Associations between long-term ambient PM2.5 exposure and the incidence of cardiopulmonary diseases and diabetes, attributable years lived with disability, and policy implication.

Long-term exposure to ambient PM2.5 is known associated with cardiovascular and respiratory health effects. However, the heterogeneous concentrationresponse function (CRF) between PM2.5 exposure across different concentration range and cardiopulmonary disease and diabetes mellitus (DM) incidence, and their implications on attributable years lived with disability (YLD) and regulation policy has not been well-studied. In this retrospective longitudinal cohort study, disease-free participants (approximately 170,000 individuals, aged ≥ 30 years) from the MJ Health Database were followed up (2007-2017) regarding incidents of coronary heart disease (CHD), ischemic stroke, chronic obstructive pulmonary disease (COPD), lower respiratory tract infections (LRIs), and DM. We used a time-dependent nonlinear weight-transformation Cox regression model for the CRF with an address-matched 3-year mean PM2.5 exposure estimate. Town/district-specific PM2.5-attributable YLD were calculated by multiplying the disease incidence rate, population attributable fraction, disability weight, and sex-age group specific subpopulation for each disease separately. The estimated CRFs for cardiopulmonary diseases were heterogeneously with the hazard ratios (HRs) increased rapidly for CHD and ischemic stroke at PM2.5 concentration lower than 10 µg/m3, whereas the HRs for DM (LRIs) increased with PM2.5 higher than 15 (20) µg/m3. Women had higher HRs for ischemic stroke and DM but not CHD. Relative to the lowest observed PM2.5 concentration of 6 µg/m3 of the study population, the PM2.5 level with an extra risk of 0.1 % (comparable to the disease incidence) for CHD, ischemic stroke, DM, and LRIs were 8.59, 11.85, 22.09, and 24.23 µg/m3, respectively. The associated attributable YLD decreased by 51.4 % with LRIs reduced most (83.6 %), followed by DM (63.7 %) as a result of PM2.5 concentration reduction from 26.10 to 16.82 µg/m3 during 2011-2019 in Taiwan. The proportion of YLD due to CHD and ischemic stroke remained dominant (56.4 %-69.9 %). The cost-benefit analysis for the tradeoff between avoidable YLD and mitigation cost suggested an optimal PM2.5 exposure level at 12 µg/m3. CRFs for cardiopulmonary diseases, attributable YLD, and regulation level, may vary depending on the national/regional background and spatial distribution of PM2.5 concentrations, as well as demographic characteristics.

Salvisertin A, a New Hexacyclic Triterpenoid, and Other Bioactive Terpenes from Salvia deserta Root.

Using various chromatographic methods, a new hexacyclic triterpenoid, 2ß,3ß,24ß-trihydroxy-12,13-cyclotaraxer-l4-en-28oic acid (1), together with ten known compounds, 2α,3α,23-trihydroxyurs-12,20(30)-dien-28oic acid (2), 6,7-dehydroroyleanone (3), horminone (4), 7-O-methylhorminone (5), sugiol (6), demethylcryptojaponol (7), 14-deoxycoleon U (8), 5,6-didehydro-7-hydroxy-taxodone (9), ferruginol (10), and dichroanone (11), were isolated from the roots of Salvia deserta. Their structures were identified on the basis of spectroscopic analysis and comparison with the reported data. The individual compounds (1, 3 - 8) were screened for cytotoxic activity, using the sulforhodamine B bioassay (SRB) method. As the results, Compounds 3, 5, and 8 showed cytotoxic potency against A549, MDA-MB-231, KB, KB-VIN, and MCF7 cell lines with IC50 values ranging from 6.5 to 10.2 µm.

Burden of cardiovascular disease attributable to long-term exposure to ambient PM2.5 concentration and the cost-benefit analysis for the optimal control level.

Environmental exposure to fine particulate matter PM2.5 is known to be associated with many hazardous health effects, including cardiovascular diseases (CVDs). To reduce the related health burden, it is crucial that policy-makers throughout the world set regulation levels according to their own evidence-based study outcomes. However, there appears to be a lack of decision-making methods for the control level of PM2.5 based on the burden of disease. In this study, 117,882 CVD-free participants (≥30-years-old) of the MJ Health Database were followed-up (for a median of 9 years) between 2007 and 2017. Each participant's residential address was matched to the 3× 3 km grid PM2.5 concentration estimates with a 5-year average for long-term exposure. We used a time-dependent nonlinear weight-transformation Cox regression model for the concentration-response function (CRF) between exposure to PM2.5 and CVD incidence. Town/district-specific PM2.5-attributable years of life in disability (YLDs) in CVD incidence were calculated by using the relative risk (RR) of the PM2.5 concentration level relative to the reference level. A cost-benefit analysis was proposed by assessing the trade-off between the gain in avoidable YLDs (given a reference level at u and considering mitigation cost) versus the loss in unavoidable YLDs by not setting at the lowest observed health effect level u0. The CRF varied across different areas with dissimilar PM2.5 exposure ranges. Areas with low PM2.5 concentrations and population sizes provided crucial information for the CVD health effect at the lower end. Additionally, women and older participants were more susceptible. The avoided town/district-specific YLDs in CVD incidence due to lower RRs ranged from 0 to 3000 person-years comparing the PM2.5 concentration levels in 2019 with the levels in 2011. Based on the cost-benefit analysis, an annual PM2.5 concentration of 13 µg/m3 would be optimal, which provides a guideline for the updated regulation level (currently at 15 µg/m3). The proposed cost-benefit analysis method may be applied to other countries/regions for regulation levels that are most suitable for their air pollution status and population health.

Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach.

Fine particulate matter (PM2.5) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM2.5 measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM2.5 estimates in a study area. In this study, we used AOD estimates of surface PM2.5 together with meteorological and land use variables to estimate monthly PM2.5 concentrations at a spatial resolution of 3 km2 over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R2 of 0.98 with a root mean square error (RMSE) of 1.40 µg/m3. The leave-one-out cross-validation (LOOCV) R2 with seasonal stratification was 0.82, and the RMSE was 3.85 µg/m3, whereas the R2 and RMSE obtained by using the pure random forest approach produced R2 and RMSE values of 0.74 and 4.60 µg/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM2.5 estimates over the entire island, which has complex terrain in terms of land use and topography.

Projection of future temperature extremes, related mortality, and adaptation due to climate and population changes in Taiwan.

BACKGROUND: Extreme temperature events have been observed to appear more frequently and with greater intensity in Taiwan in recent decades due to climate change, following the global trend. Projections of temperature extremes across different climate zones and their impacts on related mortality and adaptation have not been well studied. METHODS: We projected site-specific future temperature extremes by statistical downscaling of 8 global climate models followed by Bayesian model averaging from 2021 to 2060 across Taiwan under the representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5. We then calculated the attributable mortality (AM) in 6 municipalities and in the eastern area by multiplying the city/county- and degree-specific relative risk of mortality according to the future population projections. We estimated the degree of adaptation to heat by slope reduction of the projected AM to be comparable with that in 2018. RESULTS: The annual number of hot days with mean temperatures over 30 °C was predicted to have a substantial 2- to 5-fold increase throughout the residential areas of Taiwan by the end of 2060 under RCP8.5, whereas the decrease in cold days was less substantial. The decrease in cold-related mortality below 15 °C was projected to outweigh heat-related mortality for the next two decades, and then heat-related mortality was predicted to drastically increase and cross over cold-related mortality, surpassing it from 2045 to 2055. Adjusting for future population size, the percentage increase in heat-related deaths per 100,000 people could increase by more than 10-fold under the worst scenario (RCP8.5), especially for those over 65 years old. The heat-related impacts will be most severe in southern Taiwan, which has a tropical climate. There is a very high demand for heat-adaptation prior to 2050 under all RCP scenarios. CONCLUSIONS: Spatiotemporal variations in AM in cities in different climate zones are projected in Taiwan and are expected to have a net negative effect in the near future before shifting to a net positive effect from 2045 to 2055. However, there is an overall positive and increasing trend of net effect for elderly individuals under all the emission scenarios. Active adaptation plans need to be well developed to face future challenges due to climate change, especially for the elderly population in central and southern Taiwan.

New phenolic acids from Salvia yunnanensis C.H.Wright.

Two new phenolic acids, ethyl pro-lithospermate (1), n-butyl pro-lithospermate (2) were isolated from Salvia yunnanensis C.H.Wright, along with nineteen known compounds (3-21). The structures of the isolated compounds were elucidated on the basis of extensive spectrometry and by comparing their physical and spectroscopic data to the literature. Among them, compounds 11, 12 and 14-16 were firstly isolated from S. yunnanensis C.H.Wright. Some of the isolated compounds were evaluated for their neuroprotection. Compounds 10-12 showed significant neuroprotective effects in PC12 cells and compounds 1, 4-7 displayed moderate neuroprotective effects.