scholarly journals Neurotoxicants Are in the Air: Convergence of Human, Animal, andIn VitroStudies on the Effects of Air Pollution on the Brain

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Lucio G. Costa ◽  
Toby B. Cole ◽  
Jacki Coburn ◽  
Yu-Chi Chang ◽  
Khoi Dao ◽  
...  
Keyword(s):  
Air Pollution ◽  
Animal Studies ◽  
Epidemiological Studies ◽  
Major Constituent ◽  
Behavioral Abnormalities ◽  
Spectrum Disorders ◽  
Ultrafine Particulate Matter ◽  
Pollution Exposure ◽  
The Brain

In addition to increased morbidity and mortality caused by respiratory and cardiovascular diseases, air pollution may also negatively affect the brain and contribute to central nervous system diseases. Air pollution is a mixture comprised of several components, of which ultrafine particulate matter (UFPM; <100 nm) is of much concern, as these particles can enter the circulation and distribute to most organs, including the brain. A major constituent of ambient UFPM is represented by traffic-related air pollution, mostly ascribed to diesel exhaust (DE). Human epidemiological studies and controlled animal studies have shown that exposure to air pollution may lead to neurotoxicity. In addition to a variety of behavioral abnormalities, two prominent effects caused by air pollution are oxidative stress and neuroinflammation, which are seen in both humans and animals and are confirmed byin vitrostudies. Among factors which can affect neurotoxic outcomes, age is considered the most relevant. Human and animal studies suggest that air pollution (and DE) may cause developmental neurotoxicity and may contribute to the etiology of neurodevelopmental disorders, including autistic spectrum disorders. In addition, air pollution exposure has been associated with increased expression of markers of neurodegenerative disease pathologies.

scholarly journals Antidiabetic Properties of Naringenin: A Citrus Fruit Polyphenol

Biomolecules ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 99 ◽  
Author(s):  
Danja J. Den Hartogh ◽  
Evangelia Tsiani
Keyword(s):  
Insulin Resistance ◽  
Animal Studies ◽  
Citrus Fruit ◽  
Epidemiological Studies ◽  
Personal Health ◽  
Current Review ◽  
Vegetables And Fruits

Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by insulin resistance and hyperglycemia and is associated with personal health and global economic burdens. Current strategies/approaches of insulin resistance and T2DM prevention and treatment are lacking in efficacy resulting in the need for new preventative and targeted therapies. In recent years, epidemiological studies have suggested that diets rich in vegetables and fruits are associated with health benefits including protection against insulin resistance and T2DM. Naringenin, a citrus flavanone, has been reported to have antioxidant, anti-inflammatory, hepatoprotective, nephroprotective, immunomodulatory and antidiabetic properties. The current review summarizes the existing in vitro and in vivo animal studies examining the anti-diabetic effects of naringenin.

scholarly journals Fuzzy logic in air pollution: Revisited

2014 ◽  
Vol 2 ◽  
pp. 1-5
Author(s):  
A. Deshpande
Keyword(s):  
Air Pollution ◽  
Fuzzy Logic ◽  
Air Quality ◽  
Epistemic Uncertainty ◽  
Similarity Measures ◽  
Epidemiological Studies ◽  
Fuzzy Similarity ◽  
Parametric Data ◽  
Numeric Data ◽  
Pollution Exposure

In everyday life and field, people mostly deal with concepts that involve factors that defy classification into crisp sets. The decisions people usually make are perceptions without rigorous analysis of numeric data. Like in other field of studies, there may exist imprecision in air quality parametric data collected and in the perception made by air quality experts in defining these parameters in linguistic terms such as: very good, good, poor. This is the reason why over the past few decades, soft computing tools such as fuzzy logic based methods, neural networks, and genetic algorithms have had significant and growing impacts to deal with aleatory as well as epistemic uncertainty in air quality related issues. This paper has highlighted mathematical preliminaries of air pollution studies like Similarity Measures (Cosine Amplitude Method), Fuzzy to Crisp Conversion (Alpha cut method), Fuzzy c Mean Clustering, Zadeh-Deshpande (ZD) Approach and linguistic description of air quality. Similarly, the applications of fuzzy similarity measures and fuzzy c mean clustering with defined possibility (- cut) levels in case air pollution studies for Delhi, India have been reflected. Though the approach of using fuzzy logic in pollution studies are not of common practice, the comprehensive approach that involves air pollution exposure surveys, toxicological data, and epidemiological studies coupled with fuzzy modeling will go a long way toward resolving some of the divisiveness and controversy in the current regulatory paradigm.

scholarly journals Machine Learning for Epidemiological Analysis in The Industrial Area for a Sustainable Life

2019 ◽  
Vol 9 (2S2) ◽  
pp. 1017-1025
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Industrial Area ◽  
Epidemiological Studies ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Air Pollution Exposure ◽  
Pollution Exposure

Pollution exposure and human health in the industry contaminated area are always a concern. The need for industrialization urges to concentrate on sustainable life of residents in the vicinity of the industrial area rather than opposing the industrialists. Literature in epidemiological studies reveal that air pollution is one of the major problems for health risks faced by residents in the industrial area. Main pollutants in industry related air pollution are particulate matter (PM2.5, PM10), SO2 , NO2 , and other pollutants upon the industry. Data for epidemiological studies obtained from different sources which are limited to public access include residents’ sociodemographic characters, health problems, and air quality index for personal exposure to pollutants. This combined data and limited resources make the analysis more complex so that statistical methods cannot compensate. Our review finds that there is an increase in literature that evaluates the connection between ambient air pollution exposure and associated health events of residents in the industrially polluted area using statistical methods, mainly regression models. A very few applies machine learning techniques to figure out the impact of common air pollution exposure on human health. Most of the machine learning approach to epidemiological studies end up in air pollution exposure monitoring, not to correlate its association with diseases. A machine learning approach to epidemiological studies can automatically characterize the residents’ exposure to pollutants and its associated health effects. Uniqueness of the model depends on the appropriate exhaustive data that characterizes the features, and machine learning algorithm used to build the model. In this contribution, we discuss various existing approaches that evaluate residents’ health effects and the source of irritation in association with air pollution exposure, focuses machine learning techniques and mathematical background for epidemiological studies for residents’ sustainable life.

scholarly journals Ambient air pollution and endocrinologic disorders in childhood

2021 ◽  
Vol 26 (3) ◽  
pp. 158-170
Author(s):  
You Joung Heo ◽  
Hae Soon Kim
Keyword(s):  
Air Pollution ◽  
Animal Studies ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Placental Development ◽  
Mortality And Morbidity ◽  
Epigenetic Modulation ◽  
Childhood Type 1 Diabetes ◽  
Study Designs ◽  
Pollution Exposure

Ambient air pollution has been proposed as an important environmental risk factor that increases global mortality and morbidity. Over the past decade, several human and animal studies have reported an association between exposure to air pollution and altered metabolic and endocrine systems in children. However, the results for these studies were mixed and inconclusive and did not demonstrate causality because different outcomes were observed due to different study designs, exposure periods, and methodologies for exposure measurements. Current proposed mechanisms include altered immune response, oxidative stress, neuroinflammation, inadequate placental development, and epigenetic modulation. In this review, we summarized the results of previous pediatric studies that reported effects of prenatal and postnatal air pollution exposure on childhood type 1 diabetes mellitus, obesity, insulin resistance, thyroid dysfunction, and timing of pubertal onset, along with underlying related mechanisms.

scholarly journals Air pollution and childhood obesity

2020 ◽  
Vol 63 (10) ◽  
pp. 382-388
Author(s):  
Moon Young Seo ◽  
Shin-Hye Kim ◽  
Mi Jung Park
Keyword(s):  
Air Pollution ◽  
Childhood Obesity ◽  
Physical Inactivity ◽  
Animal Studies ◽  
Epidemiologic Studies ◽  
Health Concern ◽  
Postnatal Exposure

Childhood obesity is a global health concern. Air pollution is also a crucial health threat, especially in developing countries. Over the past decade, a number of epidemiologic and animal studies have suggested a possible role of pre- or postnatal exposure to air pollutants on childhood obesity. Although no clear mechanism has been elucidated, physical inactivity, oxidative stress, and epigenetic modifications have been suggested as possible mechanisms by which obesity develops due to air pollution. In this review, we summarize and review previous epidemiologic studies linking air pollution and childhood obesity and discuss the possible mechanisms underlying air pollution-induced obesity based on in vivo and in vitro evidence.

scholarly journals Ambient Air Pollution Exposure and Fecundability in Women Undergoing In Vitro Fertilization

2019 ◽  
Vol 3 (1) ◽  
pp. e036 ◽  
Author(s):  
Sabah M. Quraishi ◽  
Paul C. Lin ◽  
Kevin S. Richter ◽  
Mary D. Hinckley ◽  
Bill Yee ◽  
...  
Keyword(s):  
Air Pollution ◽  
In Vitro Fertilization ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Vitro Fertilization ◽  
Air Pollution Exposure ◽  
Pollution Exposure

scholarly journals Shoulder Risk Assessment Based on Fatigue Failure Theory

2019 ◽  
Vol 63 (1) ◽  
pp. 1117-1121
Author(s):  
Dania Bani Hani ◽  
Sean Gallagher ◽  
Richard F. Sesek ◽  
Rong Huangfu ◽  
Mark C. Schall ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fatigue Failure ◽  
Animal Studies ◽  
Failure Process ◽  
Epidemiological Studies ◽  
Cumulative Damage ◽  
Assessment Model ◽  
Failure Theory ◽  
Epidemiological Database

Recent studies support the notion that a fatigue failure process may be responsible for the development of MSDs, including epidemiological studies, animal studies, and in vitro testing of musculoskeletal tissues. This study presents a new risk assessment model for the shoulder, which estimates the daily dose of the cumulative damage (CD) for the shoulder and allows the CD for multiple tasks to be summed to get an overall estimate for the daily cumulative damage. Videotapes of jobs from an existing epidemiological study from a large U.S. automotive manufacturer were analyzed to get exposure information required for the model. The model was then validated using outcomes from the epidemiological database. Logistic regression was used to assess the associations between Log CD and various shoulder outcomes. Results indicated that the cumulative damage for the shoulder was highly associated with all shoulder outcomes and that application of the fatigue failure methods also works extremely well in assessing the probability of association with shoulder outcomes. These results provide further support regarding the role of the fatigue failure process in the development of MSDs.

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