Análisis espacial de las concentraciones de PM2,5 en Bogotá según los valores de las guías de la calidad del aire de la Organización Mundial de la Salud para enfermedades cardiopulmonares, 2014-2015

Lycium barbarum polysaccharide's protective effects against PM2.5-induced cellular senescence in HUVECs

Fine particulate matter (PM2.5) exposure is strongly associated with vascular endothelial senescence, a process implicated in cardiovascular diseases. While there is existing knowledge on the impact of Lycium barbarum polysaccharide (LBP) on vascular endothelial damage, the protective mechanism of LBP against PM2.5-induced vascular endothelial senescence remains unclear. In this study, we investigated the impact of PM2.5 exposure on vascular endothelial senescence and explored the intervention effects of LBP in human umbilical vein endothelial cells (HUVECs). We found that PM2.5 exposure dose-dependently reduced cell viability and proliferation in HUVECs while increasing the production of reactive oxygen species (ROS), malondialdehyde (MDA), and hydrogen peroxide (H2O2). Additionally, PM2.5 exposure inhibited the activity of superoxide dismutase (SOD). Notably, PM2.5 exposure induced autophagy impairments and cellular senescence. However, LBP mitigated PM2.5-induced cell damage. Further studies demonstrated that correcting autophagy impairment in HUVECs reduced the expression of the senescence markers P16 and P21 induced by PM2.5. This suggests the regulatory role of autophagy in cellular senescence and the potential of LBP in improving HUVECs senescence. These findings provide novel insights into the mechanisms underlying PM2.5-induced cardiovascular toxicity and highlight the potential of LBP as a therapeutic agent for improving vascular endothelial health.

Long-Term Exposure to Ozone and Fine Particulate Matter and Risk of Premature Coronary Artery Disease: Results from Genetics of Atherosclerotic Disease Mexican Study

(1) Background: Epidemiological studies have identified associations between fine particulate matter (PM2.5) and ozone exposure with cardiovascular disease; however, studies linking ambient air pollution and premature coronary artery disease (pCAD) in Latin America are non-existing. (2) Methods: Our study was a case−control analysis nested in the Genetics of Atherosclerotic Disease (GEA) Mexican study. We included 1615 participants (869 controls and 746 patients with pCAD), recruited at the Instituto Nacional de Cardiología Ignacio Chávez from June 2008 to January 2013. We defined pCAD as history of myocardial infarction, angioplasty, revascularization surgery or coronary stenosis > 50% diagnosed before age 55 in men and age 65 in women. Controls were healthy individuals without personal or family history of pCAD and with coronary artery calcification equal to zero. Hourly measurements of ozone and PM2.5 from the Atmospheric Monitoring System in Mexico City (SIMAT in Spanish; Sistema de Monitero Atmosférico de la Ciudad de México) were used to calculate annual exposure to ozone and PM2.5 in the study participants. (3) Results: Each ppb increase in ozone at 1-year, 2-year, 3-year and 5-year averages was significantly associated with increased odds (OR = 1.10; 95% CI: 1.03−1.18; OR = 1.17; 95% CI: 1.05−1.30; OR = 1.18; 95% CI: 1.05−1.33, and OR = 1.13; 95% CI: 1.04−1.23, respectively) of pCAD. We observed higher risk of pCAD for each 5 µg/m3 increase only for the 5-year average of PM2.5 exposure (OR = 2.75; 95% CI: 1.47−5.16), compared to controls. (4) Conclusions: Ozone exposure at different time points and PM2.5 exposure at 5 years were associated with increased odds of pCAD. Our results highlight the importance of reducing long-term exposure to ambient air pollution levels to reduce the burden of cardiovascular disease in Mexico City and other metropolitan areas.

Cokriging Prediction Using as Secondary Variable a Functional Random Field with Application in Environmental Pollution

Cokriging is a geostatistical technique that is used for spatial prediction when realizations of a random field are available. If a secondary variable is cross-correlated with the primary variable, both variables may be employed for prediction by means of cokriging. In this work, we propose a predictive model that is based on cokriging when the secondary variable is functional. As in the ordinary cokriging, a co-regionalized linear model is needed in order to estimate the corresponding auto-correlations and cross-correlations. The proposed model is utilized for predicting the environmental pollution of particulate matter when considering wind speed curves as functional secondary variable.