Air Pollution and Cardiovascular Disease: A Review
Bibliographic record
Abstract
Air pollution is comprised of different compounds and particulate matter (PM) of sizes 2.5 and 10 μm, with the former size posing the greatest danger to humans. Evidence suggests that the global rise in air pollution levels during the past century is correlated with the increased incidence of diseases of the cardiovascular system. On a global scale, 7 million individuals died as a result of the effects of air pollution in 2012. Air pollution leads to tremendous amounts of financial burden (in 2010, $16 trillion in the US and Europe) on the health-care system. The severity of effects experienced by varying populations due to air pollution can differ due to locale, length of exposure, weather conditions, residential proximity to major highways or factories, and soil aridity. Pollutants affect the heart, blood vessels, and blood at a molecular level through proinflammatory or oxidative stress response, autonomic nervous system imbalance, and the direct permeation of harmful compounds into the tissue. The dysfunction of cells and biological processes of the cardiovascular system due to PM leads to an increased prevalence of cardiovascular diseases (CVDs) such as atherosclerosis, hypertension, myocardial infarction, thrombosis, and restricted valve motion. Studies in countries such as China have shown an increase of 0.25% in ischemic heart disease (IHD) mortality and a 0.27% increase in IHD morbidity due to a 10 μg/m3 increase in PM. In a study conducted in the US, PM2.5 concentrations ranged from 9.2-22.6 μg/m3, and every 5-μg/m3 increase in PM2.5 caused coronary calcification to increase by 4.1 Agatston units/yr. Studies on traffic-related air pollution found that nonhypertensive participants residing within 100 m of major roadways experienced an increase in systolic (0.35 mmHg) and diastolic (0.22 mmHg) blood pressure as a result of increases in traffic. The progression of CVD due to pollution has been found to fluctuate within individuals based on age, gender, location of exercise, smoking, pregnancy, diabetes, preexisting cardiovascular or pulmonary diseases, and other factors. Considering the number of individuals affected by pollution on a daily basis and the burden that this places on society through the health-care system, immediate preventive measures are needed to address these problems. Increased knowledge about the widespread effects of pollution on human physiological systems should aid in remediating the problem across the globe. Biomedical engineers can have a great positive impact in developing better instrumentation to measure discrete pollutants and characterizing their harmful effects on physiological systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".