Opportunistic screening with multiphase contrast-enhanced dual-layer spectral CT for osteoblastic lesions in prostate cancer compared with bone scintigraphy.

Our study aimed to compare bone scintigraphy and dual-layer detector spectral CT (DLCT) with multiphase contrast enhancement for the diagnosis of osteoblastic bone lesions in patients with prostate cancer. The patients with prostate cancer and osteoblastic bone lesions detected on DLCT were divided into positive bone scintigraphy group (pBS) and negative bone scintigraphy group (nBS) based on bone scintigraphy. A total of 106 patients (57 nBS and 49 pBS) was included. The parameters of each lesion were measured from DLCT including Hounsfield unit (HU), 40-140 keV monochromatic HU, effective nuclear numbers (Zeff), and Iodine no water (InW) value in non-contrast phase (N), the arterial phase (A), and venous phase (V). The slope of the spectral curve at 40 and 100 keV, the different values of the parameters between A and N phase (A-N), V and N phase (V-N), and hybrid prediction model with multiparameters were used to differentiate pBS from nBS. Receiver operating characteristic analysis was performed to compare the area under the curve (AUC) for differentiating the pBS group from the nBS group. The value of conventional HU values, slope, and InW in A-N and V-N, and hybrid model were significantly higher in the pBS group than in the nBS group. The hybrid model of all significant parameters had the highest AUC of 0.988, with 95.5% sensitivity and 94.6% specificity. DLCT with arterial contrast enhancement phase has the potential to serve as an opportunistic screening tool for detecting positive osteoblastic bone lesions, corresponding to those identified in bone scintigraphy.

Air pollution as a potential risk factor for hepatocellular carcinoma in Taiwanese patients after adjusting for chronic viral hepatitis.

BACKGROUND: Air pollution is a risk factor for hepatocellular carcinoma (HCC). However, the effect of air pollution on HCC risk in patients with hepatitis remains unclear. METHODS: This cross-sectional study recruited 348 patients with chronic hepatitis who were tested for serum hepatitis B surface antigen (HBsAg) and for antibodies against hepatitis B core antigen (HBcIgG) and hepatitis C virus (anti-HCV) in 2022. The diagnosis of HCC was based on the International Classification of Diseases, 10th revision (ICD-10). Daily estimates of air pollutants were aggregated into mean estimates for the previous year based on the date of recruitment or HCC diagnosis. RESULTS: Out of 348 patients, 12 had HCC (3.4%). Patients with HCC were older (71.7 vs 50.9 years; p = 0.004), had higher proportion of HBsAg seropositivity (41.7% vs 5.1%; p < 0.001), and substantially higher levels of particulate matter 2.5 (PM 2.5 ) (21.5 vs 18.2 µg/m 3 ; p = 0.05). Logistic regression analysis revealed that the factors associated with HCC were age (odds ratio [OR]: 1.10; CI, 1.03-1.17; p = 0.01), PM 2.5 level (OR: 1.51; CI, 1.02-2.23; p = 0.04), and HBsAg seropositivity (OR: 6.60; CI, 1.51-28.85; p = 0.01) ( Table 3 ). There was a combined effect of PM 2.5 and HBsAg seropositivity on the risk of HCC development (OR: 22.17; CI, 3.33-147.45; p = 0.001). CONCLUSION: In this study, we demonstrated that PM 2.5 and HBsAg seropositivity were associated with HCC occurrence and had synergistic effects after adjusting for confounding factors.

Air pollution associate with advanced hepatic fibrosis among patients with chronic liver disease.

We aimed to investigate the association between air pollution and advanced fibrosis among patients with metabolic associated fatty liver disease (MAFLD) and chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections. A total of 1376 participants who were seropositive for HBV surface antigen (HBsAg) or antibodies to HCV (anti-HCV) or had abnormal liver function in a community screening program from 2019 to 2021 were enrolled for the assessment of liver fibrosis using transient elastography. Daily estimates of air pollutants (particulate matter ≤2.5 µm in diameter [PM2.5 ], nitrogen dioxide [NO2 ], ozone [O3 ] and benzene) were aggregated into mean estimates for the previous year based on the date of enrolment. Of the 1376 participants, 767 (52.8%) and 187 (13.6) had MAFLD and advanced fibrosis, respectively. A logistic regression analysis revealed that the factors associated with advanced liver fibrosis were HCV viremia (odds ratio [OR], 3.13; 95% confidence interval [CI], 2.05-4.77; p < 0.001), smoking (OR, 1.79; 95% CI, 1.16-2.74; p = 0.01), age (OR, 1.04; 95% CI, 1.02-1.05; p < 0.001) and PM2.5 (OR, 1.10; 95% CI, 1.05-1.16; p < 0.001). Linear regression analysis revealed that LSM was independently correlated with PM2.5 (ß: 0.134; 95% CI: 0.025, 0.243; p = 0.02). There was a dose-dependent relationship between different fibrotic stages and the PM2.5 level (the PM2.5 level in patients with fibrotic stages 0, 1-2 and 3-4: 27.9, 28.4, and 29.3 µg/m3 , respectively; trend p < 0.001). Exposure to PM2.5 , as well as HBV and HCV infections, is associated with advanced liver fibrosis in patients with MAFLD. There was a dose-dependent correlation between PM2.5 levels and the severity of hepatic fibrosis.

Air pollution impede ALT normalization in chronic hepatitis B patients treated with nucleotide/nucleoside analogues.

Biochemical response is an important prognostic indicator in chronic hepatitis B (CHB) patients receiving nucleotide/nucleoside analogues (NAs). However, the effects of air pollution in alanine aminotransferase (ALT) normalization remain elusive. This longitudinal study recruited 80 hepatitis B e antigen-negative CHB patients who received NAs. ALT levels were measured during the first year of anti-hepatitis B virus therapy. Normal ALT levels were defined as <19 U/L for females and <30 U/L for males, and the risk factors associated with ALT abnormalities were analyzed. The daily estimations of air pollutants (particulate matter ≤2.5 µm in diameter (PM2.5), nitrogen dioxide, ozone (O3), and benzene) were aggregated into the mean estimation for the previous month based on the date of recruitment (baseline) and 1 year later. Sixteen patients (20.0%) had a baseline ALT > 40 U/L; overall, 41 (51.6%) had an abnormal ALT (≥19 U/L for females and ≥ 30 U/L for males). After 1 year of NA therapy, 75 patients (93.8%) had undetectable hepatitis B virus DNA levels. Mean post-treatment ALT levels were significantly lower than mean pretreatment levels (21.3 vs 30.0 U/L, respectively; P < .001). The proportion of patients with a normal ALT was also significantly higher after versus before treatment (71.2% vs 51.2%, respectively; P = .001). The strongest factors associated with ALT abnormality after 1 year of NA treatment were body mass index (odds ratio [OR], 1.28; 95% confidence interval [CI], 1.05-1.54; P = .01) and ozone level (OR, 1.11; 95% CI, 1.02-1.22; P = .02). Among hepatitis B e antigen-negative CHB patients with relatively low viral loads, 1 year of NA treatment improved ALT levels after the adjustment for confounding factors and increased the proportion of patients with normal ALT levels. Air pollution affects the efficacy of ALT normalization.

Quantifying the potential effects of air pollution reduction on population health and health expenditure in Taiwan.

Air pollution, particularly ambient fine particulate matter (PM2.5) pollution, poses a significant risk to public health, underscoring the importance of comprehending the long-term impact on health burden and expenditure at national and subnational levels. Therefore, this study aims to quantify the disease burden and healthcare expenditure associated with PM2.5 exposure in Taiwan and assess the potential benefits of reducing pollution levels. Using a comparative risk assessment framework that integrates an auto-aggressive integrated moving average model, we evaluated the avoidable burden of cardiopulmonary diseases (including ischemic heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, and diabetes mellitus) and related healthcare expenditure under different air quality target scenarios, including status quo and target scenarios of 15, 10, and 5 µg/m3 reduction in PM2.5 concentration. Our findings indicate that reducing PM2.5 exposure has the potential to significantly alleviate the burden of multiple diseases. Comparing the estimated attributable disease burden and healthcare expenditure between reference and target scenarios from 2022 to 2050, the avoidable disability-adjusted life years were 0.61, 1.83, and 3.19 million for the 15, 10, and 5 µg/m3 target scenarios, respectively. Correspondingly, avoidable healthcare expenditure ranged from US$ 0.63 to 3.67 billion. We also highlighted the unequal allocation of resources and the need for policy interventions to address health disparities due to air pollution. Notably, in the 5 µg/m3 target scenario, Kaohsiung City stands to benefit the most, with 527,368 disability-adjusted life years avoided and US$ 0.53 billion saved from 2022 to 2050. Our findings suggest that adopting stricter emission targets can effectively reduce the health burden and associated healthcare expenditure in Taiwan. Overall, this study provides policymakers in Taiwan with valuable insights for mitigating the negative effects of air pollution by establishing a comprehensive framework for evaluating the co-benefits of air pollution reduction on healthcare expenditure and disease burden.

Long-term exposure to ambient fine particulate matter (PM2.5) and associations with cardiopulmonary diseases and lung cancer in Taiwan: a nationwide longitudinal cohort study.

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.

Protein phosphatase 2A inactivation induces microsatellite instability, neoantigen production and immune response.

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.

Effects of short- and long-term exposure to atmospheric pollution on COVID-19 risk and fatality: analysis of the first epidemic wave in northern Italy.

The effects of exposure to atmospheric pollution on the incidence and mortality due to COVID-19 have been studied but not for sulfur dioxide (SO2) 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 PM2.5 and PM10 had a tendency to increase the incidence and mortality of COVID-19 than long-term exposure, while for other air pollutants, including SO2 and NO2, long-term exposure was more significant than short-term exposure. Both short- and long-term exposure of SO2 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.

Estimating ground-level PM2.5 levels in Taiwan using data from air quality monitoring stations and high coverage of microsensors.

A widespread monitoring network of Airbox microsensors was implemented since 2016 to provide high-resolution spatial distributions of ground-level PM2.5 data in Taiwan. We developed models for estimating ground-level PM2.5 concentrations for all the 3 km × 3 km grids in Taiwan by combining the data from air quality monitoring stations and the Airbox sensors. The PM2.5 data from the Airbox sensors (AB-PM2.5) was used to predict daily mean PM2.5 levels at the grids in 2017 using a semiparametric additive model. The estimated PM2.5 level at the grids was further applied as a predictor variable in the models to predict the monthly mean concentration of PM2.5 at all the grids in the previous year. The modeling-predicting procedures were repeated backward for the years from 2016 to 2006. The model results revealed that the model R2 increased from 0.40 to 0.87 when the AB-PM2.5 data were included as a nonlinear component in the model, indicating that AB-PM2.5 is a significant predictor of ground-level PM2.5 concentration. The cross-validation (CV) results demonstrated that the root of mean squared prediction errors of the estimated monthly mean PM2.5 concentrations were smaller than 5 µg/m3 and the R2 of the CV models of 0.79-0.88 during 2006-2017. We concluded that Airbox sensors can be used with monitoring data to more accurately estimate long-term exposure to PM2.5 for cohorts of small areas in health impact assessment studies.

Source apportionment of mass concentration and inhalation risk with long-term ambient PCDD/Fs measurements in an urban area.

This study applies a receptor model to quantify source contributions to ambient concentration of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and inhalation cancer risk in Taipei from 2003 through 2009. Seventeen PCDD/F congeners were used in the effective variance solution to the Chemical Mass Balance equations to estimate source-specific mass contributions and inhalation risks. The average total PCDD/F concentration was 0.611pg/Nm(3) (0.036pg I-TEQ/Nm(3)). Traffic emissions contributed the most to the PCDD/F concentration (55.7%), followed by waste incinerators (18.6%) and joss stick burning (9.6%). For the inhalation cancer risk, the average was 1.1×10(-6) with traffic, waste incinerators, and joss paper burning as the main contributors (67.3%, 19.4%, and 6.3%, respectively). The mass and risk contributions of waste incinerators decreased significantly from 2003 to 2009 and were higher at downwind sites than at upwind sites. Reducing PCDD/F emissions from traffic and waste incinerators would provide the greatest health benefit. Policies that reduce the uncontrolled burning of joss stick and joss paper also need to be implemented.

Association of short-term exposure to fine particulate matter and nitrogen dioxide with acute cardiovascular effects.

This study evaluated whether exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) is associated with cardiovascular effects by examining a panel of 89 healthy subjects in Taipei, Taiwan. The subjects received two health examinations approximately 8months apart in 2013. Brachial-ankle pulse wave velocity (baPWV), a physiological indicator of arterial stiffness, and high-sensitivity C-reactive protein (hsCRP), a biomarker of vascular inflammations, were measured during each examination. Two exposure assessment methods were used for estimating the subjects' exposure to PM2.5 and NO2. The first method involved constructing daily land use regression (LUR) models according to measurements collected at ambient air quality monitoring stations. The second method required combining the LUR estimates with indoor monitoring data at the workplace of the subjects. Linear mixed models were used to examine the association between the exposure estimates and health outcomes. The results showed that a 10-µg/m(3) increase in PM2.5 concentration at a 1-day lag was associated with 2.1% (95% confidence interval: 0.7%-3.6%) and 2.4% (0.8%-4.0%) increases in baPWV based on the two exposure assessment methods, whereas no significant association was observed for NO2. The significant effects of PM2.5 remained in the two-pollutant models. By contrast, NO2, but not PM2.5, was significantly associated with increased hsCRP levels (16.0%-37.3% in single-pollutant models and 26.4%-44.6% in two-pollutant models, per 10-ppb increase in NO2). In conclusion, arterial stiffness might be more sensitive to short-term PM2.5 exposure than is inflammation.

Modeling horizontal and vertical variation in intraurban exposure to PM2.5 concentrations and compositions.

Land use regression (LUR) models are increasingly used to evaluate intraurban variability in population exposure to fine particulate matter (PM2.5). However, most of these models lack information on PM2.5 elemental compositions and vertically distributed samples. The purpose of this study was to evaluate intraurban exposure to PM2.5 concentrations and compositions for populations in an Asian city using LUR models, with special emphasis on examining the effects of having measurements on different building stories. PM2.5 samples were collected at 20 sampling sites below the third story (low-level sites). Additional vertically stratified sampling sites were set up on the fourth to sixth (mid-level sites, n=5) and seventh to ninth (high-level sites, n=5) stories. LUR models were built for PM2.5, copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), sulfur (S), silicon (Si), and zinc (Zn). The explained concentration variance (R(2)) of the PM2.5 model was 65%. R(2) values were >69% in the Cu, Fe, Mn, Ni, Si, and Zn models and <44% in the K and S models. Sampling height from ground level was a significant predictor in the PM2.5 and Si models. This finding stresses the importance of collecting vertically stratified information on PM2.5 mass concentrations to reduce potential exposure misclassification in future health studies. In addition to traffic variables, some models identified gravel-plant, industrial, and port variables with large buffer zones as important predictors, indicating that PM from these sources had significant effects at distant places.