UHPLC/TOFHRMS analysis and anti-inflammatory effect of leaf extracts from Zizyphus jujuba in LPS-stimulated RAW264.7 cells

Zizyphus jujube is a plant in the buckthorn family (Rhamnaceae) that has been the subject of research into antibacterial, antifungal and anti-inflammatory properties of its fruit and seed. However, few studies have investigated its leaves. In this study, the anti-inflammatory activity of ZJL (an extract of Z. jujube leaf) was evaluated to verify its potential as an anti-inflammatory agent and SARS-CoV-2 medicine, using nitric oxide (NO) assay, RT-PCR, SDS-PAGE, Western blotting, and UHPLC/TOFHRMS analysis. We found that ZJL suppresed pro-inflammatory mediators such as NO, inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and tumor necrosis factor α (TNF-α) in lipopolysaccharide (LPS)-induced RAW264.7 cells. ZJL acted by inhibiting NF-KB and MAPK signaling pathway activity. We also confirmed that ZJL contains a phenol compound and flavonoids with anti-inflammatory activity such as trehalose, maleate, epigallocatechin, hyperoside, catechin, 3-O-coumaroylquinic acid, rhoifolin, gossypin, kaempferol 3-neohesperidoside, rutin, myricitrin, guaiaverin, quercitrin, quercetin, ursolic acid, and pheophorbide a. These findings suggest that ZJL may have great potential for the development of anti-inflammatory drugs and vaccines via inhibition of NF‐ĸB and MAPK signaling in LPS-induced RAW264.7 cells. © Korean Society for Plant Biotechnology.

Impact of Vaccination Policies for COVID-19 Using Hybrid Simulation

A stochastic model for individual immune response is developed. This model is then incorporated in a larger simulation model for the spread of COVID-19 in a population. The simulator allows random transitions between being susceptible, exposed, having mild or severe symptoms, as well as random non-exponential sojourn times in those states. The model is more efficient than others based on geographical location, where the virus spreads according to actual distance between individuals. We are able to simulate much larger populations and vary parameters such as time between vaccinations, probability of infection, and so on. We present an application to study the effects on healthcare as a function of vaccination policies. © 2022 IEEE.

FAMILY CAREGIVING IN LTC SETTINGS DURING THE PANDEMIC: COMMUNICATION CHALLENGES AND LEVERAGING TECHNOLOGY

COVID19 related lockdown and protocols caused disruptions in family caregiving for older adults living in LTC settings. However, there is a paucity of research on the challenges and experiences of family caregivers in maintaining their caregiving role during the pandemic. Hence, this qualitative study explores family caregivers' communication challenges and the role technology played in performing their caregiving roles. One-on-one in-depth interviews (N=25) were conducted with family caregivers (Mean age= 59.7;92% female;76% child) via phone/Zoom. Interviews were transcribed and thematically analyzed using Nvivo12. Findings demonstrate that family caregivers of older adults in LTC settings experienced severe communication barriers with staff at those facilities in the early onset of the pandemic, including delays of important information about their care recipients. Participants highlighted high staff turnover, inadequate training, staff unfamiliarity with technology, and poor internet connections as perpetuating communication barriers. During this time, their older care recipients experienced visual and hearing impairments that affected their ability to communicate, as well as cognitive decline. Despite this, family caregivers were able to successfully utilize various forms of technology to continue providing care supports and social support to their loved ones. Although participants relied on phone calls and email communications, they also used other platforms including Zoom, FaceTime, Nixplay, and TextNow. Participants used devices including landline phones, cellphones, computers, tablets, Ipads, and walkie-talkies to execute their communication. Implications of this study suggest that improving access and utilization of technology in LTC settings can enhance family caregiving during unprecedented events like the COVID19 pandemic.

FAMILY CAREGIVING FOR OLDER ADULTS IN LONG-TERM CARE DURING THE PANDEMIC: STRESS, SOCIAL SUPPORT, AND ADAPTATION

Older adults residing in long-term care (LTC) are especially vulnerable to the COVID-19 pandemic. Federal and local health officials have issued strict visitation guidelines, including family caregivers. Given that family caregivers are essential in the well-being for older adults in LTC, these measures have had an enormous impact. As little is known about the experiences of family caregivers, the purpose of this study was to explore how the COVID-19 pandemic impacted family caregivers' roles, mental health, and adaptation. Semi-structured interviews (N=25) were conducted with family caregivers of older adults in LTC (Mean age= 59.7;92% female) via phone/Zoom. An interview guide led the question asking process and participants were asked open-ended questions about the impact of COVID-19 related protocols on their caregiving, mental health, and sources of social support. Interviews were transcribed verbatim and analyzed in Nvivo, guided by Grounded Theory methods. The majority of participants (76%) identified as a child of their care recipient. Findings highlight that most participants experienced numerous changes to their caregiving tasks, such as assisting with activities of daily living (ADLs), limited monitoring for their loved ones, and a reduction of social support provided to the care recipient. Family caregivers also reported other changes in their roles that resulted in increased stress and mental health concerns. These concerns were discussed in detail, including ways in which family caregivers adapted to their new roles and managed their stress. Findings from this study will inform interventions geared to better support family caregivers, particularly during times of crisis.

Public Acceptance 0f Covid-19 Related Location Tracking Technology While in Quarantine: Evidence from South Korea

INTRODUCTION: Since the outbreak of the COVI D-19 pandemic in December 2019, public policy debate has been increasingly focusing on developing and implementing new disease prevention measures based on tracking of geographical location, in particular during the quarantine period. Limited studies have so far investigated possible public acceptance of such measures.METHODS: We analyzed a sample data of 1,000 respondents from the 2021 Korean Social Science Data Center using descriptive statistics and logistic regression modelling. The outcome variable was the binary variable measuring the public acceptance of COVID-19 related tracking devices for people subjected to quarantine, explanatory variable included socio-economic characteristics and subjective perception measures.RESULTS: The results suggest that subjective factors, such as perceived likelihood of virus contraction (OR=1.78) and severity of the disease (OR=2.21), rather than socio-economic factors, are key determinants of public acceptance of COVID-19 related location tracking technology. Elderly participants in the middle socio-economic class have shown the highest acceptance rate for tracking device implementationCONCLUSION: Although the use of location tracking devices has been increasing exponentially, there is still limited understanding in terms of public acceptance of such devices. The results of this study contribute to identifying such determinants, this contributing to policy design related to COVID-19 prevention.

Detecting Context-Aware Deviations in Process Executions

A deviation detection aims to detect deviating process instances, e.g., patients in the healthcare process and products in the manufacturing process. A business process of an organization is executed in various contextual situations, e.g., a COVID-19 pandemic in the case of hospitals and a lack of semiconductor chip shortage in the case of automobile companies. Thus, context-aware deviation detection is essential to provide relevant insights. However, existing work 1) does not provide a systematic way of incorporating various contexts, 2) is tailored to a specific approach without using an extensive pool of existing deviation detection techniques, and 3) does not distinguish positive and negative contexts that justify and refute deviation, respectively. In this work, we provide a framework to bridge the aforementioned gaps. We have implemented the proposed framework as a web service that can be extended to various contexts and deviation detection methods. We have evaluated the effectiveness of the proposed framework by conducting experiments using 255 different contextual scenarios. © 2022, Springer Nature Switzerland AG.

Therapeutic Effect of Regdanvimab in Patients with Mild to Moderate COVID-19: Day 28 Results from a Multicentre, Randomised, Controlled Pivotal Trial

Background. Regdanvimab is a monoclonal antibody with activity against SARSCoV-2. A Phase 2/3 study with two parts is currently ongoing and data up to Day 28 of Part 1 is available while the data from 1315 patients enrolled in Part 2 are expected in June 2021. Methods. This phase 2/3, randomized, parallel-group, placebo-controlled, double-blind study with 2 parts is aimed to assess the therapeutic efficacy of regdanvimab in outpatients with mild to moderate COVID-19, not requiring supplemental oxygen therapy. Patients aged >18 with the onset of symptoms within 7 days were eligible to be enrolled. Results. In Part 1, 307 patients (101, 103, and 103 patients in the regdanvimab 40 mg/kg, regdanvimab 80 mg/kg, and placebo groups, respectively) were confirmed to have COIVD-19 by RT-qPCR at Day 1 (or Day 2). Regdanvimab significantly reduced the proportion of patients who required hospitalization or supplemental oxygen therapy compared to placebo (8.7% in the placebo vs. 4.0% in the regdanvimab 40 mg/kg). The difference in events rate was even larger in patients who met the high-risk criteria and confirmed a 66.1% reduction in patients receiving regdanvimab 40 mg/kg (Table 1). The median time to clinical recovery was shortened by 2.9 days (7.18 days for regdanvimab 40 mg/kg and 10.03 days for placebo;high-risk). Also, greater reductions from baseline viral load were shown in regdanvimab groups (Figure 1). The safety results confirmed that the regdanvimab was safe and well-tolerated. Occurrence of adverse events (Table 2) and results of other safety assessments were generally comparable among the 3 groups. The overall rate of infusion-related reaction was low and no serious adverse events or deaths were reported. The anti-drug antibody positive rate was low in the regdanvimab groups (1.4% in regdanvimab vs. 4.5% in placebo), and no antibody-dependent enhancement was reported. Conclusion. Results from the first part of the study indicate that regdanvimab may lower the rate of hospitalisation or requirement of oxygen supplementation, with the greatest benefit noted in patients at high-risk of progressing to severe COVID-19. The second part of the study remains ongoing and blinded. Therefore, results for the primary endpoint are forthcoming and will be presented at IDWeek.

Immuno-enhancing effects of fruit of Actinidia polygama in macrophages

In this study, we evaluated the functionality of water extracts from the fruit of Actinidia polygama (APF) in macrophages. The APF is a medicinal plant belonging to Actinidiaceae, it has been reported to exert anti-inflammatory, analgesic and hypouricemic activities. However, the potential mechanism for the immune activation of the APF is still insufficient. So, we evaluated whether APF exerts immune activation activities and elucidated its potential mechanism in macrophages. The APF dose-dependently increased the production of immunomodulators in macrophages. The inhibition of Toll-like receptor 4 (TLR4) blocked APF-mediated production of immunomodulators in RAW264.7 cells. In addition, APF-mediated production of immunomodulators was attenuated by MAPKs and NF-kappa B inhibition in RAW264.7 cells. Also, we analyzed the sugars content of the APF. The contents of glucose, galactose and fructose were 3597, 904, 7582 mg/L, respectively. These results suggest that the APF may have great potential for the development of immunomodulatory drugs.

Learning a high-dimensional linear structural equation model via '1-regularized regression

This paper develops a new approach to learning high-dimensional linear structural equation models (SEMs) without the commonly assumed faithfulness, Gaussian error distribution, and equal error distribution conditions. A key component of the algorithm is componentwise ordering and parent estimations, where both problems can be efficiently addressed using '1-regularized regression. This paper proves that sample sizes n = (d2 log p) and n = (d2p2=m) are sufficient for the proposed algorithm to recover linear SEMs with sub- Gaussian and (4m)-th bounded-moment error distributions, respectively, where p is the number of nodes and d is the maximum degree of the moralized graph. Further shown is the worst-case computational complexity O(n(p3 + p2d2)), and hence, the proposed algorithm is statistically consistent and computationally feasible for learning a high-dimensional linear SEM when its moralized graph is sparse. Through simulations, we verify that the proposed algorithm is statistically consistent and computationally feasible, and it performs well compared to the state-of-the-art US, GDS, LISTEN and TD algorithms with our settings. We also demonstrate through real COVID-19 data that the proposed algorithm is well-suited to estimating a virus-spread map in China. © 2021 Microtome Publishing. All rights reserved.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.