Comprehensive DNA methylation profiling of COVID-19 and hepatocellular carcinoma to identify common pathogenesis and potential therapeutic targets.

BACKGROUND & AIMS: The effects of SARS-CoV-2 infection can be more complex and severe in patients with hepatocellular carcinoma (HCC) as compared to other cancers. This is due to several factors, including pre-existing conditions such as viral hepatitis and cirrhosis, which are commonly associated with HCC. METHODS: We conducted an analysis of epigenomics in SARS-CoV-2 infection and HCC patients, and identified common pathogenic mechanisms using weighted gene co-expression network analysis (WGCNA) and other analyses. Hub genes were identified and analyzed using LASSO regression. Additionally, drug candidates and their binding modes to key macromolecular targets of COVID-19 were identified using molecular docking. RESULTS: The epigenomic analysis of the relationship between SARS-CoV-2 infection and HCC patients revealed that the co-pathogenesis was closely linked to immune response, particularly T cell differentiation, regulation of T cell activation and monocyte differentiation. Further analysis indicated that CD4+ T cells and monocytes play essential roles in the immunoreaction triggered by both conditions. The expression levels of hub genes MYLK2, FAM83D, STC2, CCDC112, EPHX4 and MMP1 were strongly correlated with SARS-CoV-2 infection and the prognosis of HCC patients. In our study, mefloquine and thioridazine were identified as potential therapeutic agents for COVID-19 in combined with HCC. CONCLUSIONS: In this research, we conducted an epigenomics analysis to identify common pathogenetic processes between SARS-CoV-2 infection and HCC patients, providing new insights into the pathogenesis and treatment of HCC patients infected with SARS-CoV-2.

Systemic exposure to hydroxychloroquine and its relationship with outcome in severely ill COVID-19 patients in New York City.

AIM: To investigate the relationship between systemic exposure to hydroxychloroquine (HCQ) and its metabolite desethylhydroxychloroquine (DHCQ) and clinical outcome in severely ill patients treated with a standard oral dose regimen of HCQ during the first wave of COVID-19 in New York City. METHODS: We correlated retrospective clinical data with drug exposure prospectively assessed from convenience samples using population pharmacokinetics and Bayesian estimation. Systemic exposure was assessed in 215 patients admitted to ICU or COVID-ward for whom an interleukin-6 level was requested and who were still alive 24 hours after the last dose of HCQ. Patients received oral HCQ 600 mg twice daily on day 1 followed by 4 days of 400 mg daily. RESULTS: Fifty-three precent of the patients were intubated at 5.4 ± 6.4 days after admission and 26.5% died at an average of 32.2 ± 19.1 days. QTc at admission was 448 ± 34 ms. Systemic exposure to HCQ and DHCQ demonstrated substantial variability. Cumulative area under the serum concentration-time curve up to infinity for HCQ was 71.4 ± 19.3 h mg/L and for DHCQ 56.5 ± 28.3 h mg/L. Variability in systemic exposure was not clearly explained by renal function, liver function or inflammatory state. In turn, systemic exposure did not correlate with intubation status, survival or QTc prolongation. CONCLUSION: This study in severely ill patients was not able to find any relationship between systemic exposure to HCQ and DHCQ and clinical outcome at a routine dose regimen and adds to the growing body of evidence that oral HCQ does not alter the course of disease in COVID-19 patients.

Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic.

BACKGROUND: Persistent psychological distress associated with the coronavirus disease 2019 (COVID-19) pandemic has been well documented. This study aimed to identify pre-COVID brain functional connectome that predicts pandemic-related distress symptoms among young adults. METHODS: Baseline neuroimaging studies and assessment of general distress using the Depression, Anxiety and Stress Scale were performed with 100 healthy individuals prior to wide recognition of the health risks associated with the emergence of COVID-19. They were recontacted for the Impact of Event Scale-Revised and the Posttraumatic Stress Disorder Checklist in the period of community-level outbreaks, and for follow-up distress evaluation again 1 year later. We employed the network-based statistic approach to identify connectome that predicted the increase of distress based on 136-region-parcellation with assigned network membership. Predictive performance of connectome features and causal relations were examined by cross-validation and mediation analyses. RESULTS: The connectome features that predicted emergence of distress after COVID contained 70 neural connections. Most within-network connections were located in the default mode network (DMN), and affective network-DMN and dorsal attention network-DMN links largely constituted between-network pairs. The hippocampus emerged as the most critical hub region. Predictive models of the connectome remained robust in cross-validation. Mediation analyses demonstrated that COVID-related posttraumatic stress partially explained the correlation of connectome to the development of general distress. CONCLUSIONS: Brain functional connectome may fingerprint individuals with vulnerability to psychological distress associated with the COVID pandemic. Individuals with brain neuromarkers may benefit from the corresponding interventions to reduce the risk or severity of distress related to fear of COVID-related challenges.

Exploring the Mediating Effect of Physical Activities on Built Environment and Obesity for Elderly People: Evidence From Shanghai, China.

There is still a scarcity of literature on the specific mechanisms of the linkage between the built environment and obesity. As a result, this study investigated whether and how physical activities mediate the associations between the objective built environment and the BMI of elderly people. To investigate the effect of the duration and intensity of physical activity on the effect of the built environment, the study made use of the bootstrap method. In general, we discovered that physical activity duration has a huge mediating effect on the elderly people in Shanghai, especially with respect to the density and accessibility of facilities (gyms, parks, fast-food restaurants) that can greatly stimulate physical activity in elderly people to reduce their BMI. There were both direct and indirect effects on their BMI, which means that the health benefits of green spaces for older people may be more complicated than first thought.

Forecasting COVID-19 Cases Based on Social Distancing in Maryland, USA: A Time-Series Approach.

OBJECTIVE: Our objective is to forecast the number of coronavirus disease 2019 (COVID-19) cases in the state of Maryland, United States, using transfer function time series (TS) models based on a Social Distancing Index (SDI) and determine how their parameters relate to the pandemic mechanics. METHODS: A moving window of 2 mo was used to train the transfer function TS model that was then tested on the next week data. After accounting for a secular trend and weekly cycle of the SDI, a high correlation was documented between it and the daily caseload 9 days later. Similar patterns were also observed on the daily COVID-19 cases and incorporated in our models. RESULTS: In most cases, the proposed models provide a reasonable performance that was, on average, moderately better than that delivered by TS models based only on previous observations. The model coefficients associated with the SDI were statistically significant for most of the training/test sets. CONCLUSIONS: Our proposed models that incorporate SDI can forecast the number of COVID-19 cases in a region. Their parameters have real-life interpretations and, hence, can help understand the inner workings of the epidemic. The methods detailed here can help local health governments and other agencies adjust their response to the epidemic.