A gateway conspiracy? Belief in COVID-19 conspiracy theories prospectively predicts greater conspiracist ideation

A primary focus of research on conspiracy theories has been understanding the psychological characteristics that predict people’s level of conspiracist ideation. However, the dynamics of conspiracist ideation—i.e., how such tendencies change over time—are not well understood. To help fill this gap in the literature, we used data from two longitudinal studies (Study 1 N = 107; Study 2 N = 1,037) conducted during the COVID-19 pandemic. We find that greater belief in COVID-19 conspiracy theories at baseline predicts both greater endorsement of a novel real-world conspiracy theory involving voter fraud in the 2020 American Presidential election (Study 1) and increases in generic conspiracist ideation over a period of several months (Studies 1 and 2). Thus, engaging with real-world conspiracy theories appears to act as a gateway, leading to more general increases in conspiracist ideation. Beyond enhancing our knowledge of conspiracist ideation, this work highlights the importance of fighting the spread of conspiracy theories

Investigating the conservatism-disgust paradox in reactions to the COVID-19 pandemic: A reexamination of the interrelations among political ideology, disgust sensitivity, and pandemic response

Research has documented robust associations between greater disgust sensitivity and (1) concerns about disease, and (2) political conservatism. However, the COVID-19 disease pandemic raised challenging questions about these associations. In particular, why have conservatives-despite their greater disgust sensitivity-exhibited less concern about the pandemic? Here, we investigate this "conservatism-disgust paradox" and address several outstanding theoretical questions regarding the interrelations among disgust sensitivity, ideology, and pandemic response. In four studies (N = 1,764), we identify several methodological and conceptual factors-in particular, an overreliance on self-report measures-that may have inflated the apparent associations among these constructs. Using non-self-report measures, we find evidence that disgust sensitivity may be a less potent predictor of disease avoidance than is typically assumed, and that ideological differences in disgust sensitivity may be amplified by self-report measures. These findings suggest that the true pattern of interrelations among these factors may be less "paradoxical" than is typically believed

The Inflammatory Kinase MAP4K4 Promotes Reactivation of Kaposi's Sarcoma Herpesvirus and Enhances the Invasiveness of Infected Endothelial Cells

Kaposi's sarcoma (KS) is a mesenchymal tumour, which is caused by Kaposi's sarcoma herpesvirus (KSHV) and develops under inflammatory conditions. KSHV-infected endothelial spindle cells, the neoplastic cells in KS, show increased invasiveness, attributed to the elevated expression of metalloproteinases (MMPs) and cyclooxygenase-2 (COX-2). The majority of these spindle cells harbour latent KSHV genomes, while a minority undergoes lytic reactivation with subsequent production of new virions and viral or cellular chemo- and cytokines, which may promote tumour invasion and dissemination. In order to better understand KSHV pathogenesis, we investigated cellular mechanisms underlying the lytic reactivation of KSHV. Using a combination of small molecule library screening and siRNA silencing we found a STE20 kinase family member, MAP4K4, to be involved in KSHV reactivation from latency and to contribute to the invasive phenotype of KSHV-infected endothelial cells by regulating COX-2, MMP-7, and MMP-13 expression. This kinase is also highly expressed in KS spindle cells in vivo. These findings suggest that MAP4K4, a known mediator of inflammation, is involved in KS aetiology by regulating KSHV lytic reactivation, expression of MMPs and COX-2, and, thereby modulating invasiveness of KSHV-infected endothelial cells. © 2013 Haas et al

Kaposi's Sarcoma Associated Herpes Virus (KSHV) Induced COX-2: A Key Factor in Latency, Inflammation, Angiogenesis, Cell Survival and Invasion

Kaposi's sarcoma (KS), an enigmatic endothelial cell vascular neoplasm, is characterized by the proliferation of spindle shaped endothelial cells, inflammatory cytokines (ICs), growth factors (GFs) and angiogenic factors. KSHV is etiologically linked to KS and expresses its latent genes in KS lesion endothelial cells. Primary infection of human micro vascular endothelial cells (HMVEC-d) results in the establishment of latent infection and reprogramming of host genes, and cyclooxygenase-2 (COX-2) is one of the highly up-regulated genes. Our previous study suggested a role for COX-2 in the establishment and maintenance of KSHV latency. Here, we examined the role of COX-2 in the induction of ICs, GFs, angiogenesis and invasive events occurring during KSHV de novo infection of endothelial cells. A significant amount of COX-2 was detected in KS tissue sections. Telomerase-immortalized human umbilical vein endothelial cells supporting KSHV stable latency (TIVE-LTC) expressed elevated levels of functional COX-2 and microsomal PGE2 synthase (m-PGES), and secreted the predominant eicosanoid inflammatory metabolite PGE2. Infected HMVEC-d and TIVE-LTC cells secreted a variety of ICs, GFs, angiogenic factors and matrix metalloproteinases (MMPs), which were significantly abrogated by COX-2 inhibition either by chemical inhibitors or by siRNA. The ability of these factors to induce tube formation of uninfected endothelial cells was also inhibited. PGE2, secreted early during KSHV infection, profoundly increased the adhesion of uninfected endothelial cells to fibronectin by activating the small G protein Rac1. COX-2 inhibition considerably reduced KSHV latent ORF73 gene expression and survival of TIVE-LTC cells. Collectively, these studies underscore the pivotal role of KSHV induced COX-2/PGE2 in creating KS lesion like microenvironment during de novo infection. Since COX-2 plays multiple roles in KSHV latent gene expression, which themselves are powerful mediators of cytokine induction, anti-apoptosis, cell survival and viral genome maintainence, effective inhibition of COX-2 via well-characterized clinically approved COX-2 inhibitors could potentially be used in treatment to control latent KSHV infection and ameliorate KS

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages