A Metabolomics Assay to Diagnose Citrus Huanglongbing Disease and to Aid in Assessment of Treatments to Prevent or Cure Infection.

Citrus greening disease, or Huanglongbing (HLB), has devastated citrus crops globally in recent years. The causal bacterium, 'Candidatus Liberibacter asiaticus', presents a sampling issue for qPCR diagnostics and results in a high false negative rate. In this work, we compared six metabolomics assays to identify HLB-infected citrus trees from leaf tissue extracted from 30 control and 30 HLB-infected trees. A liquid chromatography-mass spectrometry-based assay was most accurate. A final partial least squares-discriminant analysis (PLS-DA) model was trained and validated on 690 leaf samples with corresponding qPCR measures from three citrus varieties (Rio Red grapefruit, Hamlin sweet orange, and Valencia sweet orange) from orchards in Florida and Texas. Trees were naturally infected with HLB transmitted by the insect vector Diaphorina citri. In a randomized validation set, the assay was 99.9% accurate to classify diseased from nondiseased samples. This model was applied to samples from trees receiving plant defense-inducer compounds or biological treatments to prevent or cure HLB infection. From two trials, HLB-related metabolite abundances and PLS-DA scores were tracked longitudinally and compared with those of control trees. We demonstrate how our assay can assess tree health and the efficacy of HLB treatments and conclude that no trialed treatment was efficacious.

Data-Driven Approach to Modeling Microfabricated Chemical Sensor Manufacturing.

We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (µPC) performance when manufactured at scale for chemical and biochemical analysis of volatile organic compounds (VOCs). To test the proposed model, a medium-sized university-led production batch of 30 wafers of chips were subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process step leading to the final functional chemical sensor chip. We implemented a principal component analysis (PCA) model to score individual chip chemical performance, and we observed that the first two principal components represent 74.28% of chemical testing variance with 111 of 118 viable chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing parameters and steps. In our analysis, we find the amount of sorbent mass present in the chip (variable importance score = 2.6) and heater and the RTD resistance values (variable importance score = 1.1) to be the manufacturing parameters with the greatest impact on chemical performance. Other non-obvious latent manufacturing parameters also had quantified influence. Statistical distributions for each manufacturing step will allow future large-scale production runs to be statistically sampled during production to perform QA/QC in a real-time environment. We report this study as the first data-driven, model-based production of a microfabricated chemical sensor.

Characterization of the microbiome and volatile compounds in anal gland secretions from domestic cats (Felis catus) using metagenomics and metabolomics.

Many mammals rely on volatile organic chemical compounds (VOCs) produced by bacteria for their communication and behavior, though little is known about the exact molecular mechanisms or bacterial species that are responsible. We used metagenomic sequencing, mass-spectrometry based metabolomics, and culturing to profile the microbial and volatile chemical constituents of anal gland secretions in twenty-three domestic cats (Felis catus), in attempts to identify organisms potentially involved in host odor production. We found that the anal gland microbiome was dominated by bacteria in the genera Corynebacterium, Bacteroides, Proteus, Lactobacillus, and Streptococcus, and showed striking variation among individual cats. Microbiome profiles also varied with host age and obesity. Metabolites such as fatty-acids, ketones, aldehydes and alcohols were detected in glandular secretions. Overall, microbiome and metabolome profiles were modestly correlated (r = 0.17), indicating that a relationship exists between the bacteria in the gland and the metabolites produced in the gland. Functional analyses revealed the presence of genes predicted to code for enzymes involved in VOC metabolism such as dehydrogenases, reductases, and decarboxylases. From metagenomic data, we generated 85 high-quality metagenome assembled genomes (MAGs). Of importance were four MAGs classified as Corynebacterium frankenforstense, Proteus mirabilis, Lactobacillus johnsonii, and Bacteroides fragilis. They represent strong candidates for further investigation of the mechanisms of volatile synthesis and scent production in the mammalian anal gland.

Oxylipin concentration shift in exhaled breath condensate (EBC) of SARS-CoV-2 infected patients.

Infection of airway epithelial cells with severe acute respiratory coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and lung injury with hypoxia. It is challenging to sample the lower airways non-invasively and the capability to identify a highly representative specimen that can be collected in a non-invasive way would provide opportunities to investigate metabolomic consequences of COVID-19 disease. In the present study, we performed a targeted metabolomic approach using liquid chromatography coupled with high resolution chromatography (LC-MS) on exhaled breath condensate (EBC) collected from hospitalized COVID-19 patients (COVID+) and negative controls, both non-hospitalized and hospitalized for other reasons (COVID-). We were able to noninvasively identify and quantify inflammatory oxylipin shifts and dysregulation that may ultimately be used to monitor COVID-19 disease progression or severity and response to therapy. We also expected EBC-based biochemical oxylipin changes associated with COVID-19 host response to infection. The results indicated ten targeted oxylipins showing significative differences between SAR-CoV-2 infected EBC samples and negative control subjects. These compounds were prostaglandins A2 and D2, LXA4, 5-HETE, 12-HETE, 15-HETE, 5-HEPE, 9-HODE, 13-oxoODE and 19(20)-EpDPA, which are associated with specific pathways (i.e. P450, COX, 15-LOX) related to inflammatory and oxidative stress processes. Moreover, all these compounds were up-regulated by COVID+, meaning their concentrations were higher in subjects with SAR-CoV-2 infection. Given that many COVID-19 symptoms are inflammatory in nature, this is interesting insight into the pathophysiology of the disease. Breath monitoring of these and other EBC metabolites presents an interesting opportunity to monitor key indicators of disease progression and severity.

Portable chemical detection platform for on-site monitoring of odorant levels in natural gas.

The adequate odorization of natural gas is critical to identify gas leaks and to reduce accidents. To ensure odorization, natural gas utility companies collect samples to be processed at core facilities or a trained human technician smells a diluted natural gas sample. In this work, we report a detection platform that addresses the lack of mobile solutions capable of providing quantitative analysis of mercaptans, a class of compounds used to odorize natural gas. Detailed description of the platform hardware and software components is provided. Designed to be portable, the platform hardware facilitates extraction of mercaptans from natural gas, separation of individual mercaptan species, and quantification of odorant concentration, with results reported at point-of-sampling. The software was developed to accommodate skilled users as well as minimally trained operators. Detection and quantification of six commonly used mercaptan compounds (ethyl mercaptan, dimethyl sulfide, n-propylmercaptan, isopropyl mercaptan, tert­butyl mercaptan, and tetrahydrothiophene) at typical odorizing concentrations of 0.1-5 ppm was performed using the device. We demonstrate the potential of this technology to ensure natural gas odorizing concentrations throughout distribution systems.

Active sampling of volatile chemicals for non-invasive classification of chicken eggs by sex early in incubation.

According to industry estimates, approximately 7 billion day-old male chicks are disposed of annually worldwide because they are not of use to the layer industry. A practical process to identify the sex of the egg early in incubation without penetrating the egg would improve animal welfare, reduce food waste and mitigate environmental impact. We implemented a moderate vacuum pressure system through commercial egg-handling suction cups to collect volatile organic compounds (VOCs). Three separate experiments were set up to determine optimal conditions to collect eggs VOCs to discriminate male from female embryos. Optimal extraction time (2 min), storage conditions (short period of incubation during egg storage (SPIDES) at days 8-10 of incubation), and sampling temperature (37.5°C) were determined. Our VOC-based method could correctly differentiate male from female embryos with more than 80% accuracy. These specifications are compatible with the design of specialized automation equipment capable of high-throughput, in-ovo sexing based on chemical sensor microchips.

Characterization of the microbiome and volatile compounds in anal gland secretions from domestic cats (Felis catus) using metagenomics and metabolomics.

Animals rely on volatile chemical compounds for their communication and behavior. Many of these compounds are sequestered in endocrine and exocrine glands and are synthesized by anaerobic microbes. While the volatile organic compound (VOC) or microbiome composition of glandular secretions has been investigated in several mammalian species, few have linked specific bacterial taxa to the production of volatiles or to specific microbial gene pathways. Here, we use metagenomic sequencing, mass-spectrometry based metabolomics, and culturing to profile the microbial and volatile chemical constituents of anal gland secretions in twenty-three domestic cats (Felis catus), in attempts to identify organisms potentially involved in host odor production. We found that the anal gland microbiome was dominated by bacteria in the genera Corynebacterium, Bacteroides, Proteus, Lactobacillus, and Streptococcus, and showed striking variation among individual cats. Microbiome profiles also varied with host age and obesity. Metabolites such as fatty-acids, ketones, aldehydes and alcohols were detected in glandular secretions. Overall, microbiome and metabolome profiles were modestly correlated (r=0.17), indicating that a relationship exists between the bacteria in the gland and the metabolites produced in the gland. Functional analyses revealed the presence of genes predicted to code for enzymes involved in VOC metabolism such as dehydrogenases, reductases, and decarboxylases. From metagenomic data, we generated 85 high-quality metagenome assembled genomes (MAGs). Of these, four were inferred to have high relative abundance in metagenome profiles and had close relatives that were recovered as cultured isolates. These four MAGs were classified as Corynebacterium frankenforstense, Proteus mirabilis, Lactobacillus johnsonii, and Bacteroides fragilis. They represent strong candidates for further investigation of the mechanisms of volatile synthesis and scent production in the mammalian anal gland.

Exhaled breath condensate profiles of U.S. Navy divers following prolonged hyperbaric oxygen (HBO) and nitrogen-oxygen (Nitrox) chamber exposures.

Prolonged exposure to hyperbaric hyperoxia can lead to pulmonary oxygen toxicity (PO2tox). PO2tox is a mission limiting factor for special operations forces divers using closed-circuit rebreathing apparatus and a potential side effect for patients undergoing hyperbaric oxygen (HBO) treatment. In this study, we aim to determine if there is a specific breath profile of compounds in exhaled breath condensate (EBC) that is indicative of the early stages of pulmonary hyperoxic stress/PO2tox. Using a double-blind, randomized 'sham' controlled, cross-over design 14 U.S. Navy trained diver volunteers breathed two different gas mixtures at an ambient pressure of 2 ATA (33 fsw, 10 msw) for 6.5 h. One test gas consisted of 100% O2(HBO) and the other was a gas mixture containing 30.6% O2with the balance N2(Nitrox). The high O2stress dive (HBO) and low O2stress dive (Nitrox) were separated by at least seven days and were conducted dry and at rest inside a hyperbaric chamber. EBC samples were taken immediately before and after each dive and subsequently underwent a targeted and untargeted metabolomics analysis using liquid chromatography coupled to mass spectrometry (LC-MS). Following the HBO dive, 10 out of 14 subjects reported symptoms of the early stages of PO2tox and one subject terminated the dive early due to severe symptoms of PO2tox. No symptoms of PO2tox were reported following the nitrox dive. A partial least-squares discriminant analysis of the normalized (relative to pre-dive) untargeted data gave good classification abilities between the HBO and nitrox EBC with an AUC of 0.99 (±2%) and sensitivity and specificity of 0.93 (±10%) and 0.94 (±10%), respectively. The resulting classifications identified specific biomarkers that included human metabolites and lipids and their derivatives from different metabolic pathways that may explain metabolomic changes resulting from prolonged HBO exposure.

Controlled air exchange rate method to evaluate reduction of volatile organic compounds by indoor air cleaners.

Air cleaning technologies are needed to reduce indoor concentrations and exposure to volatile organic compounds (VOCs). Currently, air cleaning technologies lack an accepted test standard to evaluate their VOC removal performance. A protocol to evaluate the VOC removal performance of air cleaning devices was developed and piloted with two devices. This method injects a VOC mixture and carbon dioxide into a test chamber, supplies outdoor air at a standard building ventilation rate, periodically measures the VOC concentrations in the chamber using solid phase microextraction-gas chromatography-mass spectrometry over a 3-h decay period, and compares the decay rate of VOCs to carbon dioxide to measure the VOC removal air cleaning performance. The method was demonstrated with both a hydroxyl radical generator and an activated carbon air cleaner. It was shown that the activated carbon air cleaner device tested had a clean air delivery rate an order of magnitude greater than the hydroxyl radical generator device (72.10 vs 6.32 m3/h).

Non-destructive method to classify walnut kernel freshness from volatile organic compound (VOC) emissions using gas chromatography-differential mobility spectrometry (GC-DMS) and machine learning analysis.

Analysis of volatile organic compounds (VOCs) can be an effective strategy to inspect the quality of horticultural commodities and following their degradation. In this work, we report that VOCs emitted by walnuts can be studied using gas chromatography-differential mobility spectrometry (GC-DMS), and those GC-DMS data can be analyzed to predict the rancidity of walnuts, i.e., classify walnuts into grades of freshness. Walnut kernels were assigned a class n depending on their level of freshness as determined by a peroxide assay. VOC samples were analyzed using GC-DMS. From these VOC data, a partial least square regression (PLSR) model provided a freshness prediction value m, which corresponded to the rancid class n when m=n±0.5. The PLSR model had an accuracy of 80% to predict walnut grade and demonstrated a minimal root mean squared error of 0.42 for the m response variables (representative of walnut grade) with the GC-DMS data. We also conducted gas chromatography-mass spectrometry (GC-MS) experiments to identify volatiles that emerged or were enhanced with more rancid walnuts. The findings of the GC-MS study of walnut VOCs align excellently with the GC-DMS study. Based on our results, we conclude that a GC-DMS device deployed with a pre-trained machine learning model can be a very effective device for classifying walnut grades in the industry.

Predominant SARS-CoV-2 variant impacts accuracy when screening for infection using exhaled breath vapor.

BACKGROUND: New technologies with novel and ambitious approaches are being developed to diagnose or screen for SARS-CoV-2, including breath tests. The US FDA approved the first breath test for COVID-19 under emergency use authorization in April 2022. Most breath-based assays measure volatile metabolites exhaled by persons to identify a host response to infection. We hypothesized that the breathprint of COVID-19 fluctuated after Omicron became the primary variant of transmission over the Delta variant. METHODS: We collected breath samples from 142 persons with and without a confirmed COVID-19 infection during the Delta and Omicron waves. Breath samples were analyzed by gas chromatography-mass spectrometry. RESULTS: Here we show that based on 63 exhaled compounds, a general COVID-19 model had an accuracy of 0.73 ± 0.06, which improved to 0.82 ± 0.12 when modeling only the Delta wave, and 0.84 ± 0.06 for the Omicron wave. The specificity improved for the Delta and Omicron models (0.79 ± 0.21 and 0.74 ± 0.12, respectively) relative to the general model (0.61 ± 0.13). CONCLUSIONS: We report that the volatile signature of COVID-19 in breath differs between the Delta-predominant and Omicron-predominant variant waves, and accuracies improve when samples from these waves are modeled separately rather than as one universal approach. Our findings have important implications for groups developing breath-based assays for COVID-19 and other respiratory pathogens, as the host response to infection may significantly differ depending on variants or subtypes.

In recent decades, scientists have found we exhale thousands of compounds that reveal much about our health, including whether we are sick with COVID-19. Our team asked whether the breath profile of someone infected with the Delta variant of COVID-19 would match the breath profile caused by the Omicron variant­a version of the virus that is more transmissible. We analyzed breath samples from 142 people, some sick with either the Delta or Omicron variant of COVID-19, and others who were negative for COVID-19. Our results indicate that the Delta variant altered the contents of our breath in a different way than the Omicron variant, and breath-based tests improved when optimized to detect only one of the variants. These findings might impact the design of future breath-based tests for COVID-19.

Glass-to-Glass Fusion Bonding Quality and Strength Evaluation with Time, Applied Force, and Heat.

A bonding process was developed for glass-to-glass fusion bonding using Borofloat 33 wafers, resulting in high bonding yield and high flexural strength. The Borofloat 33 wafers went through a two-step process with a pre-bond and high-temperature bond in a furnace. The pre-bond process included surface activation bonding using O2 plasma and N2 microwave (MW) radical activation, where the glass wafers were brought into contact in a vacuum environment in an EVG 501 Wafer Bonder. The optimal hold time in the EVG 501 Wafer bonder was investigated and concluded to be a 3 h hold time. The bonding parameters in the furnace were investigated for hold time, applied force, and high bonding temperature. It was concluded that the optimal parameters for glass-to-glass Borofloat 33 wafer bonding were at 550 °C with a hold time of 1 h with 550 N of applied force.

Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification.

Differential mobility spectrometry (DMS)-based detectors are being widely studied to detect chemical warfare agents, explosives, chemicals, drugs and analyze volatile organic compounds (VOCs). The dispersion plots from DMS devices are complex to effectively analyze through visual inspection. In the current work, we adopted machine learning to differentiate pure chemicals and identify chemicals in a mixture. In particular, we observed the convolutional neural network algorithm exhibits excellent accuracy in differentiating chemicals in their pure forms while also identifying chemicals in a mixture. In addition, we propose and validate the magnitude-squared coherence (msc) between the DMS data of known chemical composition and that of an unknown sample can be sufficient to inspect the chemical composition of the unknown sample. We have shown that the msc-based chemical identification requires the least amount of experimental data as opposed to the machine learning approach.

A low cost, easy-to-assemble, open-source modular mobile sampler design for thermal desorption analysis of breath and environmental VOCs.

Exhaled breath vapor contains hundreds of volatile organic compounds (VOCs), which are the byproducts of health and disease metabolism, and they have clinical and diagnostic potential. Simultaneous collection of breath VOCs and background environmental VOCs is important to ensure analyses eliminate exogenous compounds from clinical studies. We present a mobile sampling system to extract gaseous VOCs onto commercially available sorbent-packed thermal desorption tubes. The sampler can be connected to a number of commonly available disposable and reusable sampling bags, in the case of this study, a Tedlar bag containing a breath sample. Alternatively, the inlet can be left open to directly sample room or environmental air when obtaining a background VOC sample. The system contains a screen for the operator to input a desired sample volume. A needle valve allows the operator to control the sample flow rate, which operates with an accuracy of -1.52 ± 0.63% of the desired rate, and consistently generated that rate with 0.12 ± 0.06% error across repeated measures. A flow pump, flow sensor and microcontroller allow volumetric sampling, as opposed to timed sampling, with 0.06 ± 0.06% accuracy in the volume extracted. Four samplers were compared by sampling a standard chemical mixture, which resulted in 6.4 ± 4.7% error across all four replicate modular samplers to extract a given VOC. The samplers were deployed in a clinical setting to collect breath and background/environmental samples, including patients with active SARS-CoV-2 infections, and the device could easily move between rooms and can undergo required disinfection protocols to prevent transmission of pathogens on the case exterior. All components required for assembly are detailed and are made publicly available for non-commercial use, including the microcontroller software. We demonstrate the device collects volatile compounds, including use of chemical standards, and background and breath samples in real use conditions.

Inactivation of SARS-CoV-2 in clinical exhaled breath condensate samples for metabolomic analysis.

Exhaled breath condensate (EBC) is routinely collected and analyzed in breath research. Because it contains aerosol droplets, EBC samples from SARS-CoV-2 infected individuals harbor the virus and pose the threat of infectious exposure. We report for the first time a safe and consistent method to fully inactivate SARS-CoV-2 in EBC samples and make EBC samples safe for processing and analysis. EBC samples containing infectious SARS-CoV-2 were treated with several concentrations of acetonitrile. The most commonly used 10% acetonitrile treatment for EBC processing failed to completely inactivate the virus in samples and viable virus was detected by the assay of SARS-CoV-2 infection of Vero E6 cells in a biosafety level 3 laboratory. Treatment with either 50% or 90% acetonitrile was effective to completely inactivate the virus, resulting in safe, non-infectious EBC samples that can be used for metabolomic analysis. Our study provides SARS-CoV-2 inactivation protocol for the collection and processing of EBC samples in the clinical setting and for advancing to metabolic assessments in health and disease.

IABR Symposium 2021 meeting report: breath standardization, sampling, and testing in a time of COVID-19.

Due to COVID-19 travel disruptions, the International Association of Breath Research hosted the planned 2021 Breath Summit virtually as a symposium with oral and poster presentations. The event was comprised of a week-long social media asynchronous online event for sharing research abstracts, posters and discussions. Subsequently, there were two days of real-time webinar platform interactions each featuring three technical presentations, open forum questions, answers, and commentary. The symposium was well attended and well received. It allowed the breath community to share new research and to reconnect with colleagues and friends. This report presents an overview of the topics presented and various salient discussion points.

Exhaled breath biomarkers of influenza infection and influenza vaccination.

Respiratory viral infections are considered a major public health threat, and breath metabolomics can provide new ways to detect and understand how specific viruses affect the human pulmonary system. In this pilot study, we characterized the metabolic composition of human breath for an early diagnosis and differentiation of influenza viral infection, as well as other types of upper respiratory viral infections. We first studied the non-specific effects of planned seasonal influenza vaccines on breath metabolites in healthy subjects after receiving the immunization. We then investigated changes in breath content from hospitalized patients with flu-like symptoms and confirmed upper respiratory viral infection. The exhaled breath was sampled using a custom-made breath condenser, and exhaled breath condensate (EBC) samples were analysed using liquid chromatography coupled to quadruplole-time-of-flight mass spectrometer (LC-qTOF). All metabolomic data was analysed using both targeted and untargeted approaches to detect specific known biomarkers from inflammatory and oxidative stress biomarkers, as well as new molecules associated with specific infections. We were able to find clear differences between breath samples collected before and after flu vaccine administration, together with potential biomarkers that are related to inflammatory processes and oxidative stress. Moreover, we were also able to discriminate samples from patients with flu-related symptoms that were diagnosed with confirmatory respiratory viral panels (RVPs). RVP positive and negative differences were identified, as well as differences between specific viruses defined. These results provide very promising information for the further study of the effect of influenza A and other viruses in human systems by using a simple and non-invasive specimen like breath.

Predicting Influenza and Rhinovirus Infections in Airway Cells Utilizing Volatile Emissions.

BACKGROUND: Respiratory viral infections are common and potentially devastating to patients with underlying lung disease. Diagnosing viral infections often requires invasive sampling, and interpretation often requires specialized laboratory equipment. Here, we test the hypothesis that a breath test could diagnose influenza and rhinovirus infections using an in vitro model of the human airway. METHODS: Cultured primary human tracheobronchial epithelial cells were infected with either influenza A H1N1 or rhinovirus 1B and compared with healthy control cells. Headspace volatile metabolite measurements of cell cultures were made at 12-hour time points postinfection using a thermal desorption-gas chromatography-mass spectrometry method. RESULTS: Based on 54 compounds, statistical models distinguished volatile organic compound profiles of influenza- and rhinovirus-infected cells from healthy counterparts. Area under the curve values were 0.94 for influenza, 0.90 for rhinovirus, and 0.75 for controls. Regression analysis predicted how many hours prior cells became infected with a root mean square error of 6.35 hours for influenza- and 3.32 hours for rhinovirus-infected cells. CONCLUSIONS: Volatile biomarkers released by bronchial epithelial cells could not only be used to diagnose whether cells were infected, but also the timing of infection. Our model supports the hypothesis that a breath test could serve to diagnose viral infections.

Environmental sampling of volatile organic compounds during the 2018 Camp Fire in Northern California.

Trace analysis of volatile organic compounds (VOCs) during wildfires is imperative for environmental and health risk assessment. The use of gas sampling devices mounted on unmanned aerial vehicles (UAVs) to chemically sample air during wildfires is of great interest because these devices move freely about their environment, allowing for more representative air samples and the ability to sample areas dangerous or unreachable by humans. This work presents chemical data from air samples obtained in Davis, CA during the most destructive wildfire in California's history - the 2018 Camp Fire - as well as the deployment of our sampling device during a controlled experimental fire while fixed to a UAV. The sampling mechanism was an in-house manufactured micro-gas preconcentrator (µPC) embedded onto a compact battery-operated sampler that was returned to the laboratory for chemical analysis. Compounds commonly observed in wildfires were detected during the Camp Fire using gas chromatography mass spectrometry (GC-MS), including BTEX (benzene, toluene, ethylbenzene, m+p-xylene, and o-xylene), benzaldehyde, 1,4-dichlorobenzene, naphthalene, 1,2,3-trimethylbenzene and 1-ethyl-3-methylbenzene. Concentrations of BTEX were calculated and we observed that benzene and toluene were highest with average concentrations of 4.7 and 15.1 µg/m3, respectively. Numerous fire-related compounds including BTEX and aldehydes such as octanal and nonanal were detected upon experimental fire ignition, even at a much smaller sampling time compared to samples taken during the Camp Fire. Analysis of the air samples taken both stationary during the Camp Fire and mobile during an experimental fire show the successful operation of our sampler in a fire environment.

Novel LC-MS-TOF method to detect and quantify ascorbic and uric acid simultaneously in different biological matrices.

Ascorbic acid (AA) and uric acid (UA) are known as two of the major antioxidants in biological fluids. We report a novel liquid chromatography-mass spectrometry with time-of-flight (LC-MS-TOF) method for the simultaneous quantification of ascorbic and uric acids using MPA, antioxidant solution and acetonitrile as a protein precipitating agent. Both compounds were separated from interferences using a reverse phase C18 column with water and acetonitrile gradient elution (both with formic acid) and identified and quantified by MS in the negative ESI mode. Isotope labeled internal standards were also added to ensure the accuracy of the measures. The method was validated for exhaled breath condensate (EBC), nasal lavage (NL) and plasma samples by assessing selectivity, linearity, accuracy and precision, recovery and matrix effect and stability. Sample volumes below 250 µL were used and linear ranges were determined between 1 - 25 and 1 - 40 µg/mL for ascorbic and uric acid, respectively, for plasma samples, and between 0.05 - 5 (AA) and 0.05 - 7.5 (UA) µg/mL for EBC and NL samples. The new method was successfully applied to real samples from subjects that provided each of the studied matrices. Results showed higher amounts determined in plasma samples, with similar profiles for AA and UA in EBC and NL but at much lower concentrations.