Characteristics of febrile seizures with SARS-CoV-2 infection in the Omicron era.

Background: While the pandemic of coronavirus disease 2019 (COVID-19) is ongoing, the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been dominant recently. The Omicron variant causes more seizures in pediatric patients compared with previously circulated variants. This study aimed to investigate the incidence and clinical features of febrile seizure (FS) in pediatric patients with COVID-19 during the Omicron era. Methods: The medical records of pediatric patients (≤18 years of age) diagnosed with COVID-19, who presented with FS between February 2020 and June 2022, were reviewed retrospectively to analyze clinical characteristics of FS in seven university-affiliated hospitals of Korea. Results: Of 664 pediatric patients with COVID-19 during the study period, 46 during the pre-Omicron period and 589 during the Omicron period were included in the study analysis; 29 patients during the transition period were excluded. Among the included patients, 81 (12.8%) had concomitant FS, and most (76.5%) experienced simple FS. All FS episodes occurred during the Omicron period and none of them during pre-Omicron period (P=0.016). Sixty-five (80.2%) and 16 (19.8%) patients were categorized as FS (patient age ≤60 months) and late-onset FS (patient age >60 months), respectively. Underlying neurologic disease (P=0.013) and focal onset seizure (P=0.012) were more common in the late-onset FS group than in the FS group; however, overall clinical manifestations and outcomes including seizures consistent with characteristics of complex FS and subsequent epilepsy were similar between the two groups. Conclusions: As the COVID-19 pandemic persists, the incidence of FS has increased with the emergence of the Omicron variant. About one-fifth of the patients experiencing FS due to infection by the Omicron variant of SARS-CoV-2 were aged >60 months; however, clinical characteristics and outcomes were favorable. More information and long-term prognoses in patients with FS due to COVID-19 should be acquired.

Challenges in Care for Non-COVID-19 Patients with Severe Chronic Illnesses during COVID-19 Pandemic: A Qualitative Study of Healthcare Providers Working around Acute Care Hospitals in South Korea.

BACKGROUND: The COVID-19 epidemic has afflicted patients with severe chronic illnesses who need continuous care between home and hospitals. This qualitative study examines the experiences and challenges of healthcare providers around acute care hospitals who have cared for patients with severe chronic illness in non-COVID-19 situations during the pandemic. METHODS: Eight healthcare providers, who work in various healthcare settings around acute care hospitals and frequently care for non-COVID-19 patients with severe chronic illnesses, were recruited using purposive sampling from September to October 2021 in South Korea. The interviews were subjected to thematic analysis. RESULTS: Four overarching themes were identified: (1) deterioration in the quality of care at various settings; (2) new emerging systemic problems; (3) healthcare providers holding on but reaching their limit; and (4) a decline in the quality of life of patients at the end of their lives, and their caregivers. CONCLUSION: Healthcare providers of non-COVID-19 patients with severe chronic illnesses reported that the quality of care was declining due to the structural problems of the healthcare system and policies centered solely on the prevention and control of COVID-19. Systematic solutions are needed for appropriate and seamless care for non-infected patients with severe chronic illness in the pandemic.

Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments.

The coronavirus disease 2019 (COVID-19) pandemic has caused a dramatic loss of human life and devastated the worldwide economy. Numerous efforts have been made to mitigate COVID-19 symptoms and reduce the death rate. We conducted literature mining of more than 250 thousand published works and curated the 174 most widely used COVID-19 medications. Overlaid with the human protein-protein interaction (PPI) network, we used Steiner tree analysis to extract a core subnetwork that grew from the pharmacological targets of ten credible drugs ascertained by the CTD database. The resultant core subnetwork consisted of 34 interconnected genes, which were associated with 36 drugs. Immune cell membrane receptors, the downstream cellular signaling cascade, and severe COVID-19 symptom risk were significantly enriched for the core subnetwork genes. The lung mast cell was most enriched for the target genes among 1355 human tissue-cell types. Human bronchoalveolar lavage fluid COVID-19 single-cell RNA-Seq data highlighted the fact that T cells and macrophages have the most overlapping genes from the core subnetwork. Overall, we constructed an actionable human target-protein module that mainly involved anti-inflammatory/antiviral entry functions and highly overlapped with COVID-19-severity-related genes. Our findings could serve as a knowledge base for guiding drug discovery or drug repurposing to confront the fast-evolving SARS-CoV-2 virus and other severe infectious diseases.

Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

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.

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence.

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.

Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates.

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.

Comparative analysis of mutational hotspots in the spike protein of SARS-CoV-2 isolates from different geographic origins.

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.

SCOR: A secure international informatics infrastructure to investigate COVID-19.

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.

Rapid identification of a human antibody with high prophylactic and therapeutic efficacy in three animal models of SARS-CoV-2 infection.

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.

Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation.

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.