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1.
Clinical Cancer Research ; 27(6 SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1816909

ABSTRACT

COVID-19 is leading to a global pandemic and invades human cells via ACE2. ACE2 was found to abundantly expressed in many organs and cells. However, there is no evidence about the potential risk of various types of cancer patients vulnerable to the infection of COVID-19. To obtain a risk map which indicating the novel coronavirus vulnerability of different types of cancer, so in this work we analyzed the RNA sequencing datasets of cancer patient. By interrogating the datasets, we not only identified the cancer types which vulnerable to COVID-19 attacks, but also we reported that variations in the mRNA expression level of ACE2 correlate to various prognosis phenomenon in different types of cancer cohorts and illustrated the underlying mechanism involved in may be related to lymphocytes infiltration. From these discoveries, we constructed an infection risk map which indicate the vulnerability of different types of cancer to COVID-19 infection, also elucidated the correlationship between ACE2 and the prognosis of cancer. We found that high ACE2 expression levels leading high risk of COVID-19 infection and poor prognosis of BRCA while better prognosis in OV patient cohorts. Moreover, our study demonstrated that this different pattern may correlate with the immune infiltration level. Note: This was not presented at the conference.

2.
IEEE Access ; 2022.
Article in English | Scopus | ID: covidwho-1794860

ABSTRACT

Under the severe situation of the COVID-19 pandemic, masks cover most of the effective facial features of users, and their head pose changes significantly in a complex environment, which makes the accuracy of head pose estimation in some systems such as safe driving systems and attention detection systems impossible to guarantee. To this end, we propose a powerful four-branch feature selective extraction network (FSEN) structure, in which three branches are used to extract three independent discriminative features of pose angles, and one branch is used to extract composite features corresponding to multiple pose angles. By reducing the dimension of high-dimensional features, our method significantly reduces the amount of computation while improving the estimation accuracy. Our convolution method is an improved spatial channel dynamic convolution (SCDC) that initially enhances the extracted features. Additionally, we embed a regional information exchange network (RIEN) after each convolutional layer in each branch to fully mine the potential semantic correlation between regions from multiple perspectives and learn and fuse this correlation to further enhance feature expression. Finally, we fuse the independent discriminative features of each pose angle and composite features from the three directions of channel, space, and pixel to obtain perfect feature expression for each pose angle, and then obtain the head pose angle. We conducted extensive experiments on the controlled environment datasets and a self-built real complex environment dataset (RCE) and the results showed that our method outperforms state-of-the-art single-modality methods and performs on par with multimodality-based methods. This shows that our network meets the requirements of accurate head-pose estimation in real complex environments such as complex illumination and partial occlusion. Author

3.
Journal of Xi'an Jiaotong University (Medical Sciences) ; 43(2):302-308, 2022.
Article in Chinese | EMBASE | ID: covidwho-1791917

ABSTRACT

Objective: This paper screened the factors that may influence the spatial differentiation of Neutrophil-to-lymphocyte ratio (NLR) reference values in healthy adults in China and explored the trend of NLR reference values in China. Methods: For this research, we collected the NLR of 162 681 healthy adults from 62 cities in China. Spearman regression analysis was used to analyze the correlation between NLR and 25 geography secondary indexes. We extracted 9 indexes with significant correlation, built a random forest (RF) model, and predicted the country's urban healthy adults' NLR reference value. By using the disjunctive Kriging method, we obtained the geographical distribution of NLR reference value of healthy adults in China. Results: The reference value of NLR of healthy adults in China was significantly correlated with the 9 secondary indexes, namely, altitude, sunshine duration, annual average temperature, annual average relative humidity, annual temperature range, annual average wind speed, content of organic matter in topsoil, cation exchange capacity in topsoil (clay), and total amount of CaSO4 in soil. The geographical distribution of NLR values of healthy adults in China showed a trend of being higher in Southeast China and lower in Northwest China, higher in coastal areas and lower in inland areas. Conclusion: This study lays a foundation for further research on the mechanism of different influencing factors on the reference value of NLR index. A random forest model composed of significant influencing factors has been established to provide the basis for formulating reference criteria for the prognostic factors of the novel coronavirus using NLR reference values in different regions.

4.
Journal of Third Military Medical University ; 43(20):2241-2249, 2021.
Article in Chinese | Scopus | ID: covidwho-1789737

ABSTRACT

Objective To describe the clinical characteristics of liver and kidney injuries and investigate its effect on the severity and mortality in the COVID-19 patients.Methods A total of 3 548 patients diagnosed with COVID-19 hut without liver and kidney diseases admitted in the Huoshenshan Hospital, Jinyintan Hospital and Taikang Tongji Hospital from February 4, 2020 to April 16, 2020 were recruited in this study.Their clinical data were extracted from medical database, including general information, clinical features, laboratory results and outcomes such as death were collected and analyzed.SPSS statistics 23.0 was used to perform the statistical description and analysis.Results Among the 3 548 patients with COYID-19, 875 (24.7%) cases were severe illness and above and 91 (2.6%) died during hospitalization.The proportions of the patients with higher alanine amiotransferase ( ALT) , aspartate aminotransferase ( AST) and creatinine (Cr) were 14.6% (513/3 548) , 3.4% ( 1 19/3 548) and 2.8% ( 101/3 548), respectively.Compared with the patients with normal ALT, AST and Cr, the patients with elevated ALT did not have a significantly increased risk of severe illness or death ( /-∗>().05) , and the risk of severe illness and death was significantly increased in those with elevated AST and Cr ( P<0.05).The risk of severe disease was 2.32 times (95%CI: 1.73-3.10) and 1 1.40 times ( 95% CI: 2.36-54.98 ) for those with single or both liver and kidney injuries, and the risk of death was 5.21 times (95% CI: 3.10-8.75 ) and 13.53 times (95% CI: 2.76-66.32) for those with normal liver and kidney function, respectively.Logistic regression analysis indicated that after independent factors related to severe illness and death screened out as correction factors, the risk of severe illness and death was 1.612 times (95% CI: 1.17-2.22) and 2.907 times (95% CI: 1.61-5.24) of patients with liver or kidney injuries when compared with those with normal function, respectively.Conclusion The COYID-19 patients with liver and renal injuries have a significantly increased tendency to become severity and mortality, and should undergo early intervention. © 2021 Editorial Office of Journal of Third Military Medical University. All rights reserved.

5.
24th IEEE International Conference on Computational Science and Engineering, CSE 2021 ; : 51-56, 2021.
Article in English | Scopus | ID: covidwho-1788642

ABSTRACT

With the rapid development of the COVID-19 epidemic, people are prone to panic due to delayed and incomplete information received. In order to quickly identify the sentiments of massive Internet users, it provides a good reference for government agencies to formulate healthy public opinion guidance strategies. This paper proposes a novel sentiment classification based on 'word-phrase' attention mechanism (SC-WPAtt). On the basis of TCN, we propose a shallow feature extraction model based on the word attention mechanism, and a deep extraction model based on the phrase attention mechanism. These models can effectively mine the auxiliary information contained in words, phrases (i.e. combined words) and overall comments, as well as their different contributions, so as to achieve more accurate emotion classification. Experiments show that the performance of the SC-WPAtt method proposed in this paper is better than that of the HN-Att method. © 2021 IEEE.

6.
2021 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2021 ; : 241-245, 2021.
Article in English | Scopus | ID: covidwho-1784492

ABSTRACT

Passenger flow at a new high-speed railway station presents significant uncertainty during COVID-19, which brings a huge challenge to the daily management and operation of the station. To detect the future development trend of demand and reduce the impact of its fluctuation on the daily operation of the station, three classical forecast methods are applied to predict the passenger flow in and out of the station during workdays in this paper. Furthermore, the performance of these methods is compared by conducting a case study of Huairou South Station. The results show that the ARIMA model (autoregressive integrated moving average model) shows better performance than the neural network model and Bass model (Bass diffusion model). Finally, a revised ARIMA model is introduced to predict the passenger flow of the National Day. © 2021 IEEE.

7.
Cancer Research ; 82(4 SUPPL), 2022.
Article in English | EMBASE | ID: covidwho-1779478

ABSTRACT

Background: Infection with SARS-CoV-2 has led to a global pandemic and has significantly impacted the care of cancer patients. Breast cancer patients receiving active systemic therapy need protection against COVID19 but the efficacy of vaccines in this population is unknown. Although specific biomarkers associated with protection from SARS-CoV-2 infection have yet to be identified, measurement of serum antibody activity is generally accepted as a surrogate of in vivo humoral response to vaccine. This study evaluates the efficiency and durability of binding antibodies to SARS-CoV-2 spike (S) protein in response to COVID19 vaccine in breast cancer patients receiving systemic treatment. Methods: Breast cancer patients, who were unvaccinated, partially or fully vaccinated with Pfizer-BioNTech BNT162b2 (PF), Moderna mRNA-1273 (Mod) or Johnson & Johnson AD26.COV2.S (J&J) were enrolled in this prospective longitudinal study. Eligible patients were on systemic treatment with cytotoxic chemotherapy, chemotherapy plus a checkpoint inhibitor (CPI), CPI alone or a CDK 4/6 inhibitor. Longitudinal blood samples are being collected at baseline, prior to vaccination in unvaccinated patients (T0), 2 weeks after the first vaccine dose and before Sthe second dose for the mRNA vaccines (T1), 1 month (T2), 3 months (T3), 6 months (T4) and 12 month post vaccination. For J&J, there was no T1 timepoint. Roche Elecsys® Anti-SARS-CoV-2 S receptor binding domain (RBD) antibody immunoassay was used to measure antibody titers (range 0.4 to 250 U/mL). Cut points of <0.8 U/mL = negative, ≥0.8 U/mL = seropositive, were based on validated product specifications. Results: Of the 84 breast cancer patients enrolled, 9 had documented COVID infection at baseline and were excluded from analysis. Mean age was 58 years;99% were female, 85% were Caucasian, 49% had early stage disease and 51% had metastatic breast cancer. 67% were receiving cytotoxic chemotherapy, 20% a CKD 4/6 inhibitor, 13% a CPI with or without chemotherapy. 61.2% were vaccinated with PF, 34.3% with Mod and 4.5% with J&J vaccines. Seropositivity rate for the entire group was 10% at T0, 78% at T1, 98% at T2 and 100% at T3. Seropositivity rates of all cohorts at different timepoints are shown in the table. Mean titers for all patients were 12.6 U/mL at T0, 102.3 U/mL at T1, 204.4 U/mL at T2 and 214.6 U/mL at T3 timepoints. Similar incremental increase in antibody levels was observed in all cohorts (Table). Conclusions: 78% of the patients with breast cancer on active systemic treatment were seropositive after the first dose of COVID19 vaccine and 98% after the second dose. The antibody response was maintained at 3 months, with 100% seropositivity rate. 6-month antibody response will be available at the time of presentation. Durability of antibody response at 6 and 12 months will help determine the timing of additional vaccine booster doses in this population. Importantly, this study has found that active treatment with chemotherapy, immunotherapy or CDK4/6 inhibitor therapy does not impact antibody response to SARS-CoV-2 vaccination in patients with breast cancer. Table: Seropositivity rate and mean Anti-S protein antibody levels by cohort at each time point. T0= baseline, T1=after first vaccine dose (mRNA vaccines), T2= 4 weeks after 2 doses of mRNA vaccine or after single dose of J&J vaccine, T3=3 months after the first dose of vaccine.

8.
2nd International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2021 ; : 139-143, 2021.
Article in English | Scopus | ID: covidwho-1774656

ABSTRACT

As the COVID-19 becomes more prevalent worldwide, many countries take various measures to minimize the spread of infection. Since the main route of transmission of covid-19 is through the air, an analysis of transport, particularly global air traffic nodes, will provide a visual representation of the impact of covid-19 on the worldwide air transport industry. This paper uses publicly available aviation data to model the network and analyze the topology of the world aviation network before and after the COVID-19 outbreak in 2019 and 2020 to analyze the impact of the covid-19 epidemic on the world's aviation industry. First, we successfully visualized the significance of the change in the number of flight routes before and after the outbreak and the different distribution in each region by modeling the worldwide airline traffic network. Then, after a series of analyses and investigations. Second, we collected open-source data showing that the overall number of flights worldwide has been downward following the COVID-19 outbreak. Based on this information, we have chosen to conduct specific studies of countries and regions where there have been significant changes since the outbreak of covid-19, combined with reasonable hypotheses and analysis of local traffic control policies, and deduced that covid-19 had affected people's lives more from a policy rather than a medical perspective. Finally, we built up visual analysis images and tables to base our research using open-source aviation data sites such as open-fight. The results show that the analyzed aviation networks exhibit small-world characteristics, with the total number of flights not changing significantly due to the outbreak. However, the number of routes to the most crucial airport nodes worldwide decreases, and centrality diminishes, and the number of direct flights reductions and the increase of connecting flights. © 2021 IEEE.

9.
Embase; 2021.
Preprint in English | EMBASE | ID: ppcovidwho-331185

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of novel corona virus disease (COVID-19). The neutralizing monoclonal antibodies (mAbs) targeting the receptor binding domain (RBD) of SARS-CoV-2 are among the most promising strategies to prevent and treat COVID-19. However, SARS-CoV-2 variants of concern (VOCs) profoundly reduced the efficacies of most of mAbs and vaccines approved for clinical use. Herein, we demonstrated mAb 35B5 efficiently neutralizes both wild-type (WT) SARS-CoV-2 and VOCs, including B.1.617.2 (delta) variant, in vitro and in vivo. Cryo-electron microscopy (cryo-EM) revealed that 35B5 neutralizes SARS-CoV-2 by targeting a unique epitope that avoids the prevailing mutation sites on RBD identified in circulating VOCs, providing the molecular basis for its pan-neutralizing efficacy. The 35B5-binding epitope could also be exploited for the rational design of a universal SARS-CoV-2 vaccine.

10.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329764

ABSTRACT

The COVID-19 pandemic heightened public awareness about airborne particulate matter (PM) due to the spread of infectious diseases via aerosols. The persistence of potentially infectious aerosol in public spaces, particularly medical settings, deserves close investigation;however, approaches for rapidly parameterizing the temporospatial distribution of particles released by an infected individual have not been reported in literature. This paper presents a methodology for mapping the movement of aerosol plumes using a network of low-cost PM sensors in ICUs. Mimicking aerosol generation by a patient, we tracked aerosolized NaCl particles functioning as tracers for potentially infectious aerosols. In positive (closed door) and neutral-pressure (open door) ICUs, an aerosol spike was detected outside the room, with up to 6% or 19% of all PM escaping through the door gaps, respectively. The outside sensors registered no aerosol spike in negative-pressure ICUs. The K-means clustering analysis of temporospatial data suggests three distinct zones: (1) near the aerosol source, (2) room periphery, and (3) immediately outside the room. These zones inform two-phase aerosol plume behavior: dispersion of the original aerosol spike throughout the room, and evacuation phase where “well-mixed” PM decayed uniformly. Decay rates were calculated for 4 ICUs in positive, neutral, and negative mode, with negative modes decaying the fastest. This research demonstrates the methodology for aerosol persistence monitoring in medical settings;however, it is limited by a relatively small data set. Future studies need to evaluate medical settings with high risks of infectious disease, assess risks of airborne disease transmission, and optimize hospital infrastructure. Significance Statement Airborne infectious diseases, including COVID-19, are a major concern for patients and hospital staff. Here, we develop a systematic methodology for mapping the temporospatial distribution of aerosols in ICUs using a network of low-cost particulate matter (PM) sensors. Our method of analysis provides a perspective on the exfiltration efficiency of ICUs as well as the benefits of rooms that have a negative-pressure mode. Our methods could be extended to other public spaces with a high risk of infectious disease to optimize infrastructure and assess the risk of airborne disease.

11.
Vaccines ; 10(2):30, 2022.
Article in English | Web of Science | ID: covidwho-1734771

ABSTRACT

"Bugs as drugs" in medicine encompasses the use of microbes to enhance the efficacy of vaccination, such as the delivery of vaccines by Leishmania-the protozoan etiological agent of leishmaniasis. This novel approach is appraised in light of the successful development of vaccines for Covid-19. All relevant aspects of this pandemic are summarized to provide the necessary framework in contrast to leishmaniasis. The presentation is in a side-by-side matching format with particular emphasis on vaccines. The comparative approach makes it possible to highlight the timeframe of the vaccine workflows condensed by the caveats of pandemic urgency and, at the same time, provides the background of Leishmania behind its use as a vaccine carrier. Previous studies in support of the latter are summarized as follows. Leishmaniasis confers life-long immunity on patients after cure, suggesting the effective vaccination is achievable with whole-cell Leishmania. A new strategy was developed to inactivate these cells in vitro, rendering them non-viable, hence non-disease causing, albeit retaining their immunogenicity and adjuvanticity. This was achieved by installing a dual suicidal mechanism in Leishmania for singlet oxygen (O-1(2))-initiated inactivation. In vitro cultured Leishmania were genetically engineered for cytosolic accumulation of UV-sensitive uroporphyrin I and further loaded endosomally with a red light-sensitive cationic phthalocyanine. Exposing these doubly dye-loaded Leishmania to light triggers intracellular production of highly reactive but extremely short-lived O-1(2), resulting in their rapid and complete inactivation. Immunization of susceptible animals with such inactivated Leishmania elicited immunity to protect them against experimental leishmaniasis. Significantly, the inactivated Leishmania was shown to effectively deliver transgenically add-on ovalbumin (OVA) to antigen-presenting cells (APC), wherein OVA epitopes were processed appropriately for presentation with MHC molecules to activate epitope-specific CD8+ T cells. Application of this approach to deliver cancer vaccine candidates, e.g., enolase-1, was shown to suppress tumor development in mouse models. A similar approach is predicted to elicit lasting immunity against infectious diseases, including complementation of the spike protein-based vaccines in use for COVID-19. This pandemic is devastating, but brings to light the necessity of considering many facets of the disease in developing vaccination programs. Closer collaboration is essential among those in diverse disciplinary areas to provide the roadmap toward greater success in the future. Highlighted herein are several specific issues of vaccinology and new approaches worthy of consideration due to the pandemic.

12.
Embase;
Preprint in English | EMBASE | ID: ppcovidwho-326627

ABSTRACT

The spike (S) protein of SARS-CoV-2 has been observed in three distinct pre-fusion conformations: locked, closed and open. Of these, the function of the locked conformation remains poorly understood. Here we engineered a SARS-CoV-2 S protein construct “S-R/x3” to arrest SARS-CoV-2 spikes in the locked conformation by a disulfide bond. Using this construct we determined high-resolution structures confirming that the x3 disulfide bond has the ability to stabilize the otherwise transient locked conformations. Structural analyses reveal that wild-type SARS-CoV-2 spike can adopt two distinct locked-1 and locked-2 conformations. For the D614G spike, based on which all variants of concern were evolved, only the locked-2 conformation was observed. Analysis of the structures suggests that rigidified domain D in the locked conformations interacts with the hinge to domain C and thereby restrains RBD movement. Structural change in domain D correlates with spike conformational change. We propose that the locked-1 and locked-2 conformations of S are present in the acidic high-lipid cellular compartments during virus assembly and egress. In this model, release of the virion into the neutral pH extracellular space would favour transition to the closed or open conformations. The dynamics of this transition can be altered by mutations that modulate domain D structure, as is the case for the D614G mutation, leading to changes in viral fitness. The S-R/x3 construct provides a tool for the further structural and functional characterization of the locked conformations of S, as well as how sequence changes might alter S assembly and regulation of receptor binding domain dynamics.

13.
Acm Transactions on Multimedia Computing Communications and Applications ; 17(3):18, 2021.
Article in English | Web of Science | ID: covidwho-1622095

ABSTRACT

With the rapid development of Artificial Intelligence (AI), deep learning has increasingly become a research hotspot in various fields, such as medical image classification. Traditional deep learning models use Bilinear Interpolation when processing classification tasks of multi-size medical image dataset, which will cause the loss of information of the image, and then affect the classification effect. In response to this problem, this work proposes a solution for an adaptive size deep learning model. First, according to the characteristics of the multi-size medical image dataset, the optimal size set module is proposed in combination with the unpooling process. Next, an adaptive deep learning model module is proposed based on the existing deep learning model. Then, the model is fused with the size fine-tuning module used to process multi-size medical images to obtain a solution of the adaptive size deep learning model. Finally, the proposed solution model is applied to the pneumonia CT medical image dataset. Through experiments, it can be seen that the model has strong robustness, and the classification effect is improved by about 4% compared with traditional algorithms.

14.
American Journal of Translational Research ; 13(12):14157-14167, 2021.
Article in English | EMBASE | ID: covidwho-1610152

ABSTRACT

Background: Previous studies have unveiled the occurrence of re-detectable positive (RP) RNA test result after hospital discharge among recovered COVID-19 patients, but the clinical characteristics of RP patients (RP patients) and the potential features affecting RP RNA test outcome remain unclear. Methods: A total of 742 COVID-19 patients discharged between March 1st, 2020 and March 20th, 2020 were enrolled. All patients were followed-up for SARS-CoV-2 RNA test and RP patents were identified. The clinical characteristics between RP patients and NRP patients were compared, and the potential features affecting re-detectable RNA test outcome were further evaluated. Results: Up to April 9th, 2020, 60 recovered patients (8.09%) had been re-detected to be SARS-CoV-2 RNA positive. Among those 60 RP patients, the median RP time was 12 days from the last negative result of SARS-CoV-2 RNA test or 10 days from hospital discharge. RP patients were prone to be older, having mild/moderate conditions, unilateral lung involvement and fatigue, chills, stuffy or runny nose, with high lymphocyte count. Multivariate logistic analysis and COX regression analysis demonstrated that age, lymphocyte count, urea nitrogen, stuffy or runny nose as well as lung involvement were independently associated with RP RNA test (P<0.05). Conclusions: Older patients accompanied with stuffy or runny nose, low urea nitrogen as well as unilateral lung involvement were more likely to develop RP RNA test result after hospital discharge. Therefore, we strongly suggest using broncho-alveolar lavage fluid for RNA detection, extending quarantine time, and conducting continual follow-up medical examination for those discharged patients.

15.
Journal of Geophysical Research. Atmospheres ; 126(24), 2021.
Article in English | ProQuest Central | ID: covidwho-1595324

ABSTRACT

Nitrogen oxides (NOx) are air pollutants critical to ozone and fine particle production in the troposphere. Here, we present fuel‐based emission inventories updated to 2018, including for mobile source engines using the Fuel‐based Inventory of Vehicle Emissions (FIVEs) and oil and gas production using the Fuel‐based Oil and Gas (FOG) inventory. The updated FIVE emissions are now consistent with the NEI17 estimates differing within 2% across the contiguous US (CONUS). Tropospheric NO2 columns modeled by the Weather Research and Forecasting with Chemistry model (WRF‐Chem) are compared with those observed by TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) during the summer of 2018. Modeled NO2 columns show strong temporal and spatial correlations with TROPOMI (OMI), identified with biases of −3% (−21%) over CONUS, and +8% (−6%) over point sources plus urban regions. Taking account of the negative bias (∼20%) in early version of TROPOMI over polluted regions, WRF‐Chem shows good performance with updated FIVE and FOG emissions. Our model tends to under‐predict the tropospheric NO2 columns over background and rural regions (bias of −21% to −3%). Through model sensitivity analyses, we demonstrate the important roles of emissions from soils (11.7% average over CONUS), oil and gas production (4.1%), wildfires (10.6%), and lightning (2.3%) with greater contributions at regional scales. This work provides a roadmap for satellite‐based evaluations for emission updates from various sources.Alternate :Plain Language SummarySatellite observations of tropospheric NO2 columns provide important constraints on air pollutants from space, which have been widely used to validate the performance of atmospheric models. To gain better knowledge of the accuracy of the recently updated fuel‐based emissions inventory, we conducted NO2 assessments between a regional chemical transport model (Weather Research and Forecasting with Chemistry model, WRF‐Chem), with the TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) over the contiguous United States. We find that model simulation results show strong spatial and temporal correlations with satellite observations across point sources, urban, oil and gas production, and rural regions. With updated emissions, our regional atmospheric model can reconcile with satellite retrievals differing from −3% (TROPOMI) to −21% (OMI) overall. Soils, oil and gas production, wildfires and lightning emissions can play key roles in regional air quality. This work provides an important baseline of a pre‐COVID year by which sharp changes in anthropogenic NOx emissions due to the pandemic can be assessed.

16.
18th International Conference on Scientometrics and Informetrics (ISSI) ; : 511-516, 2021.
Article in English | Web of Science | ID: covidwho-1498703

ABSTRACT

This research-in-progress paper seeks to understand how the pandemic affected international research collaboration different countries and regions. It collected 333,793 preprints submitted to ArXiv between 2019 and 2020 to compare international research collaboration patterns pre-COVID-19 and COVID-19 eras. The paper finds that international research collaboration has been substantially affected by the pandemic, but the impact is manifested in varied extent over different time periods and in different countries. The project observed 1.55% decrease in international research collaboration in 2020 as compared with 2019. More specifically, there was a significant drop of international research collaboration at the early stage of the pandemic (from January 2020 until May 2020), and a sturdy recovery (to pre-COVID time) after May 2020. The change pattern varies by discipline and country. The results also demonstrate the resilience and adaptiveness of the scientific community in maintaining international research collaboration.

17.
Journal of the American Society of Nephrology ; 32:57, 2021.
Article in English | EMBASE | ID: covidwho-1490297

ABSTRACT

Background: The coronavirus SARS-CoV-2 is the culprit of the COVID-19 pandemic. Acute kidney injury occurs frequently in COVID-19 patients and several lines of evidence suggest local infection of kidney cells by the virus. However, this remains controversial and it is unclear how the viral proteins of SARS-CoV-2 directly impact the health of renal tubular cells infected by the virus. Methods: The viral protein ORF3A of SARS-CoV-2 was overexpressed in HK-2 renal tubular cell line and the pronephric tubule epithelia of transgenic zebrafish. The NF-kB and STAT3 signaling pathways and target gene expression were analyzed using quantitative RT-PCR and Western blots. The expression of the renal injury marker KIM-1 was also assessed by Western blots, quantitative RT-PCR and in situ hybridization. Protein interactions were studied by co-immunoprecipitation and Western blots. Results: ORF3A augments both NF-kB and STAT3 signaling by enhancing the phosphorylation of the transcription factors and results in the expression of downstream target genes and subsequently increases the expression of kidney injury molecule 1 (KIM-1) in HK-2 cells. Mechanistically, ORF3A elevates the expression of Tripartite Motif-Containing Protein 59 (TRIM59), a ubiquitin E3 ligase, which forms a protein complex with ORF3A and STAT3. This in turn excludes the phosphatase TCPIP from binding to STAT3 and inhibits the dephosphorylation of STAT3. The transgenic zebrafish expressing ORF3A in renal tubular epithelia develop severe edema starting 48 hours post fertilization and in situ hybridization shows elevated kim-1 expression in the pronephric tubules, indicating that ORF3A induces renal injury in zebrafish in vivo. Conclusions: These results demonstrate that overexpression of ORF3A is sufficient to injure renal tubular epithelial cells and uncover a previously unrecognized molecular mechanism underlying the deregulation of STAT3 activity by ORF3A that leads to renal tubular cell injury. Altogether, the results of this study support the notion that direct infection of renal epithelial cells by SARS-CoV-2 may contribute to the renal complications in COVID-19 patients.

18.
Journal of the American Society of Nephrology ; 32:735, 2021.
Article in English | EMBASE | ID: covidwho-1490055

ABSTRACT

Background: Adherence is critical in chronic kidney disease (CKD) to delay progression to kidney failure. Treatment plans for CKD can include medications, diet, and exercise. Overall adherence to treatment is low in CKD, and also as few as 40% of new kidney failure patients have any documented CKD-related care. The purpose of this study was to explore CKD patients' experiences of adherence to treatment plans and what role their healthcare providers had in supporting adherence. Methods: As part of a larger mixed-methods study of Chronic Renal Insufficiency Cohort (CRIC) study participants, a subset was randomly selected for 1:1 interviews. All CRIC participants are >45 years with CKD stages 1-4, and this sample consisted of University of Pennsylvania participants interviewed in 2019-2020. Participants described their experiences with adherence and what they have done when experiencing difficulty. Interviews were recorded, transcribed, and coded using conventional content analysis. Results: The sample (n=32) had a mean age of 67 years, 53% women, 59% nonwhite. After analysis of factors relevant to treatment planning and adherence, four themes emerged: patient factors (multiple chronic conditions, motivation, outlook), provider factors (attentiveness, availability, communication), treatment planning factors (lack of plan, proactive patient research, provider-focused goals, and shared decision making), and patient responses to the treatment plan (disagreeing with treatment, frustration with their lack of adherence [I know what to do], lack of information, and positive feedback). Patients also described the impact of COVID on access to care and the positive impact of family, ancillary providers, and routines/habits. Conclusions: These themes align with behavioral learning theory, which includes: internal antecedents (patient factors), external antecedents (provider factors), behavior (treatment planning and attempts at adherence), and consequences (adherence and responses to the treatment plan). Our results provide many potential points of intervention to support treatment adherence in CKD, and a tailored approach is needed to address patients' specific adherence factors.

19.
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) ; 2020.
Article in English | Web of Science | ID: covidwho-1485912

ABSTRACT

The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0;if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods.

20.
Cardiovascular Innovations and Applications ; 6(1):25-32, 2021.
Article in English | EMBASE | ID: covidwho-1458497

ABSTRACT

Aims: During the COVID-19 epidemic, chest computed tomography (CT) has been highly recommended for screening of patients with suspected COVID-19 because of an unclear contact history, overlapping clinical features, and an overwhelmed health system. However, there has not been a full comparison of CT for diagnosis of heart failure or COVID-19 pneumonia. Methods: Patients with heart failure (n = 23) or COVID-19 pneumonia (n = 23) and one patient with both diseases were retrospectively enrolled. Clinical information and chest CT images were obtained and analyzed. Results: There was no difference in ground-glass opacity, consolidation, crazy paving pattern, the lobes affected, and septal thickening between heart failure and COVID-19 pneumonia. However, a less rounded morphology (4% vs. 70%, P = 0.00092), more peribronchovascular thickening (70% vs. 35%, P = 0.018) and fissural thickening (43% vs. 4%, P = 0.002), and less peripheral distribution (30% vs. 87%, P = 0.00085) were found in the heart failure group than in the COVID-19 group. Importantly, there were also more patients with upper pulmonary vein enlargement (61% vs. 4%, P = 0.00087), subpleural effusion (50% vs. 0%, P = 0.00058), and cardiac enlargement (61% vs. 4%, P = 0.00075) in the heart failure group than in the COVID-19 group. Besides, more fibrous lesions were found in the COVID-19 group, although there was no statistical difference (22% vs. 4%, P = 0.080). Conclusions: Although there is some overlap of CT features between heart failure and COVID-19, CT is still a useful tool for differentiating COVID-19 pneumonia.

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