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The recent outbreak of H1N1 has provided the scientific community with a sad but timely opportunity to understand the influence of socioeconomic determinants on H1N1 pandemic mortality. To this end, we have used data collected from 341 US counties to model H1N1 deaths/1000 using 12 socioeconomic predictors to discover why certain counties reported fewer H1N1 deaths compared to other counties. These predictors were then used to build a decision tree. The decision tree developed was then used to predict H1N1 mortality for the whole of the USA. Our estimate of 7667 H1N1 deaths are in accord with the lower bound of the CDC estimate of 8870 deaths. In addition to the H1N1 death estimates, we have listed possible counties to be targeted for health-related interventions. The respective state/county authorities can use these results as the basis to target and optimize the distribution of public health resources.

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Microsatellite-instable (MSI), a predictive biomarker for immune checkpoint blockade (ICB) response, is caused by mismatch repair deficiency (MMRd) that occurs through genetic or epigenetic silencing of MMR genes. Here, we report a mechanism of MMRd and demonstrate that protein phosphatase 2A (PP2A) deletion or inactivation converts cold microsatellite-stable (MSS) into MSI tumours through two orthogonal pathways: (i) by increasing retinoblastoma protein phosphorylation that leads to E2F and DNMT3A/3B expression with subsequent DNA methylation, and (ii) by increasing histone deacetylase (HDAC)2 phosphorylation that subsequently decreases H3K9ac levels and histone acetylation, which induces epigenetic silencing of MLH1. In mouse models of MSS and MSI colorectal cancers, triple-negative breast cancer and pancreatic cancer, PP2A inhibition triggers neoantigen production, cytotoxic T cell infiltration and ICB sensitization. Human cancer cell lines and tissue array effectively confirm these signaling pathways. These data indicate the dual involvement of PP2A inactivation in silencing MLH1 and inducing MSI.

The effects of exposure to atmospheric pollution on the incidence and mortality due to COVID-19 have been studied but not for sulfur dioxide (SO 2 ) in most studies. However, most studies failed to consider important cofounding factors in the estimation of health effects of air pollution. The objective of the study was to assess the short- and long-term effects of air pollution on the COVID-19 risk and fatality in Lombardy and Veneto. Air pollutants were studied based on monitoring station information in Lombardy and Veneto from January 2013 to May 2020. The daily number of cases and deaths of COVID-19 were collected from the reports of the Italian Ministry of Health in Italy. A generalized linear model with the generalized estimating equation method was used to evaluate the effects of short- and long-term exposure to air pollution on the COVID-19 outbreak in Lombardy and Veneto. After adjusting for other covariates, we found that short-term exposure to PM 2.5 and PM 10 had a tendency to increase the incidence and mortality of COVID-19 than long-term exposure, while for other air pollutants, including SO 2 and NO 2 , long-term exposure was more significant than short-term exposure. Both short- and long-term exposure of SO 2 resulted in increased health effects on COVID-19 pandemic. Our findings suggest that exposure to atmospheric pollution has a significant impact on COVID-19 pandemic and call for further researches to deeply investigate this topic.

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Background Although a number of studies have reported on the health effects of fine particulate matter (PM2.5) exposure, particularly in North American and European countries as well as China, the evidence about intermediate to high levels of PM2.5 exposures is still limited. We aimed to investigate the associations between long-term exposure to PM2.5 and risk of cardiopulmonary disease incidence in Taiwan with intermediate levels of PM2.5 exposure. Methods A cohort of Taiwanese adults, who participated in the 2001, 2005, 2009 and 2013 National Health Interview Surveys, was followed through 2016 to identify cardiopulmonary disease onset. Exposure to PM2.5 was estimated by incorporating a widespread monitoring network of air quality monitoring stations and microsensors. We used time-dependent Cox regression models to examine the associations between the PM2.5 exposures and health outcomes, adjusting for individual characteristics and ecological covariates. The natural cubic spline functions were used to explore the non-linear effects of the PM2.5 exposure. Results A total of 62 694 adults from 353 towns were enrolled. Each 10-μg/m3 increase in 5-year average exposure to PM2.5 was associated with a 4.8% increased risk of incident ischaemic heart disease (95% CI: -3.3, 13.6), 3.9% increased risk of incident stroke (95% CI: -2.9, 11.1), 6.7% increased risk of incident diabetes (95% CI: 1.1, 12.7), 15.7% increased risk of incident lung cancer (95% CI: -0.9, 35.1) and 11.5% increased risk of incident chronic obstructive pulmonary disease (95% CI: -0.8, 25.2). The concentration-response curve showed that there was no statistical evidence of non-linearity for most of the disease outcomes except for ischaemic heart disease (P for non-linearity = 0.014). Conclusions Long-term exposure to intermediate levels of ambient PM2.5 was associated with cardiopulmonary health outcomes. Our study adds value to future application and national burden of disease estimation in evaluating the health co-benefits from ambient air pollution reduction policy in Asian countries.