ABSTRACT
Covid-19 virus variants identified so far are due to viral genetic diversity, genetic evolution, and variable infectivity, suggesting that high infection rates and high mortality rates may be contributed by these mutations. And it has been reported that the targeting strategies for innate immunity should be less vulnerable to viral evolution, variant emergence and resistance. Therefore, the most effective solution to Covid-19 infection has been proposed to prevent and treat severe exacerbation of patients with moderate disease by enhancing human immune responses such as NK cell and T cell. In previous studies, we demonstrated for the first time that gamma-PGA induced significant antitumor activity and antiviral activity by modulating NK cell-mediated cytotoxicity. Especially intranasal administration of gamma-PGA was found to effectively induce protective innate and CTL immune responses against viruses and we found out that gamma-PGA can be an effective treatment for cervical intraepithelial neoplasia 1 through phase 2b clinical trial. In this study, the possibility of gamma-PGA as a Covid-19 immune modulating agent was confirmed by animal experiments infected with Covid-19 viruses. After oral administration of gamma-PGA 300mug/mouse once a day for 5 days in a K18-hACE2 TG mouse model infected with SARS-CoV-2 (NCCP 43326;original strain) and SARS-CoV-2 (NCCP 43390;Delta variant), virus titer and clinical symptom improvement were confirmed. In the RjHan:AURA Syrian hamster model infected with SARS-CoV-2 (NCCP 49930;Delta variant), 350 or 550 mug/head of gamma-PGA was administered orally for 10 days once a day. The virus for infection was administered at 5 x 104 TCID50, and the titer of virus and the improvement of pneumonia lesions were measured to confirm the effectiveness in terms of prevention or treatment. In the mouse model infected with original Covid-19 virus stain, the weight loss was significantly reduced and the survival rate was also improved by the administration of gamma-PGA. And gamma-PGA alleviated the pneumonic lesions and reduced the virus titer of lung tissue in mice infected with delta variant. In the deltavariant virus infected hamster model, gamma-PGA showed statistically significant improvement of weight loss and lung inflammation during administration after infection. This is a promising result for possibility of Covid-19 therapeutics along with the efficacy results of mouse model, suggesting gammaPGA can be therapeutic candidate to modulate an innate immune response for Covid-19.
ABSTRACT
In the United States, megaregions, which are networks of urban centres and their hinterlands, have a greater prevalence of COVID-19, but have been overlooked as a geographical unit for multi-jurisdictional governance for pandemic response. Existing multi-metropolitan planning organization (MPO) collaborations and state-level COVID-19 coalitions in the United States demonstrate the utility of the megaregion as an effective framework for regional collaboration and operations. Using multilevel modelling, we explore the significance of megaregions in explaining disease occurrence. The results suggest that the megaregion can be an appropriate geographical scale for multi-jurisdictional operations, governance and pandemic response. © 2023 Regional Studies Association.
ABSTRACT
BACKGROUND: Telehealth can effectively connect patients to clinicians across distance and time, but its accessibility remains limited for rural populations. The Veterans Health Administration (VA), serving 9+ million patients across 50 states, has long championed telehealth use through national initiatives that distribute tablets to patients for video visits and that provide contingency clinician staffing via telehealth for underserved clinics. VA efforts had focused largely on rural populations where disparities in health care access and telehealth use existed but have since expanded more broadly with the onset of the COVID-19 pandemic. This study examines rural-urban differences in telehealth use for primary care services across all VA healthcare systems nationally before and after pandemic onset. METHODS: In an ongoing VA quality improvement (non-research) effort, we conducted a retrospective study on Veterans who received primary care (PC, n= 6,340,167 patients;63,541,577 visits) and mental health integration (PC-MHI, n= 977,243 patients;3,621,653 visits) services from 138 VA healthcare systems, nationwide. For each site, we aggregated monthly counts of telehealth (and specifically, video) visits for PC and PC-MHI separately during 12-months before (March 16, 2019-March 15, 2020) and 21-months after pandemic onset (March 16, 2020-December 16, 2021). We examined whether rurality of VA healthcare system predicted telehealth (and video) use for all primary care related (PC and PC-MHI) encounters over time, adjusting for VA size (number of patients), age/disability (Charlson Comorbidity Index), and racial-ethnic composition (%Black, %Hispanic) in regression analyses. RESULTS: In fully adjusted models, rural VAs initially had higher rates of PC telehealth use than urban VAs (35% vs 29%), which reversed after pandemic onset (56% vs 60%), leading to a 35% reduction in odds of telehealth use over time (CI=0.55-0.76;p<0.001). In comparison, the rural-urban gap grew even larger for PC-MHI services (OR=0.49;CI=0.36-0.68;p<0.001), as rural lagged urban telehealth expansion (before 29% vs 25%;after 77% vs 84%). While video use was low overall, the rural-urban divide was stark for PC (OR=0.28;CI=0.19-0.40;p<0.001) and PC-MHI services (OR=0.34;CI=0.21-0.56;p<0.001). Rural VAs initially had higher rates of PC (2% vs 1%) and PC-MHI video use (8% vs 5%) than urban VAs, which reversed after pandemic onset (PC 4% vs 6%;PC-MHI 21% vs 33%). CONCLUSIONS: Despite initial telehealth gains in rural VAs, the pandemic exacerbated the rural-urban telehealth divide across sites. Compared to urban VAs, telehealth expansion lagged for rural ones, especially in mental health integration services. Video visits still constituted a minority of primary care services delivered. To prevent widening rural-urban divides, telehealth implementation efforts should address persistent disparities in structural capacity (e.g., internet bandwidth) and tailor technology to encourage adoption among rural users.
ABSTRACT
In this study, a survival analysis of the time to death caused by coronavirus disease 2019 is presented. The analysis of a dataset from the East Asian region with a focus on data from the Philippines revealed that the hazard of time to death was associated with the symptoms and background variables of patients. Machine learning algorithms, i.e., dimensionality reduction and boosting, were used along with conventional Cox regression. Machine learning algorithms solved the diverging problem observed when using traditional Cox regression and improved performance by maximizing the concordance index (C-index). Logistic principal component analysis for dimensionality reduction was significantly efficient in addressing the collinearity problem. In addition, to address the nonlinear pattern, a higher C-index was achieved using extreme gradient boosting (XGBoost). The results of the analysis showed that the symptoms were statistically significant for the hazard rate. Among the symptoms, respiratory and pneumonia symptoms resulted in the highest hazard level, which can help in the preliminary identification of high-risk patients. Among various background variables, the influence of age, chronic disease, and their interaction were identified as significant. The use of XGBoost revealed that the hazards were minimized during middle age and increased for younger and older people without any chronic diseases, with only the elderly having a higher risk of chronic disease. These results imply that patients with respiratory and pneumonia symptoms or older patients should be given medical attention.
ABSTRACT
The ongoing COVID-19 pandemic resulted in significant ramifications for international relations ranging from travel restrictions, global ceasefires, and international vaccine production and sharing agreements. Amidst a wave of infections in India that resulted in a systemic breakdown of healthcare infrastructure, a social welfare organization based in Pakistan offered to procure medical-grade oxygen to assist India - a nation which was involved in four wars with Pakistan in the past few decades. In this paper, we focus on Pakistani Twitter users' response to the ongoing healthcare crisis in India. While #IndiaNeedsOxygen and #PakistanStandsWithIndia featured among the toptrending hashtags in Pakistan, divisive hashtags such as #EndiaSaySorryToKashmir simultaneously started trending. Against the back-drop of a contentious history including four wars, divisive content of this nature, especially when a country is facing an unprecedented healthcare crisis, fuels further deterioration of relations. In this paper, we define a new task of detecting supportive content and demonstrate that existing NLP for social impact tools can be effectively harnessed for such tasks within a quick turnaround time. We also release the first publicly available data set(1) at the intersection of geopolitical relations and a raging pandemic in the context of India and Pakistan.