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Existing studies show that the outbreak of the COVID-19 pandemic has a significant impact on crime occurrences. However, it remains to be empirically examined whether the regular pandemic prevention and control measures in China can significantly affect the spatial patterns of crimes. To fill this gap, this study chosed the central urban area of Haining City in Zhejiang Province, which had taken the regular pandemic prevention and control measures, as the research area. Using standard deviation ellipse and kernel density estimation methods, this study examined the spatiotemporal patterns of theft crimes between February 2018 and July 2021 in the study area. The results show that: 1) While there were no significant changes in the hourly patterns of theft crimes within a day, the monthly number of thefts in the study area decreased by an average of 53% after the outbreak of the COVID-19 pandemic and fluctuated between 100 to 200 thefts per month. The regular COVID-19 pandemic prevention and control measures had a long-term and stable inhibitory effect on the number of theft crimes in Haining City. 2) The intensity and amount of hotspots for theft crimes in the study area decreased significantly during February 2020 and July 2021 when compared with the pre-pandemic period, and the spatial distribution patterns of theft crimes were relatively stable. 3) For the 12 crime hotspots identified for the pre-pandemic period, the monthly number of crimes decreased between 52% and 78% on average after the outbreak while trajectories of their quarterly temporal changes differed significantly. The average crime drop for the 12 hotspot areas were 63%, which was higher than that for the whole study area. 4) Seven crime-control measures were identified among the regular pandemic prevention and control measures based on the routine activity theory and the crime triangle theory. Functional type of place, geographic location, and pre-pandemic local governance ability all played an important role in affecting the actual crime-control effects of the seven regular COVID-19 pandemic prevention and control measures. The results of this study are important for understanding the spatial evolution of crimes under the influence of major public health emergencies and for formulating scientific strategies for crime prevention and control. © 2023, Editorial office of PROGRESS IN GEOGRAPHY. All rights reserved.
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Objective:To explore the differences in laboratory indicators test results of coronavirus disease 2019 (COVID-19) and influenza A and to establish a differential diagnosis model for the two diseases, and to clarify the clinical significance of the model for distinguishing the two diseases. Methods :A total of 56 common COVID-19 patients and 54 influenza A patients were enrolled , and 24 common COVID-19 patients and 30 influenza A patients were used for model validation. The average values of the laboratory indicators of the patients 5 d after admission were calculated,and the elastic network model and the stepwise Logistic regression model were used to screen the indicators for identifying COVID-19 and influenza A. Elastic network models were used for the first round of selection,in which the optimal cutoff of lambda was chosen by performing 10-fold cross validations. With different random seeds,the elastic net models were fit for 200 times to select the high-frequency indexes ( frequency>90% ). A Logistic regression model with AIC as the selection criterions was used in the second round of screening uses;a nomogram was used to represent the final model;an independent data were used as an external validation set,and the area under the curve (AUC) of the validation set were calculate to evaluate the predictive the performance of the model. Results:After the first round of screening, 16 laboratory indicators were selected as the high-frequency indicators. After the second round of screening,albumin/ globulin (A/G),total bilirubin (TBIL) and erythrocyte volume (HCT) were identified as the final indicators. The model had good predictive performance , and the AUC of the verification set was 0. 844 (95% CI:0. 747-0. 941). Conclusion:A differential diagnosis model for COVID-19 and influenza A based on laboratory indicators is successfully established,and it will help clinical and timely diagnosis of both diseases.Copyright © 2022 Jilin University Press. All rights reserved.
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Dayuanyin (DYY) has been shown to reduce lung inflammation in both coronavirus disease 2019 (COVID-19) and lung injury. This experiment was designed to investigate the efficacy and mechanism of action of DYY against hypoxic pulmonary hypertension (HPH) and to evaluate the effect of DYY on the protection of lung function. Animal welfare and experimental procedures are approved and in accordance with the provision of the Animal Ethics Committee of the Institute of Materia Medica, Chinese Academy of Medical Science. Male C57/BL6J mice were randomly divided into 4 groups: control group, model group, DYY group (800 mg.kg-1), and positive control sildenafil group (100 mg.kg-1). The animals were given control solvents or drugs by gavage three days in advance. On day 4, the animals in the model group, DYY group and sildenafil group were kept in a hypoxic chamber containing 10% +/- 0.5% oxygen, and the animals in the control group were kept in a normal environment, and the control solvent or drugs continued to be given continuously for 14 days. The right ventricular systolic pressure, right ventricular hypertrophy index, organ indices and other metrics were measured in the experimental endpoints. Meantime, the expression levels of the inflammatory factors in mice lung tissues were measured. The potential therapeutic targets of DYY on pulmonary hypertension were predicted using network pharmacology, the expression of nuclear factor kappa B (NF- kappaB) signaling pathway-related proteins were measured by Western blot assay. It was found that DYY significantly reduced the right ventricular systolic pressure, attenuated lung injury and decreased the expression of inflammatory factors in mice. It can also inhibit hypoxia-induced activation of NF- kappaB signaling pathway. DYY has a protective effect on lung function, as demonstrated by DYY has good efficacy in HPH, and preventive administration can slow down the disease progression, and its mechanism may be related to inhibit the activation of NF-kappaB and signal transducer and activator of transcription 3 (STAT3) by DYY.Copyright © 2023, Chinese Pharmaceutical Association. All rights reserved.
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Constructing an epidemic dynamic model and exploring the spreading law of epidemic have very important theoretical significance for epidemic prevention and control. Based on the existing homogeneous mixing model, in view of the increasingly obvious heterogeneity of individual contact relationships, and each individual is in a different contact relationship, a dynamic small-world network model that takes into account individual status. Contact tracking has been established to simulate the spread of the COVID-19 in society. By comparing the simulation results, the rationality of the built model is explained. On this basis, the simulation calculated the impact of the network topology and the proportion of vaccinated people on the spread of the COVID-19, analyzed the critical value of herd immunity. The established propagation model is reasonable, and feasible to achieve herd immunization by vaccination. © 2023 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.
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As cruise ships call at many ports and passengers come from all over the world, it is very easy to carry viruses on cruise ships. Under the control of the epidemic situation on board, the solid waste generated by them should be scientifically treated to prevent the spread of infectious diseases such as COVID-19 pneumonia. Therefore, Reasonable selection of waste disposal ports and formulation of unloading plans are directly related to the resumption of cruise operations. This study considers the cost and risk of waste disposal, uses robust optimization to deal with waste volume, increases the scenarios of port service interruption due to epidemics and other reasons, and proposes a variety of emergency strategies. Finally, the relevant strategies are selected according to the decision-maker's preference for cost and risk;By solving the relevant examples, the optimal choice of the cruise ship waste disposal port under the epidemic situation is given, which verifies the validity and feasibility of the model. The research helps to improve the management of cruise waste during the post-epidemic period, and has practical value and guiding significance for the normal operation and development of the global cruise market. © 2023 SPIE.
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To better understand how public transport use varied during the first year of COVID-19, we define and measure travel behavior resilience. With trip records between November 2019 and September 2020 in Kunming, China, we identify people who relied on traveling by subway both before and after the first pandemic wave. We investigate whether and how travelers recover to their pre-pandemic mobility level. We find that public transport use recovered slowly, as urban mobility is a result of urban functionality, transport supply, social context, and inter-personal differences. In general, urban mobility represents a strengthened revisiting tendency during COVID-19, as individual's trips occur within a more limited space. We confirm that travel behavior resilience differs by groups. Commuters recover travel frequency and length, while older people decrease frequency but retain activity space. The study suggests that policymakers take group heterogeneity and travel behavior resilience into account for transport management and city restoration.
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In supply chain management (SCM), product classification and demand forecasting are crucial pillars to ensure companies to have production in the right category and quantity for long-term profitability. Due to COVID-19 from 2019, the automobile industry has been seriously negatively affected as the demand dropped dramatically. Therefore, it is necessary to make reasonable product classification and accurate demand forecasting to facilitate automobile companies in SCM to reduce unpopular product manufacture and unnecessary storage costs. In this paper, the Canada automobile market has been chosen with the period from 1946 to 2022. To classify a number of different types of motor vehicles into several categories with general characteristics, K-means Clustering method is applied. With the seasonal patterns and random generated features for auto sales, the time series models ARIMA and SARIMA are adopted for demand forecasting. According to the analysis, the automobiles fitting in the category with high demand and low price are valuable for further production. In addition, SARIMA Model is more accurate and fits better than ARIMA Model for both the training and test datasets for long-term prediction. The classification and forecasting results shed light on guiding manufacturers to adjust production schemes and ensuring auto dealers to predict more accurate sales in order to optimize the strategic planning. © 2023 SPIE.
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Background During the containment of COVID-19，the traditional face-to-face interventions conducted at the rehabilitation center were plagued by many limitations，while internet-based interventions can overcome the limitations of geographic location，working hours and transportation，with less medical costs. Objective To examine the effects of internet-based interventions on knowledge，attitude/belief and practice（KAP） toward rehabilitation exercises，physical activity（PA）level，and exercise compliance inpatients after PCI. Methods The subjects were 76 patients who received their first PCI in Department of Cardiology，Tangshan Gongren Hospital from November 2021 to June 2022. They were randomly and equally divided into two groups to receive either internet-based intervention with routine nursing（experimental group） or routine nursing （control group）. Before and three months after the intervention，the Rehabilitation Exercise Knowledge-Belief-Practice Scale for Patients with Coronary Heart Disease（REKBPCHD），the International Physical Activity Questionnaire-Short Form （IPAQ-SF），and Patients' Exercise Log were used to assess the KAP level，PA level，and exercise adherence，respectively. The impact of network intervention on exercise adherence in patients after PCI by univariate Logistic regression analysis. Results After 3 months of intervention，a significant increase was found in the average total score of REKBPCHD，and the average scores of the knowledge dimension，attitude dimension and practice dimension of the scale in the experimental group（P<0.05），and the increase was more notable than that in the control group（P<0.05）. Moreover，both post-intervention low PA level and total PA level in the experimental group were higher than those in the control group（P<0.05）. Both post-intervention low PA level and total PA level were higher than the baseline levels in the experimental group（P<0.05）. The post-intervention exercise compliance of experimental group were higher than that in the control group（P=0.003）. Univariate Logistic regression analysis showed that the risk of non-adherence to exercise in the experimental group was relatively lower than that in the control group 〔OR=0.143，95%CI（0.034，0.594），P=0.007〕. Conclusion Theinternet-based intervention can effectively improve the KAP level toward rehabilitation，PA level，and exercise adherence in patients after PCI. © 2023 Chinese General Practice. All rights reserved.
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Objective To analyze the epidemiological characteristics of a Human rhinovirus outbreak in a primary school in northern Shaanxi, and to provide scientific evidence for the prevention and control. Methods On - site epidemiological investigation of an unexplained febrile aggregated outbreak reported in a primary school in northern Shaanxi on May 22, 2020. Nasopharyngeal swabs were collected from typical cases, and nucleic acid testing was performed to test for SARS COV 2, and 16 respiratory pathogens. Results A total of 37 cases were reported, including 1 adult teacher and 36 students, with the overall incidence rate of 1.75%, a male and female ratio of 3:1, and the incidence age mainly concentrated in 6 to 12 years old. The cases were mainly concentrated in 3 first-grade classes and 7 second-grade classes on the same floor, and the first grade cases accounted for 75.68% of the total number of cases. There was a statistically significant difference in the incidence rate of the cases in the classes (χ2 = 49.29, P<0.01). The clinical features of the cases were mainly fever (body temperature between 37.3 and 38.8°C), sore throat, runny nose, nasal congestion and cough, and some of which were accompanied by diarrhea and vomiting, and other gastrointestinal symptoms. Of the 33 nasopharyngeal swabs detected by laboratory, 14 were positive for Rhinovirus, and the positive rate was 42.42%. Conclusion This aggregated outbreak is caused by Rhinovirus infection. Primary and secondary schools in northern Shaanxi should be alert for aggregated unexplained fever due to Rhinovirus outbreaks during the epidemic season of respiratory infectious diseases. © 2023, Editorial Department of Medical Pest Control. All rights reserved.
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In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects. To address these problems, a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed, which combines the generalized noise technique, relaxes the equational weight constraint in the objective function as the boundary constraint, and uses a genetic algorithm as a method to optimize the initialized clustering center. The genetic algorithm finds the best clustering center and reduces the algorithm's dependence on the initial clustering center. The experiment verifies the robustness of the algorithm, as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People's Hospital with specific high accuracy for clinical medicine.
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Objective: Although the neurological and olfactory symptoms of coronavirus disease 2019 have been identified, the neurotropic properties of the causative virus, severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2), remain unknown. We sought to identify the susceptible cell types and potential routes of SARS-CoV-2 entry into the central nervous system, olfactory system, and respiratory system. Method(s): We collected single-cell RNA data from normal brain and nasal epithelium specimens, along with bronchial, tracheal, and lung specimens in public datasets. The susceptible cell types that express SARS-CoV-2 entry genes were identified using single-cell RNA sequencing and the expression of the key genes at protein levels was verified by immunohistochemistry. We compared the coexpression patterns of the entry receptor angiotensin-converting enzyme 2 (ACE2) and the spike protein priming enzyme transmembrane serine protease (TMPRSS)/cathepsin L among the specimens. Result(s): The SARS-CoV-2 entry receptor ACE2 and the spike protein priming enzyme TMPRSS/cathepsin L were coexpressed by pericytes in brain tissue;this coexpression was confirmed by immunohistochemistry. In the nasal epithelium, ciliated cells and sustentacular cells exhibited strong coexpression of ACE2 and TMPRSS. Neurons and glia in the brain and nasal epithelium did not exhibit coexpression of ACE2 and TMPRSS. However, coexpression was present in ciliated cells, vascular smooth muscle cells, and fibroblasts in tracheal tissue;ciliated cells and goblet cells in bronchial tissue;and alveolar epithelium type 1 cells, AT2 cells, and ciliated cells in lung tissue. Conclusion(s): Neurological symptoms in patients with coronavirus disease 2019 could be associated with SARS-CoV-2 invasion across the blood-brain barrier via pericytes. Additionally, SARS-CoV-2-induced olfactory disorders could be the result of localized cell damage in the nasal epithelium.Copyright © Wolters Kluwer Health, Inc. All rights reserved.
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Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results. © 2022 IEEE.
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With the continuous developing of the COVID-2019, more and more countries and regions have strictly supervised the personal information and location data of confirmed patients and their close contacts. At the same time, how to share the necessary information of patients while ensuring that the personal privacy of patients and their close contacts is not leaked, the access process is transparent, traceable, and data is not tampered with, has become a key issue that needs to be solved urgently. Based on this, we propose an accountable medical attribute pass (AMAP) access control scheme in this paper. The scheme first combines the blockchain with an attribute-based access control model. While introducing the blockchain to trace the source of the access process, the access control strategy and key steps in the access system are deployed on the blockchain in the form of smart contracts, so that the entire system can not only ensure the safe access of users to data, but also trace the source of the entire access process. In particular, the solution introduces the medical attribute pass module. Users apply for access in the form of a pass, which avoids multiple matches between subject attributes and access control strategies in the traditional access control model. While achieving fine-grained access control to medical data, a certain degree Improved access efficiency. Finally, the security analysis shows that this scheme can resist denial of service attacks, malicious tampering attacks, single point of failure attacks, main body masquerading attacks, replay attacks, etc. Experiments and performance analysis show that this solution is compared with other solutions. Under the same access control strategy, the more access times, the more obvious the advantages of this solution;the more access control strategies have the same access control strategy, the more effective the solution is. The more obvious the advantages. © 2023 Journal of Propulsion Technology. All rights reserved.
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In this paper, a research was conducted to analyse and predict the impacts of COVID-19 on public transportation ridership in the U.S. and 5 most populous cities of the U.S. (New York City, Los Angeles, Chicago, Houston, Philadelphia). The paper aims to exploit the correlation between COVID-19 and public transportation ridership in the U.S. and make the reasonable prediction by machine learning models, including ARIMA and Prophet, to help the local governments improve the rationality of their policy implementation. After correlation analyses, high level of significant and negative correlations between monthly growth rate of COVID-19 infections and monthly growth rate of public transportation ridership are decidedly validated in the total U.S., and New York City, Los Angeles, Chicago, Philadelphia, except Houston. To analyse the errors of Houston, we consult the literature and made a discussion of Influencing factors. We find that the level of public transportation in quantity and utilization is terribly low in Houston. In addition, the factors, such as the lack of planning law and estimation of urban expressways, the high level of citizens' dependence on private cars and pride of owning cars play a considerable roll in the errors. And the impacts can be predicted to a certain extent through two forecasting models (ARIMA and Prophet), although the precision of our models is not enough to make a precise forecast due to the limitations of model tuning and model design. According to the comparison of the two models, ARIMA models' forecasting accuracy is between 6% and 10%, and Prophet's forecasting accuracy is between 8%-12%, depending on the city. Since the insufficient stationarity, periodicity, seasonality of time series, the Prophet models are hard be more refined. © 2023 SPIE.
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Preventing the spread of Coronavirus Disease 2019 (COVID-19) has become the focus of epidemiologists as the highly infectious respiratory disease spreads primarily by close, person-to-person contact via droplets or the skin. Aerosol dissemination may occur in a closed, high-aerosol environment. The aerosols generated in dental procedures can pollute surrounding air and device surfaces. In this paper, we summarize prevention and control measures relating to dentistry. We focus on the relationship between COVID-19 and dental disease prevention and control in dental treatment procedures and imaging examinations, oral health education and perspectives, and guidance for the practice of dentistry during the COVID-19 pandemic to provide a consistent and broadly endorsed standard for dental hospital and clinics. Copyright © 2021 Luo, Wang, Tang, Peng, Ma and Wu.
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Dayuanyin (DYY) has been shown to reduce lung inflammation in both coronavirus disease 2019 (COVID-19) and lung injury. This experiment was designed to investigate the efficacy and mechanism of action of DYY against hypoxic pulmonary hypertension (HPH) and to evaluate the effect of DYY on the protection of lung function. Animal welfare and experimental procedures are approved and in accordance with the provision of the Animal Ethics Committee of the Institute of Materia Medica, Chinese Academy of Medical Science. Male C57/BL6J mice were randomly divided into 4 groups: control group, model group, DYY group (800 mg.kg-1), and positive control sildenafil group (100 mg.kg-1). The animals were given control solvents or drugs by gavage three days in advance. On day 4, the animals in the model group, DYY group and sildenafil group were kept in a hypoxic chamber containing 10% +/- 0.5% oxygen, and the animals in the control group were kept in a normal environment, and the control solvent or drugs continued to be given continuously for 14 days. The right ventricular systolic pressure, right ventricular hypertrophy index, organ indices and other metrics were measured in the experimental endpoints. Meantime, the expression levels of the inflammatory factors in mice lung tissues were measured. The potential therapeutic targets of DYY on pulmonary hypertension were predicted using network pharmacology, the expression of nuclear factor kappa B (NF- kappaB) signaling pathway-related proteins were measured by Western blot assay. It was found that DYY significantly reduced the right ventricular systolic pressure, attenuated lung injury and decreased the expression of inflammatory factors in mice. It can also inhibit hypoxia-induced activation of NF- kappaB signaling pathway. DYY has a protective effect on lung function, as demonstrated by DYY has good efficacy in HPH, and preventive administration can slow down the disease progression, and its mechanism may be related to inhibit the activation of NF-kappaB and signal transducer and activator of transcription 3 (STAT3) by DYY.Copyright © 2023, Chinese Pharmaceutical Association. All rights reserved.
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PurposeThe purpose of this paper is to explain how aspects of institutional theory can be used to explain how tertiary educational providers must necessarily adopt the same features while competing in the market for students. Using new institutional sociology (NIS), the authors seek to explain this phenomena and how such diverse private providers have borrowed from the polytechnics and universities to share the same institutional features.Design/methodology/approachThis paper employs NIS to explain how tertiary educational providers must necessarily adopt the same features while competing in the market for students. In the last few decades, the Auckland tertiary market for overseas students has enjoyed strong growth before Covid-19.FindingsDecoupling provides a link between NIS and legitimacy theory in that it explains how symbolic strategies are aimed at some kind of legitimization rather than directed at technical efficiencies. It is argued that the public tertiary providers are more prone to decoupling than PTEPs because they are less attentive to costs due to their more monopolistic position in the market.Originality/valueOriginally, the market was dominated by the local universities and polytechnics with few private providers. Currently, private providers cater for a large share of the certificate and diploma market and have a growing stake in the degree market. As these changes came about so, more diverse private providers entered the market. This paper explains how these diverse entrants came to share the same organizational features.
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As cruise ships call at many ports and passengers come from all over the world, it is very easy to carry viruses on cruise ships. Under the control of the epidemic situation on board, the solid waste generated by them should be scientifically treated to prevent the spread of infectious diseases such as COVID-19 pneumonia. Therefore, Reasonable selection of waste disposal ports and formulation of unloading plans are directly related to the resumption of cruise operations. This study considers the cost and risk of waste disposal, uses robust optimization to deal with waste volume, increases the scenarios of port service interruption due to epidemics and other reasons, and proposes a variety of emergency strategies. Finally, the relevant strategies are selected according to the decision-maker's preference for cost and risk;By solving the relevant examples, the optimal choice of the cruise ship waste disposal port under the epidemic situation is given, which verifies the validity and feasibility of the model. The research helps to improve the management of cruise waste during the post-epidemic period, and has practical value and guiding significance for the normal operation and development of the global cruise market. © 2023 SPIE.
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Case Report: Since the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic, there has been much work to understand the negative effects of SARS-CoV-2 on tissues expressing the Angiotensin Converting Enzyme-2 (ACE2) receptor, including the placenta. However, there is limited information regarding placental pathology findings in mothers with COVID-19 and the effects of SARS-CoV-2 on the placenta. The available research reports effects on the fetus ranging from minimal to intrauterine fetal demise. Case Description: A 4680g baby boy was born at 38+1 weeks of gestation to 36y old G4P1021 female via repeat cesarian section. The pregnancy was complicated by advanced maternal age, chronic hypertension with superimposed pre-eclampsia with severe features, BMI of 80, and SARS-CoV-2 infection. The mother had mild COVID-19 symptoms and did not require hospitalization or oxygen support. Prenatal ultrasounds were limited due to body habitus. At the time of delivery, there was clear amniotic fluid. Upon delivery the infant was cyanotic and limp and was brought to the warmer immediately. Non-invasive positive pressure ventilation was initiated at 5 minutes of life with improvement in infant color and oxygen saturation. He was then admitted to the Neonatal Intensive Care Unit (NICU). APGARs were 2, 3, 5, and 7 at 1, 5, 10, and 15 minutes respectively. Cord gases showed severe metabolic acidosis. The patient was diagnosed with hypoxic-ischemic encephalopathy (HIE) and therapeutic hypothermia was initiated. Both the NICU and obstetric teams were unable to identify a clear perinatal cause of HIE in this patient. Later, the placenta pathology report revealed a large placenta for estimated gestational age corresponding to the 75th percentile, villous parenchyma with focal chorangiosis and thrombi, with unremarkable fetal membrane and three vessel umbilical cord. The cause of HIE was then thought to be due to the placental thrombi likely caused by SARS-CoV-2 infection. Discussion(s): Fetal vascular malperfusion and fetal vascular thrombus have been noted as a common finding in the placentas of pregnant women who test positive for SARS-CoV-2. There are various causes of HIE, from maternal, placental and fetal factors. This patient had no clinically evident hypoxic event, but information was limited due to the lack of monitoring of the fetus in utero. Given the mother's SARS-CoV-2 infection and the placental pathology findings, it is likely that the cause of this patient's HIE was related to the effects on the placenta from SARS-CoV-2. Conclusion(s): As more information comes to light about the effects of SARS-CoV-2 on the placenta, it is important to consider a maternal SARS-CoV-2 infection during pregnancy as a cause of HIE in a newborn.