and control the outbreak of COVID-19. Methods: Descriptive analysis of the outbreak of COVID-19 in Shijiazhuang, Hebei Province was performed with SPSS 21.0 and Excel software. The statistical analysis of the incubation period was performed using the rstan package in R4.0.4. Results: As of February 14th 2021, a total of 942 local confirmed cases were reported in Hebei Province, 869 cases in Shijiazhuang, of which 847 cases were available for case information. This outbreak was mainly in rural areas, with the largest number of confirmed cases in Xiaoguozhuang village, 249 (29.4%); followed by Nanqiaozhai village, 128 (15.1%); and Liujiazuo village, 85 (10.0%). The outbreak lasted from January 2nd, 2021 to February 14th, 2021, and was mainly transmitted among the farmers as well as the students through dining parties, public gatherings and family contacts, showing an obvious time and occupation concentration trend. An analysis of 116 local confirmed cases in this outbreak with specific exposure time and onset time indicated that the median incubation period was 6 [interquartile range(IQR): 3.3, 10.0] days; whereas another report including 264 local confirmed cases with specific exposure time window showed that a median incubation period was 8.5 [95% confidence interval (CI): 1.8-18.8] days. Conclusions: This outbreak was mainly related to rural areas, and was associated with parties, public gatherings and family gatherings. Self-protection and isolation of key areas and populations at risk should be effectively implemented to avoid close contact and other measures to reduce the occurrence of COVID-19 aggregation. Based on the results of the incubation period of this outbreak, the isolation period could be recommended to be extended to three weeks.

Multivariate Long and Short Term LSTM-Based Network for Traffic Forecasting under Interference: Experiments during COVID-19

Due to COVID-19, work-from-home policy and travel restrictions were taken to decelerate the virus spreading. While these policies successfully eliminated the transmission of COVID-19, original traffic patterns have been completely disrupted, including considerable reductions in travel time and vehicle miles traveled. The impacted traffic patterns from the unexpected event brings challenges to the U.S. Department of Transportation and transportation planners. With fluctuated traffic conditions, it is difficult for transportation agencies to learn representative traffic patterns from short-term historical data. Therefore, we proposed a multivariate long and short-term LSTM-based model (var LS-LSTM) for network-wide traffic forecasting under interference. We considered multiple spatial and temporal features to evaluate network-wide traffic performance and forecast the influenced travel behaviors. Multi-dimensional spatial-temporal features were fused into long-term and short-term historical matrices and fed into the model, which enabled the model to accommodate intervention from unexpected events. Thorough experiments were conducted using loop detector data in the Greater Seattle Area from 2020 to early 2021 and achieved reliable prediction performance in both robustness as well as accuracy. The proposed model showed competitiveness against other state-of-art algorithms in all experiment time frames, from pre-COVID-19 to COVID-19-relieving period. This study would benefit government agencies and the general public in making sustainable policies and future resilience plans for post-pandemic smart cities. © 2021 IEEE.

COVID-19 related stress intensify the impact of child maltreatment on sleep quality

Introduction: Child maltreatment (CM) is a significant stressor that is associated with sleep problems in children and adolescents. The COVID-19 pandemic introduces new psychosocial stressors, which may be particularly harmful to youth already experiencing stress in the home environment. Using multi-dimensional (threat vs deprivation) assessments of CM, the present study aimed to test whether COVID-19 related stress intensified the association between maltreatment (abuse vs neglect) and sleep problems among youth. Methods: This study utilized data from a longitudinal sample of youth (N=126;Mage at T1=12.9) assessed between January 2019 and March 2020 (T1) and after the beginning of the COVID-19 pandemic (May 2020;T2). Latent factors for COVID-19 related stress included three questions asking about negative changes, uncertainty about the future, and stress-induced by disruptions. CM at T1 was measured with the Childhood Trauma Questionnaire (CTQ). Multidimensional aspects of CM included a threat factor (sum of Emotional, Physical, and Sexual Abuse) and a deprivation factor (sum of Emotional and Physical Neglect). Sleep-related problems at both T1 and T2 were assessed using the Pittsburgh Sleep Quality Index (PSQI) global score. Structural equation modeling was conducted in Mplus 8.1 to test direct and interaction effects of CM and COVID-19 related stress on sleep problems at T2 while controlling for sleep problems at T1 and demographic covariates. Results: Threat-related abuse was significantly associated with increased sleep problems at T2 (β =.43, p < .01) but neglect was not (β =.03, p = .85). Additionally, COVID-19 related stress significantly intensified the link between abuse and sleep problems (β =.14, p < .05) as well as between neglect and sleep problems (β =.43, p < .01) at T2. Among youth who experienced higher levels of CM, increased COVID-19 related stress exacerbated sleep problems. Conclusion: These results bolster extant research on the negative impact CM bears on youth sleep health and indicates that COVID- 19 stress may exacerbate sleep problems. Our findings inform future prevention and intervention efforts that aim to reduce sleep problems among youth who experience CM during the COVID-19 pandemic.

COVID-19 as a factor influencing air quality? A city study in China

To prevent the spread of coronavirus disease 2019 (COVID-19), which emerged in late December 2019, the Chinese government immediately adopted lockdown measures, such as restricting traffic and closing factories. By analyzing the spatiotemporal distribution of the air quality index (AQI) values in Dongying, a city dominated by the petrochemical industry (specifically, petroleum exploration), during February 2020, when the strictest measures were in force, this study investigates the effect of short-term lockdowns on air quality. We observed a statistically significant reduction in the monthly average AQI—24.6%, or an absolute decrease of 25.4—compared to February 2019. Additionally, the difference between the maximum and the minimum hourly average AQI dropped to almost one-third of the value that in the normal time during winter. We also assessed the influence of meteorological factors and industrial exhaust emissions. Quantitative analysis revealed a strong positive correlation (p < 0.01) between the AQI and exhaust emissions, confirming the latter’s contribution to air pollution. However, this contribution shrunk by approximately 38.3% during February 2020. Our results indicate that the improvement in air quality was related to traffic reduction and enterprise closures during the lockdown, which only marginally affected the spatial distribution of the AQI values. This research serves as a reference for controlling air pollution in Dongying and areas with similar conditions. © The Author(s).

Research on Effects of Online PAD Class on Autonomous Learning for College English Learners Based on Pearson Test

This paper is based on a survey conducted after the college English learners finished a semester' online learning in the COVID-19 pandemic. Pearson Test of the survey from the dimensions: self-confidence establishment, learning motivation stimulation, learning habit development, and communication or interaction to strengthening was to test whether PAD Class is positive in cultivating autonomous learning ability. The Pearson correlation analysis showed PAD Class is medium positive to the development of autonomous learning ability. © 2021 IEEE.

Allogeneic Stem Cell Transplantation in a Large Urban Cohort of North-American Adult T-Cell Leukemia/Lymphoma

Introduction: Adult T-cell leukemia lymphoma (ATLL) is a rare hematologic malignancy caused by human T-cell lymphotropic virus (HTLV-1) with dismal cure rates and poor response to conventional chemotherapy. Allogeneic Hematopoietic Stem Cell Transplantation (AlloSCT) is the only therapeutic option which may offer the chance of long-term remission and cures in a subset of patients. We sought to investigate the outcomes of transplantation in one of the largest cohorts in North America. Methods: A retrospective chart review study was conducted using the North-American ATLL and the Hematopoietic Precursor Cell transplantation databases at Montefiore Medical Center from 2011 to 2020. Variables collected include age, sex, ethnicity, ATLL subtype, molecular profile, previous treatments, conditioning regimens, type of transplant, immunosuppressive regimen, progression free survival (PFS) post-transplant and overall survival (OS) post-transplant. Results: Fourteen patients with ATLL who received an AlloSCT from 2011-2020 were identified. Fifty-seven percent (8/14) of patients were male. Seventy-one percent (10/14) of patients were African American and twenty-nine percent (4/14) were Hispanic. Median age was 51 years. Sixty-four percent (9/14) of patients had Stage IV disease at the time of diagnosis. Forty-three percent (6/14) patients had acute and fifty-seven percent (8/14) had lymphomatous ATLL. Almost all patients (92%) were treated initially with EPOCH combination chemotherapy. Twenty-eight percent (4/14) of patients received interferon/zidovudine as bridge-to-transplant. Fifty-seven percent (8/14) of patients achieved complete remission (CR) prior to AlloSCT, 7% (1/14) were in partial remission, and 28% (4/14) were relapsed or refractory. Forty-three percent (6/14) of patients received SCT from a matched-related donor (MRD), 36% (5/14) from a haplo-identical donor and 21% (3/14) from a matched-unrelated donor (MUD). Ninety-three percent (13/14) of patients received a reduced-intensity conditioning (RIC) regimen pre-transplantation. Seven percent (1/14) received a myeloablative conditioning (MAC) regimen. RIC regimens consisted of fludarabine with melphalan +/- anti-thymocyte globulin (ATG) or fludarabine with cyclophosphamide with total-body irradiation in doses less than 500 cGy. Patients receiving haplo-identical SCT also received post-transplant cyclophosphamide (PTCy) for prevention of graft vs host disease (GVHD). The MAC regimen used included busulfan with cyclophosphamide at myeloablative doses. Twenty-eight percent (4/14) of patients relapsed post-alloSCT with a median relapse-free survival of 6 months (range 4-18 months). The median OS of the whole cohort was 27 months (8-82 months). Graft-versus-host disease (GVHD) developed in 28% (4/14) percent of patients. The most common manifestation was skin GVHD. Fifty-percent (7/14) of the patients are surviving to-date. Transplant-related mortality (TRM) at day 100 was 21% (3/14) of patients. Causes of death were complex and included several diagnoses in certain patients. The most frequent diagnoses associated with death were infection (28%), graft failure (14%), GVHD (14%), veno-occlusive disease of the liver (VOD) (7%), disease progression (14%) and unknown due to patient lost to follow-up (14%). The main infectious events included fungal (2), bacterial (1), and COVID-19 (1) infection. Forty-three percent (6/14) of patients remain in complete remission to date. Conclusions: Allogeneic Stem Cell Transplantation offers long-term survival with a TRM of 21% in a disease with an inherently dismal prognosis. AlloSCT using several graft sources, is thus, a safe and well tolerated treatment modality and offers long term remissions. Disclosures: Steidl: Pieris Pharmaceuticals: Consultancy;Aileron Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding;Bayer Healthcare: Research Funding;Stelexis Therapeutics: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advi ory committees. Verma: BMS: Consultancy, Research Funding;acceleron: Consultancy, Honoraria;Janssen: Research Funding;Medpacto: Research Funding;stelexis: Current equity holder in private company. Janakiram: ADC Therapeutics, FATE therapeutics, TAKEDA pharmaceuticals: Research Funding.

Dynamics of Leukocyte Subpopulations Reconstitution Predict Infection Propensity in a Multiethnic Real World Cohort Treated with Anti-CD19 CAR-T Cell Therapy (Axicabtagene-Ciloleucel)

Background: Adoptive immunotherapy using CD19-targeted Chimeric Antigen Receptor T-cells (CAR-T) has revolutionized the treatment of relapsed/refractory diffuse large B-cell lymphoma (DLBCL). We have demonstrated the efficacy of FDA-approved axicabtagene ciloleucel (Yescarta) in a multiethnic New York City underserved population with 80% complete response (CR) rate in the first ten patients treated at our institution (Abbasi et al., 2020). There is limited data on the propensity of infections and lymphohematopoietic reconstitution after Day 30 (D30) following CAR-T cell therapy. In this study, we evaluated the prevalence and nature of infectious complications in an expanded cohort of DLBCL patients treated with CD19 CAR-T therapy and its association with the dynamics of leukocyte subpopulation reconstitution post-CAR-T cell therapy. Methods: We conducted a retrospective study of patients who received CAR-T therapy at our institution between 2018-2020. Variables collected include patient demographics, absolute neutrophil (ANC), lymphocyte (ALC) and monocyte counts (AMC) at Day 30, hematologic reconstitution (ANC≥ 1500/µL) at Day 90 (D90), presence or absence of infections after D30 by clinical and/or microbiological parameters. Associations between presence of infection and D30 ANC, ALC, AMC, ANC/ALC ratio, AMC/ALC ratio were assessed using Kruskal-Wallis test. Association between infection and hematologic reconstitution at D90 was done using Chi-square test. Kaplan-Meier curves with log-rank test were used to evaluate overall survival (OS) and progression-free survival (PFS). Results: Nineteen patients were evaluated in our study, consisting of 42% (8) Hispanic, 32% (6) Caucasian, 21% (4) African-American, and 5% (1) Asian subjects. Based on clinical and microbiologic data, 47% (9) developed an infection after D30 (infection group) while 53% (10) of subjects remained infection-free after D30 (non-infection group). The most common infection type observed was viral (11 patients) followed by bacterial (8 patients) and fungal (3 patients) (Table 1). Of 25 total infectious events, 44% (11) were grade 1 or 2 and 48% (12) were grade 3 with 10 being viral in etiology. Two deaths occurred due to an infectious process. Three patients tested SARS-CoV-2 positive and were hospitalized with COVID-19 pneumonia. Median OS and PFS has not been reached in either group. To determine the kinetics of lymphohematopoietic reconstitution and its association with infection risk, we evaluated the relationship between cytopenias and rates of infection after D30. Notably, compared to non-infection group, infection group had a higher median ALC (1000/µL vs 600/µL p=0.04), a lower median ANC/ALC ratio (1.4 vs 4.5 p<0.01) and a lower median AMC/ALC at D30 (0.36 vs 1.33, p=0.01) (Table 2). In addition, patients in the infection group had a lower rate of hematologic reconstitution (ANC >1500/µL) at D90. We observed that only 22% (2) of patients had recovered ANC > 1500/µLin the infection group as opposed to 80% (8) in the non-infection group at D90 (p= 0.038). Rates of cytokine release syndrome (CRS) were comparable between the two groups (55.6% vs 70% p=0.52). Surprisingly, rates of immune-effector cell associated neurotoxicity syndrome (ICANS) was lower (55.6%) in the infection group compared to (90%) non-infection group (p=0.09). Fourteen of 19 patients had follow-up over one year, of which 8 (57%) remained in complete remission (CR). Conclusions: We demonstrate an infection rate of 47% (9) beyond D30 in patients undergoing CD19 CAR-T. Increased ALC, lower ANC/ALC and AMC/ALC ratios at D30 may be predictive of infectious complications. Median OS has not been reached in our cohort. Given the potential clinical impact, our observations should be corroborated using larger datasets. [Formula presented] Disclosures: Steidl: Pieris Pharmaceuticals: Consultancy;Bayer Healthcare: Research Funding;Stelexis Therapeutics: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees;Ai eron Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Janakiram: ADC Therapeutics, FATE therapeutics, TAKEDA pharmaceuticals: Research Funding. Verma: BMS: Consultancy, Research Funding;acceleron: Consultancy, Honoraria;Janssen: Research Funding;stelexis: Current equity holder in private company;Medpacto: Research Funding.

Structural basis for neutralization of SARS-CoV-2 and SARS-CoV by a potent therapeutic antibody

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented public health crisis. There are no approved vaccines or therapeutics for treating COVID-19. Here we report a humanized monoclonal antibody, H014, that efficiently neutralizes SARS-CoV-2 and SARS-CoV pseudoviruses as well as authentic SARS-CoV-2 at nanomolar concentrations by engaging the spike (S) receptor binding domain (RBD). H014 administration reduced SARS-CoV-2 titers in infected lungs and prevented pulmonary pathology in a human angiotensin-converting enzyme 2 mouse model. Cryo-electron microscopy characterization of the SARS-CoV-2 S trimer in complex with the H014 Fab fragment unveiled a previously uncharacterized conformational epitope, which was only accessible when the RBD was in an open conformation. Biochemical, cellular, virological, and structural studies demonstrated that H014 prevents attachment of SARS-CoV-2 to its host cell receptors. Epitope analysis of available neutralizing antibodies against SARS-CoV and SARS-CoV-2 uncovered broad cross-protective epitopes. Our results highlight a key role for antibody-based therapeutic interventions in the treatment of COVID-19.