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Vaccination uptake has become the key factor that will determine our success in containing the coronavirus pneumonia (COVID-19) pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel COVID-19 pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can predistribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and, hence, accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts and, hence, contribute to accelerating the achievement of herd immunity.
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Lateral flow immunoassay (LFIA) is one of the most common analytical platforms for point-of-care testing (POCT), which is capable of large-scale primary screening and home self-testing of infectious diseases. However, the sensitivity of conventional AuNPs-based LFIA is relatively low and more prone to false negatives. Herein, we report a novel LFIA based on gold-core-silver-shell bimetallic nanoparticles (Au4-ATP@Ag NPs) emitting Surface-enhanced Raman scatting (SERS) and Photothermal (PT) effect, named SERS/PT-based dual-modal LFIA (SERS/PT-dmLFIA), for the antigen detection of infectious diseases pathogens, which displayed an excellent performance. For influenza A virus (IAV), influenza B virus (IBV), and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) N protein detection, the limit of detections (LoD) with Raman as signal were 31.25, 93.75, and 31.25 pg mL-1 respectively, and the LoDs with temperature difference (∆T) as signal were as low as 15.63, 187.5, and 15.63 pg mL-1 respectively, which were over 4-fold more sensitive than visual-based LFIA. The proposed SERS/PT-dmLFIA was used for detecting virus antigen in pharyngeal swabs and showed ideal coincidence rate of over 95% compared to the commercialized assays. In addition, we explored the development of multiplex SERS/PT-dmLFIA that can detect IAV, IBV, and SARS-CoV-2 antigens simultaneously without cross reactivity. Overall, the SERS/PT-dmLFIA for antigen detection not only exhibits high sensitivity, accuracy and specificity, but also have characteristics of rapidity and simplicity, which holds high potential for rapid diagnosis of infectious diseases in laboratory testing, mass screening, and home self-testing. © 2023 Elsevier B.V.
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Due to the coronavirus pandemic, the non-touch personal signature can reduce the risk of infection to a certain extent, which is of great significance to our daily life. Therefore, a simple and efficient spatiotemporal fusion network was proposed to realize skeleton-based dynamic hand gesture recognition, based on which a virtual signature system was developed. The spatiotemporal fusion network is mainly composed of spatiotemporal fusion modules based on the attention mechanism, and its key idea is to synchronously realize the extraction and fusion of spatiotemporal features using an incremental method. This network adopts different spatiotemporal coding features as inputs, and employs the double sliding window mechanism for post-processing in practical applications, thus ensuring more stable and robust results. Extensive comparative experiments on two benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art single-stream network. Besides, the virtual signature system performs well with a single normal RGB camera, which not only greatly reduces the complexity of the interaction system, but also provides a more convenient and secure approach to personal signature. © 2022, Editorial of Board of Journal of Graphics. All rights reserved.
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Background: Comprehensive real-world evidence of the virulence of COVID-19 Omicron, Delta, and Alpha variants as well as the effectiveness of booster vaccinations in patients with cancer are lacking. We aimed to fill in these gaps for cancer patients and provide essential insights on the management of the fast-evolving pandemic by leveraging the nationally-representative electronic medical records from the National COVID Cohort Collaborative (N3C) registry. Methods: The virulence of COVID-19 variants was examined according to severe outcomes of infected patients with cancer, compared with non-cancer patients, using the N3C data between 12/01/2020 and 02/03/2022. Variants were inferred according to the time periods of variant dominance at > 95% accuracy. The Cox proportional hazards model was employed to evaluate the effects of COVID-19 variants, adjusting for age, gender, race/ethnicity, geographic regions, vaccination status, cancer types, smoking status, cancer treatments, and adjusted Charlson Comorbidity Index (CCI). Results: Our study cohort included 114,195 COVID-19 patients with cancer and 160,493 without cancer as control. Among them, 52,539 (21%) were infected by Omicron, 82,579 (33%) by Delta, and 115,200 (46%) by Alpha variants. Prior to the COVID-19 breakthrough infection, 7%, 22%, 3%, and 69% were vaccinated with 1 dose, 2 doses, a booster, or unvaccinated respectively. The proportions of hospitalization and death among patients with vs without cancer were 40% and 7% vs 18% and 0.4%, respectively. Characteristics of the cancer subcohort are summarized in the Table. Our analysis showed dramatically lower risks of severe outcomes for patients who were infected by Omicron (HR 0.42, 95%CI: 0.38 - 0.46) and slightly lower risks for Delta (HR 0.93, 95%CI: 0.89 - 0.98) compared with those infected by Alpha, after adjusting for other demographic clinical risk factors, and vaccination status. This trend remained similar in subgroups of patients with solid tumors, hematologic malignancies, or without cancer. Similar associations were observed when virulence was evaluated in association with mortality. The effectiveness of booster vaccinations varied across sub-cohorts stratified by variants and cancer types. Booster shots reduced the risk of severe outcomes for patients with solid tumors infected by Omicron variant or hematologic malignancies infected by Delta variants. Conclusions: Our work provides up-to-date and comprehensive real-world evidence of the virulence of COVID-19 variants in patients with cancer. Omicron variant showed significantly reduced virulence for different cancer types.
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Background: Post-acute sequelae of SARS-CoV-2 or long COVID, is characterized by persistence of symptoms and/or emergence of new symptoms post COVID-19 infection. As evidence accumulates and national initiatives arise to address this increasingly prevalent syndrome, characterization of specific patient groups is still lacking including patients with cancer. Using a nationally representative sample of over 4.3M COVID-19 patients from the National COVID Cohort Collaborative (N3C), we aim to describe characteristics of patients with cancer and long COVID. Methods: We employed two approaches to identify long COVID patients within N3C: i) patients presenting to a long COVID clinic at four N3C sites and ii) patients diagnosed using the recently introduced ICD-10 code: U09.9 Post COVID- 19 condition, unspecified. We included patients with at least one positive COVID-19 diagnosis between 1/1/2020 and 2/3/2022. Patients had to survive at least 90 days from the date of their COVID- 19 diagnosis. Analyses were performed in the N3C Data Enclave on the Palantir platform. Results: A total of 1700 adult patients with long COVID were identified from the N3C cohort;634 (37.3%) were cancer patients and 1066 were non-cancer controls. The most common represented cancers were skin (21.9%), breast (17.7%), prostate (8.3%), lymphoma (8.0%) and leukemia (5.7%). Median age of long-COVID cancer patients was 64 years (Interquartile Range: 54-72), 48.6% were 65 years or older, 60.4% females, 76.8% non-Hispanic White, 12.3% were Black, and 3% Hispanic. A total of 41.1% were current or former smokers, 27.7% had an adjusted Charlson Comorbidity Index score of 0, 18.6% score of 1 and 11.2% score of 2. A total of 57.2% were hospitalized for their initial COVID-19 infection, the average length of stay in the hospital was 9.6 days (SD: 16.7 days), 9.1% required invasive ventilation, and 13% had acute kidney injury during hospitalization. The most common diagnosis among the non-cancer long COVID patients was asthma (26%), diabetes (17%), chronic kidney disease (12%), heart failure (9.4%), and chronic obstructive pulmonary disease (7.8%). Among long COVID patients, compared to non-cancer controls, cancer patients were more likely to be older (OR = 2.4, 95%CI: 1.1-5.4, p = 0.03), have comorbidities (OR = 4.3, 95%CI: 2.9-6.2, p < 0.0001), and to be hospitalized for COVID-19 (OR = 1.3, 95%CI: 1.0-1.7, p = 0.05), adjusting for sex, race/ethnicity, body mass index and smoking history. Conclusions: In a nationally representative sample of long COVID patients, there was a relative overrepresentation of patients with cancer. Compared to non-cancer controls, cancer patients were older, more likely to have more comorbidities and to be hospitalized for COVID-19 warranting further investigation to identify risk factors for long COVID in patients with cancer.
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Background: Patients with multiple myeloma (MM), an age-dependent neoplasm of antibody-producing plasma cells, have compromised immune systems due to multiple factors that may increase the risk of severe COVID-19. The NCATS' National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multi-center cohort of ∼12M COVID-19 cases and controls nationwide. In this study, we aim to analyze risk factors associated with COVID-19 severity and death in MM patients using the N3C database. Methods: Our cohort included MM patients within the N3C registry diagnosed with COVID-19 based on positive PCR or antigen tests or ICD-10-CM. The outcomes of interest include all-cause mortality (including discharge to hospice) during the index encounter, and clinical indicators of severity (hospitalization/ED visit, use of mechanical ventilation, or extracorporeal membrane oxygenation/ECMO). Results: As of 09/10/2021, the N3C registry included 690371 cancer patients, out of which 17791 were MM patients (4707 were COVID-19+). The mean age at diagnosis was 65.9yrs, 57.6% were >65yo, 46.4% were females, and 21.8% were Blacks. 25.6% had a Charlson Comorbidity Index (CCI) score of ≥2. 55.6% required an inpatient or ED visit, and 3.65% required invasive ventilation. 11.4% developed acute kidney injury during hospitalization. Multivariate logistic regression analysis showed histories of pulmonary disease (OR 2.2;95%CI: 1.7-2.8), renal disease (OR 1.8;95%CI: 1.4-2.4), and black race (p<0.001) were associated with higher risk of severity. Interestingly, smoking status was significantly associated with a lower risk of severity (OR 0.7;95%CI: 0.5-0.9). Further, protective association was also observed between COVID-19 severity and blood or marrow transplant (BMT) (OR 0.52;95%CI: 0.4-0.7), daratumumab therapy (OR 0.64;95%CI: 0.42- 0.99) and COVID-19 vaccination (OR 0.28;95%CI: 0.18-0.44). IMiDs were associated increase in the risk of COVID-19 severity (OR 2.1;95%CI: 1.6-2.7). 2.3% of N3C-myeloma COVID-19+ patients died within the first 10 days, while 4.95% died within 30 days of COVID-19 hospitalization. Overall, the survival probability was 90.5% across the course of the study. Multivariate cox proportional hazard model showed that CCI score ≥2 (HR 4.4;95%CI: 2.2-8.8), hypertension (HR 1.6;95%CI: 1.02- 2.4), IMiD (HR 2.6;95%CI: 1.8-3.8) and proteasome inhibitor (HR 1.6;95%CI: 1.1-2.5) therapy were associated with worse survival. COVID-19 vaccination (HR 0.195;95%CI: 0.09-0.45) and BMT (HR 0.65;95%CI: 0.4-0.995) were associated with lower risk of death. Conclusions: We have identified previously unpublished potential risk factors for COVID-19 severity and death in MM as well as validated some published ones. To the best of our knowledge, this is the largest nationwide study on multiple myeloma patients with COVID-19.
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Vaccination uptake has become the key factor that will determine our success in containing the COVID-19 pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel coronavirus pneumonia (COVID-19) pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely-scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can pre-distribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and hence accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts, and hence contribute to accelerating the achievement of herd immunity. IEEE
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Work-from-home (WFH) influences both work and life, and further impacts family relationships. The current study explored the impacts of WFH on family relationships during the COVID-19 pandemic and identified effective adaptive processes for maintaining family relationships under WFH. Using the Vulnerability-Stress-Adaptation (VSA) model, the study examined the roles of adaptive processes (spending time with family members and balancing work and life) and demographic differences (gender, age, marital status, and education level) in the relation between WFH and family relationships. Path analysis results based on an online survey (N = 150) suggested that, overall, WFH improved family relationships through proper adaptive processes. WFH had a positive relation to time spent with family members, and this relation was especially salient for workers with lower education levels. While there was no statistically significant overall relation between WFH and work-life balance, older workers tended to engage in increased work-life balance during WFH. Both adaptive processes were positively related to family relationship quality. The findings advance the understanding of family relationships and WFH and provide practical recommendations to enhance family relationships under WFH.
Subject(s)
COVID-19 , Family Relations , COVID-19/epidemiology , Family , Humans , Pandemics , TeleworkingABSTRACT
Historically, covalent drugs were avoided in drug development process due to the possible toxicity linked to the covalent binding of such drugs and cellular proteins. However, recent years have witnessed the fast resurgence of the discovery of covalent drugs because of the realization of the advantages of covalent drugs in efficacy, duration of action, therapy-induced resistance, and targeting hard targets. Since December 2019, SARS-CoV-2 has caused nearly 40 million COVID-19 patients and over one million deaths in the whole world as of October 19, 2020. In contrast, effective drugs have not been found at the same time. Therefore, it is of great value to discover and design drugs for the prevention and cure of SARS-CoV-2 infection. Recently, we developed a simple but efficient method named as SCARdock for the discovery of covalent drugs. In this work, we present a detailed protocol of this method in discovering potential anti-SARS-CoV-2 drugs.
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Background: The impact of COVID-19 has disproportionately affected every aspect of cancer care and research-from introducing new risks for patients to disrupting the delivery of treatment and continuity of research. Variation in risk of adverse clinical outcomes in COVID-19 patients by cancer type has been reported from relatively small cohorts. Gaps in understanding effects of COVID-19 on cancer patients can be addressed through the study of a well-constructed representative cohort. The NCATS' National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multi-center cohort of COVID-19 cases and controls nationwide. We aimed to construct and characterize the cohort of cancer patients within N3C and identify risk factors for all-cause mortality from COVID-19. Methods: From the harmonized N3C clinical dataset, we used 3,295,963 patients from 39 medical US centers to construct a cancer patient cohort. We restricted analyses to adults ≥18 yo with a COVID-19 positive PCR or antigen test or ICD-10-CM diagnostic code for COVID-19 between 1/1/2020 and 2/14/2021. We followed N3C definitions where each lab-confirmed positive patient has one single index encounter. A modified WHO Clinical Progression Scale was used to determine clinical severity. All analyses were performed in the N3C Data Enclave on the Palantir platform. Results: A total of 372,883 adult patients with cancer were identified from the N3C cohort;54,642 (14.7%) were COVID-19 positive. Most common represented cancers were skin (11.5%), breast (10.2%), prostate (8%), and lung cancer (5.6%). Mean age of COVID-19 positive patients was 61.6 years (SD 16.7), 47.3% over 65yo, 53.7% females, 67.2% non-Hispanic White, 21.0% Black, and 7.7% Hispanic or Latino. A total of 14.6% were current or former smokers, 22.3% had a Charlson Comorbidity Index (CCI) score of 0, 4.6% score of 1 and 28.1% score of 2. Among hospitalized COVID-19 positive patients, average length of stay in the hospital was 6 days (SD 23.1 days), 7.0% patients had died while in their initial COVID-19 hospitalization, 4.5% required invasive ventilation, and 0.1% extracorporeal membrane oxygenation. Survival probability was 86.4% at 10 days and 63.6% at 30 days. Older age over 65yo (Hazard ratio (HR) = 6.1, 95%CI: 4.3, 8.7), male gender (HR = 1.2, 95%CI: 1.1, 1.2), a CCI score of 2 or more (HR = 1.15, 95%CI: 1.1, 1.2), and acute kidney injury during hospitalization (HR = 1.3, 95%CI: 1.2, 1.4) were associated with increased risk of all-cause mortality. Conclusions: Using the N3C cohort we assembled the largest nationally representative cohort on patients with cancer and COVID-19 to date. We identified demographic and clinical factors associated with increased all-cause mortality in cancer patients. Full characterization of the cohort will provide further insights on the effects of COVID-19 on cancer outcomes and the ability to continue specific cancer treatments.
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This paper analyzes the market supply and demand of polyethylene from 2015 to 2019 in China. In 2019, the production capacity of polyethylene reached 19.27 Mt, the output was 15.76 Mt, the import volume was 16.67 Mt, and the consumption volume reached 32.14 Mt. China's new polyethylene production capacity is summarized and consumption is predicted from 2020 to 2024. The forecast results show that affected by COVID-19 at the beginning of 2020, China's polyethylene consumption will be about 32.39 Mt. However, with the orderly recovery of China's economy and the growth of national industrial support, polyethylene consumption will continue to grow rapidly in the future, and it is expected to reach 37.90 Mt by 2024. Although polyethylene market is large, local enterprises in China still face severe challenges such as product homogeneity, low-priced imported products, stricter environmental protection supervision, and plastic bans. In order to survive in the fiercely competitive market, Chinese enterprises must reduce product costs, develop customized services based on user needs, carry out more scientific research and development, and strengthen independent innovation. © 2021, Beijing Yanshan Branch, Assets Management Corporation, SINOPEC. All right reserved.
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The corona virus disease 2019 (COVID-19) caused by the new coronavirus (SARS-CoV-2) has spread rapidly around the world, posing a serious threat to the public's health. As of September 30, 2020, the number of infected people in the world has reached 33 million, causing more than 1 million deaths. Normalized nucleic acid detection methods based on lab have long turnaround time and high cost. Therefore, there is an urgent need to develop a convenient method to detect SARS-CoV-2, so as to achieve rapid testing and timely control of the epidemic when resources are limited. This review summarizes the point-of-care testing (POCT) methods developed for SARS-CoV-2 in terms of extraction, amplification and detection, and briefly introduces commercial POCT instruments that integrate these three steps, in order to provide references for emergency response and rapid deployment of COVID-19 and other emerging infectious diseases. © 2020 China Pharmaceutical University. All rights reserved.
Subject(s)
COVID-19/epidemiology , Pandemics , Thyroid Diseases/surgery , Adult , Female , Humans , Male , Risk Assessment , SARS-CoV-2 , Time-to-Treatment , Treatment OutcomeABSTRACT
Starting in December 2019, Wuhan, China, the first round of outbreak of coronavirus, which began to transmission rapidly in urban Wuhan and extended all around Hubei Province within a short period. The Chinese government took active anti-pandemic measures on occurrence of this regional pandemic. As Wuhan locked down, the number of confirmed cases dramatically reduced. However, the pandemic began to accelerate in other cities around the country and then globally, and the secondary infection has become the primary method of spreading of the pandemic. Curbing secondary infection of the pandemic in urban areas has become an essential goal for prevention and control of COVID-19. This paper uses the digital platform Rhino & Grasshopper to simulate the possible traces of urbanites' activities, and visually combines the most widely used virus transmission model such as SEIR, and proposes a new M-SEIR model to control transmission of urban pandemics. By exploring the relationship between the morphology of urbanites' activities flow and the development of the pandemic, this study will generate fresh insight into the design of public transportation, prevention and control of urban pandemics. © 2020 ACM.
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The new coronavirus SARS-CoV-2 has spread to the whole world, seriously threatening human life and disrupting people's lives. SARS-CoV-2 is a highly infectious virus with a long incubation period, encompassing asymptomatic infections. Therefore, accurate detection of SARS-CoV-2 is essential for the prevention and control of the epidemic. Currently, nucleic acid detection has played an important role in the prevention and control of SARS-CoV-2. A variety of nucleic acid detection technologies for SARS-CoV-2 have been developed, and some technologies have been converted into available kits for clinical detection. However, these technologies have different principles and different detection platforms. How to choose the appropriate nucleic acid detection technology for SARS-CoV-2 perplexes epidemic prevention and control. Based on the latest research progresses of nucleic acid technologies for SARS-CoV-2, this paper introduced the principles and technology platforms of these detection technologies, thoroughly compared the advantages and disadvantages of each technique and clarified the scope of applications, providing a reference for selecting the appropriate nucleic acid detection technology for SARS-CoV-2. Furthermore, this paper provided a prospect for developing technologies of detecting pathogens similar to SARS-CoV-2. © 2020 Changchun Institute of Applied Chemistry, Chinese Academy of Sciences
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Objective: To study the early dynamics of the epidemic of coronavirus disease (COVID-19) in China from 15 to 31 January, 2020, and estimate the corresponding epidemiological parameters (incubation period, generation interval and basic reproduction number) of the epidemic. Methods: By means of Weibull, Gamma and Lognormal distributions methods, we estimated the probability distribution of the incubation period and generation interval data obtained from the reported COVID-19 cases. Moreover, the AIC criterion was used to determine the optimal distribution. Considering the epidemic is ongoing, the exponential growth model was used to fit the incidence data of COVID-19 from 10 to 31 January, 2020, and exponential growth method, maximum likelihood method and SEIR model were used to estimate the basic reproduction number. Results: Early COVID-19 cases kept an increase in exponential growth manner before 26 January, 2020, then the increase trend became slower. The average incubation period was 5.01 (95%CI: 4.31-5.69) days; the average generation interval was 6.03 (95%CI: 5.20-6.91) days. The basic reproduction number was estimated to be 3.74 (95%CI: 3.63-3.87), 3.16 (95%CI: 2.90-3.43), and 3.91 (95%CI: 3.71-4.11) by three methods, respectively. Conclusions: The Gamma distribution fits both the generation interval and incubation period best, and the mean value of generation interval is 1.02 day longer than that of incubation period. The relatively high basic reproduction number indicates that the epidemic is still serious; Based on our analysis, the turning point of the epidemic would be seen on 26 January, the growth rate would be lower afterwards.