Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure.
Ontology highlight
ABSTRACT:
SUBMITTER: Kebalepile MM
PROVIDER: S-EPMC8582892 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundExposure to fine particulate matter (PM2.5) increases the risk of asthma exacerbations, and thus, monitoring personal exposure to PM2.5 may aid in disease self-management. Low-cost, portable air pollution sensors offer a convenient way to measure personal pollution exposure directly and may improve personalized monitoring compared with traditional methods that rely on stationary monitoring stations. We aimed to understand whether adults with asthma would be willing to use personal sensors to monitor their exposure to air pollution and to assess the feasibility of using sensors to measure real-time PM2.5 exposure.MethodsWe conducted semi-structured interviews with 15 adults with asthma to understand their willingness to use a personal pollution sensor and their privacy preferences with regard to sensor data. Student research assistants used HabitatMap AirBeam devices to take PM2.5 measurements at 1-s intervals while walking in Philadelphia neighborhoods in May-August 2018. AirBeam PM2.5 measurements were compared to concurrent measurements taken by three nearby regulatory monitors.ResultsAll interview participants stated that they would use a personal air pollution sensor, though the consensus was that devices should be small (watch- or palm-sized) and light. Patients were generally unconcerned about privacy or sharing their GPS location, with only two stating they would not share their GPS location under any circumstances. PM2.5 measurements were taken using AirBeam sensors on 34 walks that extended through five Philadelphia neighborhoods. The range of sensor PM2.5 measurements was 0.6-97.6 μg/mL (mean 6.8 μg/mL), compared to 0-22.6 μg/mL (mean 9.0 μg/mL) measured by nearby regulatory monitors. Compared to stationary measurements, which were only available as 1-h integrated averages at discrete monitoring sites, sensor measurements permitted characterization of fine-scale fluctuations in PM2.5 levels over time and space.ConclusionsPatients were generally interested in using sensors to monitor their personal exposure to PM2.5 and willing to share personal sensor data with health care providers and researchers. Compared to traditional methods of personal exposure assessment, sensors captured personalized air quality information at higher spatiotemporal resolution. Improvements to currently available sensors, including more reliable Bluetooth connectivity, increased portability, and longer battery life would facilitate their use in a general patient population.
| S-EPMC8420032 | biostudies-literature
Project description:BACKGROUND:Early-life exposure to traffic-related air pollution exacerbates childhood asthma, but it is unclear what role it plays in asthma development. METHODS:The association between exposure to primary mobile source pollutants during pregnancy and during infancy and asthma incidence by ages 2 through 6 was examined in the Kaiser Air Pollution and Pediatric Asthma Study, a racially diverse birth cohort of 24,608 children born between 2000 and 2010 and insured by Kaiser Permanente Georgia. We estimated concentrations of mobile source fine particulate matter (PM2.5, µg/m), nitrogen oxides (NOX, ppb), and carbon monoxide (CO, ppm) at the maternal and child residence using a Research LINE source dispersion model for near-surface releases. Asthma was defined using diagnoses and medication dispensings from medical records. We used binomial generalized linear regression to model the impact of exposure continuously and by quintiles on asthma risk. RESULTS:Controlling for covariates and modeling log-transformed exposure, a 2.7-fold increase in first year of life PM2.5 was associated with an absolute 4.1% (95% confidence interval, 1.6%, 6.6%) increase in risk of asthma by age 5. Quintile analysis showed an increase in risk from the first to second quintile, but similar risk across quintiles 2-5. Risk differences increased with follow-up age. Results were similar for NOX and CO and for exposure during pregnancy and the first year of life owing to high correlation. CONCLUSIONS:Results provide limited evidence for an association of early-life mobile source air pollution with childhood asthma incidence with a steeper concentration-response relationship observed at lower levels of exposure.
| S-EPMC5718963 | biostudies-literature
Project description:PURPOSE:Maternal asthma increases adverse neonatal respiratory outcomes, and pollution may further increase risk. Air quality in relation to neonatal respiratory health has not been studied. METHODS:Transient tachypnea of the newborn (TTN), asphyxia, and respiratory distress syndrome (RDS) were identified using medical records among 223,375 singletons from the Consortium on Safe Labor (2002-2008). Community Multiscale Air Quality models estimated pollutant exposures. Multipollutant Poisson regression models calculated adjusted relative risks of outcomes for interquartile range increases in average exposure. Maternal asthma and preterm delivery were evaluated as effect modifiers. RESULTS:TTN risk increased after particulate matter (PM) less than or equal to 10-micron exposure during preconception and trimester one (9-10%), and whole-pregnancy exposure to PM less than or equal to 2.5 microns (PM2.5; 17%) and carbon monoxide (CO; 10%). Asphyxia risk increased after exposure to PM2.5 in trimester one (48%) and whole pregnancy (84%), CO in trimester two and whole pregnancy (28-32%), and consistently for ozone (34%-73%). RDS risk was associated with increased concentrations of nitrogen oxides (33%-42%) and ozone (9%-21%) during all pregnancy windows. Inverse associations were observed with several pollutants, particularly sulfur dioxide. No interaction with maternal asthma was observed. Restriction to term births yielded similar results. CONCLUSIONS:Several pollutants appear to increase neonatal respiratory outcome risks.
| S-EPMC6232679 | biostudies-literature
Project description:Maternal asthma and air pollutants have been independently associated with preeclampsia but rarely studied together. Our objective was to comprehensively evaluate preeclampsia risk based on the interaction of maternal asthma and air pollutants. Preeclampsia and asthma diagnoses, demographic and clinical data came from electronic medical records for 210,508 singleton deliveries. Modified Community Multiscale Air Quality models estimated preconception, first and second trimester and whole pregnancy exposure to: particulate matter (PM)<2.5 and <10µm, ozone, nitrogen oxides (NOx), sulfur dioxide (SO2) and carbon monoxide (CO); PM2.5 constituents; volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs). Asthma-pollutant interaction adjusted relative risks (RR) and 95% confidence intervals (CI) for preeclampsia were calculated by interquartile range for criteria pollutants and high exposure (?75th percentile) for PAHs and VOCs. Asthmatics had higher risk associated with first trimester NOx and SO2 and whole pregnancy elemental carbon (EC) exposure than non-asthmatics, but only EC significantly increased risk (RR=1.11, CI:1.03-1.21). Asthmatics also had a 10% increased risk associated with second trimester CO. Significant interactions were observed for nearly all VOCs and asthmatics had higher risk during all time windows for benzene, ethylbenzene, m-xylene, o-xylene, p-xylene and toluene while most PAHs did not increase risk.
| S-EPMC4874861 | biostudies-literature
Project description:Exposure to traffic-related air pollution (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant as large segments of the global population resides in zones that are most impacted by TRAP and schools are often located in high TRAP exposure areas. Recent findings shed new light on the epigenetic mechanisms by which exposure to traffic pollution may contribute to the development and persistence of asthma. In order to delineate TRAP induced effects on the epigenome, utilization of newly available innovative methods to assess and quantify traffic pollution will be needed to accurately quantify exposure. This review will summarize the most recent findings in each of these areas. Although there is considerable evidence that TRAP plays a role in asthma, heterogeneity in both the definitions of TRAP exposure and asthma outcomes has led to confusion in the field. Novel information regarding molecular characterization of asthma phenotypes, TRAP exposure assessment methods, and epigenetics are revolutionizing the field. Application of these new findings will accelerate the field and the development of new strategies for interventions to combat TRAP-induced asthma.
| S-EPMC5069789 | biostudies-literature
Project description:EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-directed embedding enrichment, and demonstrate that it works well even with manifold-learning techniques other than the self-organizing maps. Using this generalization, we introduce an inwards-growing variant of self-organizing maps that is designed to mitigate some earlier identified deficiencies of EmbedSOM output. Finally, we measure the performance of the generalized EmbedSOM, compare several variants of the algorithm that utilize different landmark-generating functions, and showcase the functionality on single-cell cytometry datasets from recent studies.
| S-EPMC7255855 | biostudies-literature
Project description:Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country's measures, which were implemented to contain the virus' spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus' spread in these cities, states, and regions.
| S-EPMC7999572 | biostudies-literature
Project description:BackgroundAmbient air pollutants may increase preterm birth (PTB) risk, but critical exposure windows are uncertain. The interaction of asthma and pollutant exposure is rarely studied.ObjectiveWe sought to assess the interaction of maternal asthma and air pollutant exposures in relation to PTB risk.MethodsElectronic medical records for 223,502 US deliveries were linked with modified Community Multiscale Air Quality model outputs. Logistic regression with generalized estimating equations estimated the odds ratio and 95% CIs for PTB on the basis of the interaction of maternal asthma and particulate matter with aerodynamic diameter of less than 2.5 microns and particulate matter with aerodynamic diameter of less than 10 microns, ozone (O3), nitrogen oxides (NOx), sulfur dioxide (SO2), and carbon monoxide (CO) per interquartile range. For each gestational week 23 to 36, exposures among women who delivered were compared with those remaining pregnant. Three-month preconception, whole pregnancy, weeks 1 to 28, and the last 6 weeks of gestation averages were also evaluated.ResultsOn assessing PTB by gestational week, we found that significant asthma interactions were sporadic before 30 weeks but more common during weeks 34 to 36, with higher risk among mothers with asthma for NOx, CO, and SO2 exposure and an inverse association with O3 in week 34. Odds of PTB were significantly higher among women with asthma for CO and NOx exposure preconception and early in pregnancy. In the last 6 weeks of pregnancy, PTB risk associated with particulate matter with aerodynamic diameter of less than 10 microns was higher among women with asthma.ConclusionsMothers with asthma may experience a higher risk for PTB after exposure to traffic-related pollutants such as CO and NOx, particularly for exposures 3-months preconception and in the early weeks of pregnancy.
| S-EPMC4975980 | biostudies-literature
Project description:Rationale: House endotoxin and ambient air pollution are risk factors for asthma; however, the effects of their coexposure on asthma are not well characterized.Objectives: To examine potential synergistic associations of coexposure to house dust endotoxin and ambient air pollutants with asthma outcomes.Methods: We analyzed data of 6,488 participants in the National Health and Nutrition Examination Survey 2005-2006. Dust from bedding and bedroom floor was analyzed for endotoxin content. The Community Multiscale Air Quality Modeling System (CMAQ) and Downscaler Model data were used to determine annual average particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) exposures at participants' residential locations. The associations of the coexposures with asthma outcomes were assessed and tested for synergistic interaction.Measurements and Main Results: In adjusted analysis, PM2.5 (CMAQ) (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.07-1.18), O3 (Downscaler Model) (OR, 1.07; 95% CI, 1.02-1.13), and log10 NO2 (CMAQ) (OR, 3.15; 95% CI, 1.33-7.45) were positively associated with emergency room visits for asthma in the past 12 months. Coexposure to elevated concentrations of house dust endotoxin and PM2.5 (CMAQ) was synergistically associated with the outcome, increasing the odds by fivefold (OR, 5.01; 95% CI, 2.54-9.87). A synergistic association was also found for coexposure to higher concentrations of endotoxin and NO2 in children (OR, 3.45; 95% CI, 1.65-7.18).Conclusions: Coexposure to elevated concentrations of residential endotoxin and ambient PM2.5 in all participants and NO2 in children is synergistically associated with increased emergency room visits for asthma. Therefore, decreasing exposure to both endotoxin and air pollution may help reduce asthma morbidity.
| S-EPMC6775869 | biostudies-literature
Project description:An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.
| S-EPMC4793242 | biostudies-other