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1.
SSRN; 2022.
Preprint in English | SSRN | ID: ppcovidwho-334513

ABSTRACT

One of the main changes in Korea’s foreign affairs in recent years is the expansion of official development assistance (ODA), among which Africa is showing particularly rapid growth. Korea’s ODA to Africa accounted for 15% of its total ODA budget in 2010, and rose to 25% in 2019 as Korea emphasized its role in international development. Korea ranks 11th in terms of the cumulative size of ODA to the African healthcare sector between 2011–2019, totaling 674 million USD. As Korea’s expansion of ODA and solidarity in international development aid to respond to COVID-19 are related, the expansion of ODA in the African healthcare sector is anticipated to continue. This study analyzes features of the healthcare sector in Africa in an effort to suggest various plans for development cooperation, based on an evaluation of Korea’s ODA project design to enable the effective provision of ODA.

2.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326046

ABSTRACT

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This is a post-peer-review, pre-copyedit version of an article published in Scientific Reports The final authenticated version is available online at: https://www.nature.com/articles/s41598-021-02353-5

3.
Electronics ; 10(23):3019, 2021.
Article in English | ProQuest Central | ID: covidwho-1559148

ABSTRACT

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

4.
Sci Rep ; 11(1): 23179, 2021 11 30.
Article in English | MEDLINE | ID: covidwho-1545641

ABSTRACT

Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug's representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.


Subject(s)
COVID-19/drug therapy , Drug Repositioning , Neural Networks, Computer
5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-292589

ABSTRACT

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This is a post-peer-review, pre-copyedit version of an article published in Scientific Reports The final authenticated version is available online at: https://www.researchsquare.com/article/rs-114758/v1

6.
J Biomed Inform ; 119: 103818, 2021 07.
Article in English | MEDLINE | ID: covidwho-1237740

ABSTRACT

OBJECTIVE: Study the impact of local policies on near-future hospitalization and mortality rates. MATERIALS AND METHODS: We introduce a novel risk-stratified SIR-HCD model that introduces new variables to model the dynamics of low-contact (e.g., work from home) and high-contact (e.g., work on-site) subpopulations while sharing parameters to control their respective R0(t) over time. We test our model on data of daily reported hospitalizations and cumulative mortality of COVID-19 in Harris County, Texas, from May 1, 2020, until October 4, 2020, collected from multiple sources (USA FACTS, U.S. Bureau of Labor Statistics, Southeast Texas Regional Advisory Council COVID-19 report, TMC daily news, and Johns Hopkins University county-level mortality reporting). RESULTS: We evaluated our model's forecasting accuracy in Harris County, TX (the most populated county in the Greater Houston area) during Phase-I and Phase-II reopening. Not only does our model outperform other competing models, but it also supports counterfactual analysis to simulate the impact of future policies in a local setting, which is unique among existing approaches. DISCUSSION: Mortality and hospitalization rates are significantly impacted by local quarantine and reopening policies. Existing models do not directly account for the effect of these policies on infection, hospitalization, and death rates in an explicit and explainable manner. Our work is an attempt to improve prediction of these trends by incorporating this information into the model, thus supporting decision-making. CONCLUSION: Our work is a timely effort to attempt to model the dynamics of pandemics under the influence of local policies.


Subject(s)
COVID-19 , Hospitalization , Humans , Pandemics , Policy , SARS-CoV-2 , United States
7.
Gene Rep ; 23: 101100, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1144615

ABSTRACT

The spike (S) protein mutations of SARS-CoV-2 are of major concern in terms of viral transmission and pathogenesis. Hence, we developed a PCR-based method to rapidly detect the 6 mutational hotspots (H49Y, G476S, V483A, H519Q, A520S, and D614G) in the S protein and applied this method to analyze the hotspots in the viral isolates from different geographical origins. Here, we identified that there was only the D614G mutation in the viral isolates. As of September 30, 2020, the analysis of 113,381 sequences available from the public repositories revealed that the SARS-CoV-2 variant carrying G614 has become the most prevalent form globally. Our results support recent epidemiological and genomic data demonstrating that the viral infectivity and transmission are enhanced by the S protein D614G mutation.

8.
J Am Med Inform Assoc ; 27(11): 1721-1726, 2020 11 01.
Article in English | MEDLINE | ID: covidwho-1024117

ABSTRACT

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.


Subject(s)
Biomedical Research , Computer Security , Coronavirus Infections , Information Dissemination , Pandemics , Pneumonia, Viral , Privacy , COVID-19 , Humans , Information Dissemination/ethics , Internationality , Machine Learning
9.
Proc Natl Acad Sci U S A ; 117(47): 29832-29838, 2020 11 24.
Article in English | MEDLINE | ID: covidwho-900111

ABSTRACT

Effective therapies are urgently needed for the SARS-CoV-2/COVID-19 pandemic. We identified panels of fully human monoclonal antibodies (mAbs) from large phage-displayed Fab, scFv, and VH libraries by panning against the receptor binding domain (RBD) of the SARS-CoV-2 spike (S) glycoprotein. A high-affinity Fab was selected from one of the libraries and converted to a full-size antibody, IgG1 ab1, which competed with human ACE2 for binding to RBD. It potently neutralized replication-competent SARS-CoV-2 but not SARS-CoV, as measured by two different tissue culture assays, as well as a replication-competent mouse ACE2-adapted SARS-CoV-2 in BALB/c mice and native virus in hACE2-expressing transgenic mice showing activity at the lowest tested dose of 2 mg/kg. IgG1 ab1 also exhibited high prophylactic and therapeutic efficacy in a hamster model of SARS-CoV-2 infection. The mechanism of neutralization is by competition with ACE2 but could involve antibody-dependent cellular cytotoxicity (ADCC) as IgG1 ab1 had ADCC activity in vitro. The ab1 sequence has a relatively low number of somatic mutations, indicating that ab1-like antibodies could be quickly elicited during natural SARS-CoV-2 infection or by RBD-based vaccines. IgG1 ab1 did not aggregate, did not exhibit other developability liabilities, and did not bind to any of the 5,300 human membrane-associated proteins tested. These results suggest that IgG1 ab1 has potential for therapy and prophylaxis of SARS-CoV-2 infections. The rapid identification (within 6 d of availability of antigen for panning) of potent mAbs shows the value of large antibody libraries for response to public health threats from emerging microbes.


Subject(s)
COVID-19 Serological Testing/methods , COVID-19 Vaccines/immunology , COVID-19/therapy , Angiotensin-Converting Enzyme 2/metabolism , Animals , Antibodies, Viral/blood , Antibodies, Viral/immunology , Antibody-Dependent Cell Cytotoxicity , COVID-19 Serological Testing/standards , COVID-19 Vaccines/standards , Chlorocebus aethiops , Cricetinae , Female , Humans , Immunization, Passive/methods , Immunization, Passive/standards , Immunogenicity, Vaccine , Immunoglobulin G/blood , Immunoglobulin G/immunology , Mice , Mice, Inbred BALB C , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/immunology , Vero Cells
10.
ArXiv ; 2020 Sep 23.
Article in English | MEDLINE | ID: covidwho-807713

ABSTRACT

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

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