Childhood maltreatment and engaging in NSSI for automatic-negative reinforcement: The mediating role of alexithymia and moderating role of help-seeking attitudes.

BACKGROUND: There is evidence indicating that childhood maltreatment is linked to the occurrence of non-suicidal self-injury (NSSI). Nevertheless, the association between childhood maltreatment and the automatic-negative reinforcement aspect of NSSI remains understudied. Chapman's (2006) experiential avoidance model posits that the main factor in sustaining NSSI is negative reinforcement, specifically through the avoidance or escape from distressful emotional experiences. The current study examines a conceptual framework based on this theory and the available literature that explores the potential mediation role of alexithymia in the relation between childhood maltreatment and the automatic-negative reinforcement of NSSI. Additionally, this study investigates how this process may be influenced by individuals' attitudes toward seeking professional help. METHODS: 3657 adolescents (1616 females) completed questionnaires regarding childhood maltreatment, alexithymia, help-seeking attitudes, the NSSI, and its functions. RESULTS: The findings of the study exposed a positive link between childhood maltreatment and the automatic-negative reinforcement of NSSI, with the mediating role of alexithymia. Interestingly, it was unexpected to discover that individuals with high help-seeking attitudes experienced an intensification of the relationship between childhood maltreatment and both alexithymia and the automatic-negative reinforcement of NSSI. LIMITATION: The study's cross-sectional design hindered the inference of causality. CONCLUSION: The present study demonstrated that it is crucial to consider the impact of both alexithymia and help-seeking attitudes in adolescents who have experienced maltreatment. These findings hold implications for preventive interventions that target the reduction of NSSI behaviors driven by automatic-negative reinforcement.

Public Expenditure and Green Total Factor Productivity: Evidence from Chinese Prefecture-Level Cities.

Whilst effective public expenditure policies are essential for transforming the traditional factor-driven economy into a green and innovation-driven economy, the impacts of public expenditure's size and composition on green economic development have not been comprehensively investigated. This paper attempts to fill this research gap. Based on the data of Chinese prefecture-level cities from 2010 to 2018, we first measure green total factor productivity (GTFP), the proxy variable for green development, and briefly analyze its spatial-temporal trends. Then, using the dynamic panel models, dynamic panel mediation models, and dynamic panel threshold models, we evaluate how public expenditure affects GTFP. The main findings are fourfold: (1) there is a significant inverted U-shaped relationship between the expenditure size and GTFP. (2) The expansion of social expenditures and science and technology (S&T) and environmental protection expenditures play an important role in stimulating green growth, while economic expenditures and administrative expenditures have adverse effects. (3) Public expenditure mainly promotes green development through four channels: human capital accumulation, technological innovation, environmental quality improvement, and labor productivity increase. (4) The expenditure composition influences the turning point of the inverted U-shaped relationship. Based on these findings, we propose some targeted policy suggestions to promote green development.

Behavioral and neurophysiological abnormalities during cued continuous performance tasks in patients with mild traumatic brain injury.

Objective: This study's aim was to investigate the features and neural mechanisms of sustained attention in patients with mild traumatic brain injury (mTBI) by comparing and analyzing neuropsychological, behavioral, event-related potentials, and event-related desynchronization and synchronization between mTBI patients and healthy controls. Methods: Twenty mTBI patients with mTBI and 20 healthy controls underwent the Mini-Mental State Examination (MMSE) and a cued continuous performance task (AX-CPT). Neuropsychological, behavioral, and electroencephalogram (EEG) data were collected and analyzed. Results: There were significant differences between the mTBI group and the control group in their MMSE total scores, attention, and calculation, but there were no significant differences in orientation, memory, recall, and verbal scores. There were significant differences between the mTBI group and the control group in hitting the number, reaction time, and the number of errors of omission, but there were no significant differences in the number of false errors. The amplitude of Go-N2 and Nogo-N2 was significantly smaller for the mTBI group than that for the control group. The amplitude of Go-P3 was significantly smaller for the mTBI group than that for the control group, but not for the amplitude of Nogo-P3. The Go-αERS were significantly less for the mTBI group than for the control group during the 0-200 ms after the stimulus onset. The Go-αERD and Nogo-αERD were significantly less for the mTBI group than for the control group during the 600-1,000 ms after the stimulus onset. The Go-ßERS were significantly less for the mTBI group than for the control group during the 200-400 ms after the stimulus onset. There were no significant differences in the Nogo-αERS and Nogo-ßERD/ERS between the mTBI group and the control group. Conclusion: Patients with mTBI exhibited impairments in sustained attention and conflict monitoring, while response inhibition may have been spared.

Exhaled volatile organic compounds in individuals with a history of high altitude pulmonary edema and varying hypoxia-induced responses.

With ascent to altitude, certain individuals are susceptible to high altitude pulmonary edema (HAPE), which in turn can cause disability and even death. The ability to identify individuals at risk of HAPE prior to ascent is poor. The present study examined the profile of volatile organic compounds (VOC) in exhaled breath condensate (EBC) and pulmonary artery systolic pressures (PASP) before and after exposure to normobaric hypoxia (12% O2) in healthy males with and without a history of HAPE (Hx HAPE, n = 5; Control, n = 11). In addition, hypoxic ventilatory response (HVR), and PASP response to normoxic exercise were also measured. Auto-regression/partial least square regression of whole gas chromatography/mass spectrometry (GC/MS) data and binary logistic regression (BLR) of individual GC peaks and physiologic parameters resulted in models that separate individual subjects into their groups with variable success. The result of BLR analysis highlights HVR, PASP response to hypoxia and the amount of benzyl alcohol and dimethylbenzaldehyde dimethyl in expired breath as markers of HAPE history. These findings indicate the utility of EBC VOC analysis to discriminate between individuals with and without a history of HAPE and identified potential novel biomarkers that correlated with physiological responses to hypoxia.

Analysis of volatile compounds in exhaled breath condensate in patients with severe pulmonary arterial hypertension.

BACKGROUND: An important challenge to pulmonary arterial hypertension (PAH) diagnosis and treatment is early detection of occult pulmonary vascular pathology. Symptoms are frequently confused with other disease entities that lead to inappropriate interventions and allow for progression to advanced states of disease. There is a significant need to develop new markers for early disease detection and management of PAH. METHODOLGY AND FINDINGS: Exhaled breath condensate (EBC) samples were compared from 30 age-matched normal healthy individuals and 27 New York Heart Association functional class III and IV idiopathic pulmonary arterial hypertenion (IPAH) patients, a subgroup of PAH. Volatile organic compounds (VOC) in EBC samples were analyzed using gas chromatography/mass spectrometry (GC/MS). Individual peaks in GC profiles were identified in both groups and correlated with pulmonary hemodynamic and clinical endpoints in the IPAH group. Additionally, GC/MS data were analyzed using autoregression followed by partial least squares regression (AR/PLSR) analysis to discriminate between the IPAH and control groups. After correcting for medicaitons, there were 62 unique compounds in the control group, 32 unique compounds in the IPAH group, and 14 in-common compounds between groups. Peak-by-peak analysis of GC profiles of IPAH group EBC samples identified 6 compounds significantly correlated with pulmonary hemodynamic variables important in IPAH diagnosis. AR/PLSR analysis of GC/MS data resulted in a distinct and identifiable metabolic signature for IPAH patients. CONCLUSIONS: These findings indicate the utility of EBC VOC analysis to discriminate between severe IPAH and a healthy population; additionally, we identified potential novel biomarkers that correlated with IPAH pulmonary hemodynamic variables that may be important in screening for less severe forms IPAH.

Cellular scent of influenza virus infection.

Volatile organic compounds (VOCs) emanating from humans have the potential to revolutionize non-invasive diagnostics. Yet, little is known about how these compounds are generated by complex biological systems, and even less is known about how these compounds are reflective of a particular physiological state. In this proof-of-concept study, we examined VOCs produced directly at the cellular level from B lymphoblastoid cells upon infection with three live influenza virus subtypes: H9N2 (avian), H6N2 (avian), and H1N1 (human). Using a single cell line helped to alleviate some of the complexity and variability when studying VOC production by an entire organism, and it allowed us to discern marked differences in VOC production upon infection of the cells. The patterns of VOCs produced in response to infection were unique for each virus subtype, while several other non-specific VOCs were produced after infections with all three strains. Also, there was a specific time course of VOC release post infection. Among emitted VOCs, production of esters and other oxygenated compounds was particularly notable, and these may be attributed to increased oxidative stress resulting from infection. Elucidating VOC signatures that result from the host cells response to infection may yield an avenue for non-invasive diagnostics and therapy of influenza and other viral infections.

Detection of Huanglongbing disease using differential mobility spectrometry.

The viability of the multibillion dollar global citrus industry is threatened by the "green menace", citrus greening disease (Huanglongbing, HLB), caused by the bacterial pathogen Candidatus Liberibacter. The long asymptomatic stage of HLB makes it challenging to detect emerging regional infections early to limit disease spread. We have established a novel method of disease detection based on chemical analysis of released volatile organic compounds (VOCs) that emanate from infected trees. We found that the biomarkers "fingerprint" is specific to the causal pathogen and could be interpreted using analytical methods such as gas chromatography/mass spectrometry (GC/MS) and gas chromatography/differential mobility spectrometry (GC/DMS). This VOC-based disease detection method has a high accuracy of ∼90% throughout the year, approaching 100% under optimal testing conditions, even at very early stages of infection where other methods are not adequate. Detecting early infection based on VOCs precedes visual symptoms and DNA-based detection techniques (real-time polymerase chain reaction, RT-PCR) and can be performed at a substantially lower cost and with rapid field deployment.

Gene regulatory networks elucidating huanglongbing disease mechanisms.

Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas), especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes) would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation), sucrose metabolism (upregulation), and starch biosynthesis (upregulation). In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70) was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur.

Transcriptome profiling of citrus fruit response to huanglongbing disease.

Huanglongbing (HLB) or "citrus greening" is the most destructive citrus disease worldwide. In this work, we studied host responses of citrus to infection with Candidatus Liberibacter asiaticus (CaLas) using next-generation sequencing technologies. A deep mRNA profile was obtained from peel of healthy and HLB-affected fruit. It was followed by pathway and protein-protein network analysis and quantitative real time PCR analysis of highly regulated genes. We identified differentially regulated pathways and constructed networks that provide a deep insight into the metabolism of affected fruit. Data mining revealed that HLB enhanced transcription of genes involved in the light reactions of photosynthesis and in ATP synthesis. Activation of protein degradation and misfolding processes were observed at the transcriptomic level. Transcripts for heat shock proteins were down-regulated at all disease stages, resulting in further protein misfolding. HLB strongly affected pathways involved in source-sink communication, including sucrose and starch metabolism and hormone synthesis and signaling. Transcription of several genes involved in the synthesis and signal transduction of cytokinins and gibberellins was repressed while that of genes involved in ethylene pathways was induced. CaLas infection triggered a response via both the salicylic acid and jasmonic acid pathways and increased the transcript abundance of several members of the WRKY family of transcription factors. Findings focused on the fruit provide valuable insight to understanding the mechanisms of the HLB-induced fruit disorder and eventually developing methods based on small molecule applications to mitigate its devastating effects on fruit production.

Characterization of volatile organic compounds in human leukocyte antigen heterologous expression systems: a cell's "chemical odor fingerprint".

The major histocompatibility complex (MHC), or human leukocyte antigen (HLA) gene-coding region in humans, plays a significant role in infectious disease response, autoimmunity, and cellular recognition. This super locus is essential in mate selection and kin recognition because of the organism-specific odor which can be perceived by other individuals. However, how the unique MHC genetic combination of an organism correlates with generation of the organism-specific odor is not well understood. In the present work, we have shown that human B-cells produce a set of volatile organic compounds (VOCs) that can be measured by GC-MS. More importantly, our results show that specific HLA alleles are related to production of selected VOCs, and that this leads to a cell-specific odor "fingerprint". We used a C1R HLA class I A and B locus negative cell line, along with C1R cell lines that were stably transfected with specific A and B alleles. Our work demonstrates for the first time that HLA alleles can directly influence production of specific odor compounds at the cellular level. Given that the resulting odor fingerprint depends on expression of specific HLA sequences, it may yield information on unique human scent profiles, composition of exhaled breath, as well as immune response states in future studies.

A modified artificial immune system based pattern recognition approach--an application to clinical diagnostics.

OBJECTIVE: This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. METHODS AND MATERIALS: Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function--partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). RESULTS: The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of "replacement threshold=0.3", the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for study 2 are 0.49% (AIS-RBFPLS) and 6.61% (AIS-kNN); and (3) the proposed AIS-RBFPLS classification approaches also yielded better diagnosis results than two classical neural network approaches of BPNN and Ortho-RBF network. CONCLUSION: In summary, this paper proposed a new machine learning method for complex systems by integrating the AIS system with RBFPLS. This new method demonstrates its satisfactory effect on classification accuracy for clinical diagnosis, and also indicates its wide potential applications to other diagnosis and detection problems.

Analysis of early host responses for asymptomatic disease detection and management of specialty crops.

The rapid and unabated spread of vector-borne diseases within US specialty crops threatens our agriculture, our economy, and the livelihood of growers and farm workers. Early detection of vector-borne pathogens is an essential step for the accurate surveillance and management of vector-borne diseases of specialty crops. Currently, we lack the tools that would detect the infectious agent at early (primary) stages of infection with a high degree of sensitivity and specificity. In this paper, we outline a strategy for developing an integrated suite of platform technologies to enable rapid, early disease detection and diagnosis of huanglongbing (HLB), the most destructive citrus disease. The research has two anticipated outcomes: i) identification of very early, disease-specific biomarkers using a knowledge base of translational genomic information on host and pathogen responses associated with early (asymptomatic) disease development; and ii) development and deployment of novel sensors that capture these and other related biomarkers and aid in presymptomatic disease detection. By combining these two distinct approaches, it should be possible to identify and defend the crop by interdicting pathogen spread prior to the rapid expansion phase of the disease. We believe that similar strategies can also be developed for the surveillance and management of diseases affecting other economically important specialty crops.

Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data.

This paper introduces the ant colony algorithm, a novel swarm intelligence based optimization method, to select appropriate wavelet coefficients from mass spectral data as a new feature selection method for ovarian cancer diagnostics. By determining the proper parameters for the ant colony algorithm (ACA) based searching algorithm, we perform the feature searching process for 100 times with the number of selected features fixed at 5. The results of this study show: (1) the classification accuracy based on the five selected wavelet coefficients can reach up to 100% for all the training, validating and independent testing sets; (2) the eight most popular selected wavelet coefficients of the 100 runs can provide 100% accuracy for the training set, 100% accuracy for the validating set, and 98.8% accuracy for the independent testing set, which suggests the robustness and accuracy of the proposed feature selection method; and (3) the mass spectral data corresponding to the eight popular wavelet coefficients can be located by reverse wavelet transformation and these located mass spectral data still maintain high classification accuracies (100% for the training set, 97.6% for the validating set, and 98.8% for the testing set) and also provide sufficient physical and medical meaning for future ovarian cancer mechanism studies. Furthermore, the corresponding mass spectral data (potential biomarkers) are in good agreement with other studies which have used the same sample set. Together these results suggest this feature extraction strategy will benefit the development of intelligent and real-time spectroscopy instrumentation based diagnosis and monitoring systems.

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

Source apportionment of airborne particulate matter for the Speciation Trends Network site in Cleveland, OH.

Aerosol composition data from the Speciation Trends Network (STN) site (East 14th Street) in Cleveland, OH, were analyzed by advanced receptor model methods for source apportionment as well as by the standard positive matrix factorization (PMF) using PMF2. These different models are used in combination to test model limitations. These data were 24-hr average mass concentrations and compositions obtained for samples taken every third day from 2001 to 2003. The Multilinear Engine (ME) was used to solve an expanded model to estimate the source profiles and source contributions and also to investigate the wind speed, wind direction, time-of-day, weekend/weekday, and seasonal effects. PMF2 was applied to the same dataset. Potential source contribution function (PSCF) and conditional probability function (CPF) analyses were used to locate the regional and local sources using the resolved source contributions and appropriate meteorological data. Very little difference was observed between the results of the expanded model and the PMF2 values for the profiles and source contribution time series. The identified sources were as ferrous smelter, secondary sulfate, secondary nitrate, soil/combustion mixture, steel mill, traffic, wood smoke, and coal burning. The CPF analysis was useful in helping to identify local sources, whereas the PSCF results were only useful for regional source areas. Both of these analyses were more useful than the wind directional factor derived from the expanded factor analysis. However, the expanded analysis provided direct information on seasonality and day-of-week behavior of the sources.

Design-of-experiment optimization of exhaled breath condensate analysis using a miniature differential mobility spectrometer (DMS).

Analytical instruments that can measure small amounts of chemicals in complicated biological samples are often useful as diagnostic tools. However, it can be challenging to optimize these sensors using actual clinical samples, given the heterogeneous background and composition of the test materials. Here we use gas chromatography-differential mobility spectrometry (GC/DMS) to analyze the chemical content of human exhaled breath condensate (EBC). Ultimately, this system can be used for non-invasive disease diagnostics. Many parameters can be adjusted within this instrument system, and we implemented a factorial design-of-experiments to systematically test several combinations of parameter settings while concurrently analyzing effects and interactions. We examined four parameters that affect sensitivity and detection for our instrument, requiring a 2(4) factorial design. We optimized sensor function using EBC samples spiked with acetone, a known clinical biomarker in breath. Two outputs were recorded for each experiment combination: number of chemicals detected, and the amplitude of acetone signal. Our goal is to find the best parameter combination that yields the highest acetone peak while also preserving the largest number of other chemical peaks in the spectra. By optimizing the system, we can conduct further clinical experiments with our sensor more efficiently and accurately.

Machine Learning: A Crucial Tool for Sensor Design.

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.

A study of health effect estimates using competing methods to model personal exposures to ambient PM2.5.

Various methods have been developed recently to estimate personal exposures to ambient particulate matter less than 2.5 microm in diameter (PM2.5) using fixed outdoor monitors as well as personal exposure monitors. One class of estimators involves extrapolating values using ambient-source components of PM2.5, such as sulfate and iron. A key step in extrapolating these values is to correct for differences in infiltration characteristics of the component used in extrapolation (such as sulfate within PM2.5) and PM2.5. When this is not done, resulting health effect estimates will be biased. Another class of approaches involves factor analysis methods such as positive matrix factorization (PMF). Using either an extrapolation or a factor analysis method in conjunction with regression calibration allows one to estimate the direct effects of ambient PM2.5 on health, eliminating bias caused by using fixed outdoor monitors and estimated personal ambient PM2.5 concentrations. Several forms of the extrapolation method are defined, including some new ones. Health effect estimates that result from the use of these methods are compared with those from an expanded PMF analysis using data collected from a health study of asthmatic children conducted in Denver, Colorado. Examining differences in health effect estimates among the various methods using a measure of lung function (forced expiratory volume in 1 s) as the health indicator demonstrated the importance of the correction factor(s) in the extrapolation methods and that PMF yielded results comparable with the extrapolation methods that incorporated correction factors.

Source identification of fine particles in Washington, DC, by expanded factor analysis modeling.

An expanded factor analysis model (ME-2) that is capable of taking into account the influence of independent variables such as wind speed, wind direction, time of year and other variables of the measured fine particle matter (PM2.5) concentration data was utilized for identifying sources of airborne pollutants and providing quantitative estimations of the contribution of each source. The chemical composition data used in this study were obtained from PM2.5 samples collected using the Interagency Monitoring of Protected Visual Environments samplers from August 1999 to December 2001 at an urban monitoring site in Washington, DC. The expanded model has been applied to two different data sets based on the particulate carbon variables. Such an approach had been successfully applied previously and provided improved source resolution in simulated and ambient concentration data. Initially, total OC and EC were used in the expanded model and were compared to the results using conventional positive matrix factorization that had been done previously using the individual carbon fractions data. In the other expanded model analysis, the eight carbon fractions were used during the modeling in order to ascertain if additional source information could be extracted from the data. In both cases, it was possible to separate diesel from spark-ignition vehicles. The use of the individual carbon fractions in the model provides information on what appears to be secondary organic aerosol formation.

On-road exposure to highway aerosols. 2. Exposures of aged, compromised rats.

Ambient particulate pollution is associated with adverse health effects in epidemiological studies of the elderly with cardiopulmonary diseases. We hypothesize that ultrafine particles (UFP) contribute to these effects, especially when they are freshly generated and occur at high number concentrations. Studies to determine adverse effects have been performed using laboratory-generated surrogates, diluted exhaust from stationary engines, or concentrated ambient UFPs. Methodological difficulties exist with such experiments, and questions remain about how well these particles model those found in ambient air. Freshly generated UFPs are present at high concentrations on highways and vehicle passengers are directly exposed to them. We wished to expose rats to these UFPs to test their potential to cause effects. Since such exposures have not been done before, one objective of our study was to demonstrate the feasibility of an on-road exposure study. Secondly, we wished to determine if there are significant exposure-related effects in aged, compromised rats. Old rats (21-mo F-344) were exposed directly on highways to either the aerosol (<1 microm)/gas phase, gas phase only, or filtered air using an on-road exposure system. Some rats were pretreated with a low dose of inhaled endotoxin or with instilled influenza virus to induce lung inflammation. The exposures in compartmentalized whole-body chambers consisted of 6-h driving periods on I-90 between Rochester and Buffalo once or 3 days in a row. Endpoints related to lung inflammation, inflammatory cell activation, and acute-phase responses were measured after exposure. The on-road exposure system did not affect measured endpoints in filtered air-exposed rats, indicating that it was well tolerated by them. We observed the expected increases in response (inflammation, inflammatory cell activation) to the priming agents. We also found a significant particle-associated increase in plasma endothelin-2, suggesting alterations in vascular endothelial cell activation. In addition, we observed main effects of particles related to the acute-phase response and inflammatory-cell activation. Interactions between on-road particles and the priming agents were also found. These results suggest that exposures to on-road particle mixtures have effects on the pulmonary and cardiovascular system in compromised, old rats. Furthermore, they demonstrate that on-road exposures are feasible and could be performed in future studies with more continuous particle exposures.