Commonly disrupted pathways in brain and kidney in a pig model of systemic endotoxemia.

Sepsis is a life-threatening state that arises due to a hyperactive inflammatory response stimulated by infection and rarely other insults (e.g., non-infections tissue injury). Although changes in several proinflammatory cytokines and signals are documented in humans and small animal models, far less is known about responses within affected tissues of large animal models. We sought to understand the changes that occur during the initial stages of inflammation by administering intravenous lipopolysaccharide (LPS) to Yorkshire pigs and assessing transcriptomic alterations in the brain, kidney, and whole blood. Robust transcriptional alterations were found in the brain, with upregulated responses enriched in inflammatory pathways and downregulated responses enriched in tight junction and blood vessel functions. Comparison of the inflammatory response in the pig brain to a similar mouse model demonstrated some overlapping changes but also numerous differences, including oppositely dysregulated genes between species. Substantial changes also occurred in the kidneys following LPS with several enriched upregulated pathways (cytokines, lipids, unfolded protein response, etc.) and downregulated gene sets (tube morphogenesis, glomerulus development, GTPase signal transduction, etc.). We also found significant dysregulation of genes in whole blood that fell into several gene ontology categories (cytokines, cell cycle, neutrophil degranulation, etc.). We observed a strong correlation between the brain and kidney responses, with significantly shared upregulated pathways (cytokine signaling, cell death, VEGFA pathways) and downregulated pathways (vasculature and RAC1 GTPases). In summary, we have identified a core set of shared genes and pathways in a pig model of systemic inflammation.

Threshold-Responsive Colorimetric Sensing System for the Continuous Monitoring of Gases.

Colorimetric sensors are widely used because of their inherent advantages including accuracy, rapid response, ease-of-use, and low costs; however, they usually lack reusability, which precludes the continuous use of a single sensor. We have developed a threshold-responsive colorimetric system that enables repeated analyte measurements by a single colorimetric sensor. The threshold responsive algorithm automatically adjusts the sensor exposure time to the analyte and measurement frequency according to the sensor response. The system registers the colorimetric sensor signal change rate, prevents the colorimetric sensor from reaching saturation, and allows the sensor to fully regenerate before the next measurement is started. The system also addresses issues common to colorimetric sensors, including the response time and range of detection. We demonstrate the benefits and feasibility of this novel system, using colorimetric sensors for ammonia and carbon dioxide gases for continuous monitoring of up to (at least) 60 detection cycles without signs of analytical performance degradation of the sensors.

A Smart System for the Contactless Measurement of Energy Expenditure.

Energy Expenditure (EE) (kcal/day), a key element to guide obesity treatment, is measured from CO2 production, VCO2 (mL/min), and/or O2 consumption, VO2 (mL/min). Current technologies are limited due to the requirement of wearable facial accessories. A novel system, the Smart Pad, which measures EE via VCO2 from a room's ambient CO2 concentration transients was evaluated. Resting EE (REE) and exercise VCO2 measurements were recorded using Smart Pad and a reference instrument to study measurement duration's influence on accuracy. The Smart Pad displayed 90% accuracy (±1 SD) for 14-19 min of REE measurement and for 4.8-7.0 min of exercise, using known room's air exchange rate. Additionally, the Smart Pad was validated measuring subjects with a wide range of body mass indexes (BMI = 18.8 to 31.4 kg/m2), successfully validating the system accuracy across REE's measures of ~1200 to ~3000 kcal/day. Furthermore, high correlation between subjects' VCO2 and λ for CO2 accumulation was observed (p < 0.00001, R = 0.785) in a 14.0 m3 sized room. This finding led to development of a new model for REE measurement from ambient CO2 without λ calibration using a reference instrument. The model correlated in nearly 100% agreement with reference instrument measures (y = 1.06x, R = 0.937) using an independent dataset (N = 56).

A novel vertical flow assay for point of care measurement of iron from whole blood.

Disorders in iron metabolism are endemic globally, affecting more than several hundred million individuals and often resulting in increased rates of mortality or general deterioration of quality of life. To both prevent and monitor treatment of iron related disorders, we present a point of care medical device which leverages a simple smartphone camera to measure total iron concentration from a finger-prick sample. The system consists of a smartphone and an in-house developed app, a 3D printed sensing chamber and a vertical flow membrane-based sensor strip designed to accommodate 50 µl of whole blood, filter out the cellular components and carry out a colorimetric chelation reaction producing a colour change which is detected by our smartphone device. The app's accuracy and precision were assessed via comparison of the mobile app's RGB output to a reference imaging software, ImageJ for the same colorimetric sensing strip. Correlation plots resulted in slopes of 0.99 and coefficient of determination (R2 = 0.99). The device was determined to have a signal to noise ratio >40 and a mean bias of 2% which both indicate high analytical accuracy and precision (in terms of RGB measurement). The smartphone device's iron concentration readout was then studied using an extensively validated laboratory developed test (LDT) for iron detection, which is an optimized spectrophotometry-based technique (this is considered the gold standard for iron quantification among LDTs). In comparison of the smartphone-based technique with the gold standard LDT, a calibration slope of 0.0004 au µg-1 dL-1, a correlation plot with slope of 1.09 and coefficient of determination (R2) of 0.96 and a mean bias of 5.3%, our device can accurately measure iron levels in blood. With detection times of five minutes, fingerpick sample and sensor cost less than 10 cents, the device shows great promise in being developed as the first ever commercial device for iron quantification in blood.

Reliable Breathing Tracking with Wearable Mask Device.

Breathing tracking is critical for the assessment of lung functions, exercise physiologies, and energy expenditure. Conventional methods require using a face mask or mouthpiece that is connected to a stationary equipment through a tube, restricting the location, movement, or even the posture. To obtain accurate breathing physiology parameters that represent the true state of the patient during different scenarios, a wearable technology that has less intervention to patient's activities in free-living conditions is highly preferred. Here, we propose a miniaturized, reliable, and wide-dynamic ranged flow sensing technology that is immune to orientation, movement, and noise. As far as we know, this is the first work of introducing a fully integrated mask device focusing on breath tracking in free-living conditions. There are two key challenges for achieving this goal: miniaturized flow sensing and motion-induced artifacts elimination. To address these challenges, we come up with two technical innovations: 1) in hardware wise, we have designed an integrated flow sensing technique based on differential pressure Pneumotach approach and motion sensing; 2) in software wise, we have developed comprehensive algorithms based baseline tracking and orientation and motion compensation. The effectiveness of the proposed technology has been proven by the experiments. Experimental results from simulator and real breath conditions show high correlation (R2 = 0.9994 and 0.9964 respectively) and mean error within 2.5% for Minute Volume (VE), when compared to values computed from reference methods. These results show that the proposed method is accurate and reliable to track the key breath parameters in free-living conditions.

An Unobstructive Sensing Method for Indoor Air Quality Optimization and Metabolic Assessment within Vehicles.

This work investigates the use of an intelligent and unobstructive sensing technique for maintaining vehicle cabin's indoor air quality while simultaneously assessing the driver metabolic rate. CO2 accumulation patterns are of great interest because CO2 can have negative cognitive effects at higher concentrations and also since CO2 accumulation rate can potentially be used to determine a person's metabolic rate. The management of the vehicle's ventilation system was controlled by periodically alternating the air recirculation mode within the cabin, which was actuated based on the CO2 levels inside the vehicle's cabin. The CO2 accumulation periods were used to assess the driver's metabolic rate, using a model that considered the vehicle's air exchange rate. In the process of the method optimization, it was found that the vehicle's air exchange rate (λ [h-1]) depends on the vehicle speeds, following the relationship: λ = 0.060 × (speed) - 0.88 when driving faster than 17 MPH. An accuracy level of 95% was found between the new method to assess the driver's metabolic rate (1620 ± 140 kcal/day) and the reference method of indirect calorimetry (1550 ± 150 kcal/day) for a total of N = 16 metabolic assessments at various vehicle speeds. The new sensing method represents a novel approach for unobstructive assessment of driver metabolic rate while maintaining indoor air quality within the vehicle cabin.

Gradient-Based Colorimetric Sensors for Continuous Gas Monitoring.

Colorimetry detects a color change resulted from a chemical reaction or molecular binding. Despite its widespread use in sensing, continuous monitoring of analytes with colorimetry is difficult, especially when the color-producing reaction or binding is irreversible. Here, we report on a gradient-based colorimetric sensor (GCS) to overcome this limitation. Lateral transport of analytes across a colorimetric sensor surface creates a color gradient that shifts along the transport direction over time, and GCS tracks the gradient shift and converts it into analyte concentration in real time. Using a low cost complementary metal-oxide semiconductor imager and imaging processing algorithm, we show submicrometer gradient shift tracking precision and continuous monitoring of ppb-level ozone.

Tracking Personal Health-Environment Interaction with Novel Mobile Sensing Devices.

The development of connected health devices has allowed for a more accurate assessment of a person's state under free-living conditions. In this work, we use two mobile sensing devices and investigate the correlation between individual's resting metabolic rate (RMR) and volatile organic compounds (VOCs) exposure levels. A total of 17 healthy, young, and sedentary office workers were recruited, measured for RMR with a mobile indirect calorimetry (IC) device, and compared with their corresponding predicted RMR values from the Academy of Nutrition and Dietetics' recommended epidemiological equation, the Mifflin⁻St Jeor equation (MSJE). Individual differences in the RMR values from the IC device and the epidemiological equation were found, and the subjects' RMRs were classified as normal, high, or low based on a cut-off of ±200 kcal/day difference with respect to the predicted value. To study the cause of the difference, VOCs exposure levels of each participant's daytime working environment and nighttime resting environment were assessed using a second mobile sensing device for VOCs exposure detection. The results showed that all sedentary office workers had a low VOCs exposure level (<2 ppmC), and there was no obvious correlation between VOCs exposure and the RMR difference. However, an additional participant who was a worker in an auto repair shop, showed high VOCs exposure with respect to the sedentary office worker population and a significant difference between measured and predicted RMR, with a low RMR of 500 kcal/day difference. The mobile sensing devices have been demonstrated to be suitable for the assessment of direct information of human health⁻environment interactions at free-living conditions.

Thermochemical Humidity Detection in Harsh or Non-Steady Environments.

We present a new method of chemical quantification utilizing thermal analysis for the detection of relative humidity. By measuring the temperature change of a hydrophilically-modified temperature sensing element vs. a hydrophobically-modified reference element, the total heat from chemical interactions in the sensing element can be measured and used to calculate a change in relative humidity. We have probed the concept by assuming constant temperature streams, and having constant reference humidity (~0% in this case). The concept has been probed with the two methods presented here: (1) a thermistor-based method and (2) a thermographic method. For the first method, a hydrophilically-modified thermistor was used, and a detection range of 0-75% relative humidity was demonstrated. For the second method, a hydrophilically-modified disposable surface (sensing element) and thermal camera were used, and thermal signatures for different relative humidity were demonstrated. These new methods offer opportunities in either chemically harsh environments or in rapidly changing environments. For sensing humidity in a chemically harsh environment, a hydrophilically-modified thermistor can provide a sensing method, eliminating the exposure of metallic contacts, which can be easily corroded by the environment. On the other hand, the thermographic method can be applied with a disposable non-contact sensing element, which is a low-cost upkeep option in environments where damage or fouling is inevitable. In addition, for environments that are rapidly changing, the thermographic method could potentially provide a very rapid humidity measurement as the chemical interactions are rapid and their changes are easily quantified.

A Novel Wireless Wearable Volatile Organic Compound (VOC) Monitoring Device with Disposable Sensors.

A novel portable wireless volatile organic compound (VOC) monitoring device with disposable sensors is presented. The device is miniaturized, light, easy-to-use, and cost-effective. Different field tests have been carried out to identify the operational, analytical, and functional performance of the device and its sensors. The device was compared to a commercial photo-ionization detector, gas chromatography-mass spectrometry, and carbon monoxide detector. In addition, environmental operational conditions, such as barometric change, temperature change and wind conditions were also tested to evaluate the device performance. The multiple comparisons and tests indicate that the proposed VOC device is adequate to characterize personal exposure in many real-world scenarios and is applicable for personal daily use.

Acetone as biomarker for ketosis buildup capability--a study in healthy individuals under combined high fat and starvation diets.

BACKGROUND: Ketogenic diets are high fat and low carbohydrate or very low carbohydrate diets, which render high production of ketones upon consumption known as nutritional ketosis (NK). Ketosis is also produced during fasting periods, which is known as fasting ketosis (FK). Recently, the combinations of NK and FK, as well as NK alone, have been used as resources for weight loss management and treatment of epilepsy. METHODS: A crossover study design was applied to 11 healthy individuals, who maintained moderately sedentary lifestyle, and consumed three types of diet randomly assigned over a three-week period. All participants completed the diets in a randomized and counterbalanced fashion. Each weekly diet protocol included three phases: Phase 1 - A mixed diet with ratio of fat: (carbohydrate + protein) by mass of 0.18 or the equivalence of 29% energy from fat from Day 1 to Day 5. Phase 2- A mixed or a high-fat diet with ratio of fat: (carbohydrate + protein) by mass of approximately 0.18, 1.63, or 3.80 on Day 6 or the equivalence of 29%, 79%, or 90% energy from fat, respectively. Phase 3 - A fasting diet with no calorie intake on Day 7. Caloric intake from diets on Day 1 to Day 6 was equal to each individual's energy expenditure. On Day 7, ketone buildup from FK was measured. RESULTS: A statistically significant effect of Phase 2 (Day 6) diet was found on FK of Day 7, as indicated by repeated analysis of variance (ANOVA), F(2,20) = 6.73, p < 0.0058. Using a Fisher LDS pair-wise comparison, higher significant levels of acetone buildup were found for diets with 79% fat content and 90% fat content vs. 29% fat content (with p = 0.00159**, and 0.04435**, respectively), with no significant difference between diets with 79% fat content and 90% fat content. In addition, independent of the diet, a significantly higher ketone buildup capability of subjects with higher resting energy expenditure (R(2) = 0.92), and lower body mass index (R(2) = 0.71) was observed during FK.

Colorimetric humidity sensor based on liquid composite materials for the monitoring of food and pharmaceuticals.

Using supported ionic-liquid membrane (SILM)-inspired methodologies, we have synthesized, characterized, and developed a humidity sensor by coating a liquid composite material onto a hygroscopic, porous substrate. Similar to pH paper, the sensor responds to the environment's relative humidity and changes color accordingly. The humidity indicator is prepared by casting a few microliters of low-toxicity reagents on a nontoxic substrate. The sensing material is a newly synthesized liquid composite that comprises a hygroscopic medium for environmental humidity capture and a color indicator that translates the humidity level into a distinct color change. Sodium borohydride was used to form a liquid composite medium, and DenimBlu30 dye was used as a redox indicator. The liquid composite medium provides a hygroscopic response to the relative humidity, and DenimBlu30 translates the chemical changes into a visual change from yellow to blue. The borate-redox dye-based humidity sensor was prepared, and then Fourier transform infrared spectroscopy, differential scanning calorimetry, and image analysis methods were used to characterize the chemical composition, optimize synthesis, and gain insight into the sensor reactivity. Test results indicated that this new sensing material can detect relative humidity in the range of 5-100% in an irreversible manner with good reproducibility and high accuracy. The sensor is a low-cost, highly sensitive, easy-to-use humidity indicator. More importantly, it can be easily packaged with products to monitor humidity levels in pharmaceutical and food packaging.

A Novel Real-time Carbon Dioxide Analyzer for Health and Environmental Applications.

To be able to detect carbon dioxide (CO2) with high accuracy and fast response time is critical for many health and environmental applications. We report on a pocket-sized CO2 sensor for real-time analysis of end-tidal CO2, and environmental CO2. The sensor shows fast and reversible response to CO2 over a wide concentration range, covering the needs of both environmental and health applications. It is also immune to the presence of various interfering gases in ambient or expired air. Furthermore, the sensor has been used for real-time breath analysis, and the results are in good agreement with those from a commercial CO2 detector.

Eigen-entropy based time series signatures to support multivariate time series classification.

Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset. To manage dataset's dynamic nature, we employ preprocessing with dense multi scale entropy. Consequently, the proposed framework, Eigen-entropy-based Time Series Signatures, captures correlations among multivariate time series without losing its temporal and dynamic aspects. The efficacy of our algorithm is assessed using six binary datasets sourced from the University of East Anglia, in addition to a publicly available gait dataset and an institutional sepsis dataset from the Mayo Clinic. We use recall as the evaluation metric to compare our approach against baseline algorithms, including dependent dynamic time warping with 1 nearest neighbor and multivariate multi-scale permutation entropy. Our method demonstrates superior performance in terms of recall for seven out of the eight datasets.

A wireless hybrid chemical sensor for detection of environmental volatile organic compounds.

A hybrid sensor for monitoring volatile organic compounds (VOCs) in air is developed. The device combines two orthogonal sensing principles, selective molecular binding with a microfabricated quartz tuning fork detector and separation of analytes with a column. The tuning fork detector is functionalized with molecular imprinted polymers for selective binding to benzene, toluene, ethylbenzene, and xylenes (BTEX), and the separation column provides further discrimination of the analytes for real world complex sample analysis. The device is wireless, portable, battery-powered, and cell-phone operated, and it allows reliable detection in parts per billion (ppb) by volume-levels of BTEX in the presence of complex interferents. The hybrid device is suitable for occupational, environmental health, and epidemiological applications.

Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective.

There is a growing interest in the application of machine learning (ML) in Alzheimer's disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.

A Novel Acetone Sensor for Body Fluids.

Ketones are well-known biomarkers of fat oxidation produced in the liver as a result of lipolysis. These biomarkers include acetoacetic acid and ß-hydroxybutyric acid in the blood/urine and acetone in our breath and skin. Monitoring ketone production in the body is essential for people who use caloric intake deficit to reduce body weight or use ketogenic diets for wellness or therapeutic treatments. Current methods to monitor ketones include urine dipsticks, capillary blood monitors, and breath analyzers. However, these existing methods have certain disadvantages that preclude them from being used more widely. In this work, we introduce a novel acetone sensor device that can detect acetone levels in breath and overcome the drawbacks of existing sensing approaches. The critical element of the device is a robust sensor with the capability to measure acetone using a complementary metal oxide semiconductor (CMOS) chip and convenient data analysis from a red, green, and blue deconvolution imaging approach. The acetone sensor device demonstrated sensitivity of detection in the micromolar-concentration range, selectivity for detection of acetone in breath, and a lifetime stability of at least one month. The sensor device utility was probed with real tests on breath samples using an established blood ketone reference method.

Online sample conditioning for portable breath analyzers.

Various innovative chemical sensors have been developed in recent years to sense dangerous substances in air and trace biomarkers in breath. However, in order to solve real world problems, the sensors must be equipped with efficient sample conditioning that can, e.g., control the humidity, which is discussed much less in the literature. To meet the demand, a miniaturized mouthpiece was developed for personal breath analyzers. A key function of the mouthpiece is to condition the humidity in real breath samples without changing the analyte concentrations and introducing substantial backpressure, which is achieved with optimized packing of desiccant particles. Numerical simulations were carried out to determine the performance of the mouthpiece in terms of various controllable parameters, such as the size, density, and geometry of the packing. Mouthpieces with different configurations were built and tested, and the experimental data validated the simulation findings. A mouthpiece with optimized performance reducing relative humidity from 95% (27,000 ppmV) to 29% (8000 ppmV) whereas retaining 92% nitric oxide (50 ppbV to 46 ppbV) was built and integrated into a hand-held exhaled nitric oxide sensor, and the performance of exhaled nitric oxide measurement was in good agreement with the gold standard chemiluminescence technique. Acetone, carbon dioxide, oxygen, and ammonia samples were also measured after passing through the desiccant mouthpiece using commercial sensors to examine wide applicability of this breath conditioning approach.

A New Sensor for the Assessment of Personal Exposure to Volatile Organic Compounds.

To improve our understanding of indoor and outdoor personal exposures to common environmental toxicants released into the environment, new technologies that can monitor and quantify the toxicants anytime anywhere are needed. This paper presents a wearable sensor to provide such capabilities. The sensor can communicate with a common smart phone and provides accurate measurement of volatile organic compound concentration at a personal level in real time, providing environmental toxicants data every three minutes. The sensor has high specificity and sensitivity to aromatic, alkyl, and chlorinated hydrocarbons with a resolution as low as 4 parts per billion (ppb), with a detection range of 4 ppb to 1000 ppm (parts per million). The sensor's performance was validated using Gas Chromatography and Selected Ion Flow Tube - Mass Spectrometry reference methods in a variety of environments and activities with overall accuracy higher than 81% (r(2) > 0.9). Field tests examined personal exposure in various scenarios including: indoor and outdoor environments, traffic exposure in different cities which vary from 0 to 50 ppmC (part-per-million carbon from hydrocarbons), and pollutants near the 2010 Deepwater Horizon's oil spill. These field tests not only validated the performance but also demonstrated unprecedented high temporal and spatial toxicant information provided by the new technology.

Multiplexed Chemical Sensing CMOS Imager.

A miniaturized and multiplexed chemical sensing technology is urgently needed to empower mobile devices and robots for various new applications such as mobile health and Internet of Things. Here, we show that a complementary metal-oxide-semiconductor (CMOS) imager can be turned into a multiplexed colorimetric sensing chip by coating micron-scale sensing spots on the CMOS imager surface. Each sensing spot contains nanocomposites of colorimetric sensing probes and silica nanoparticles that enhance sensing signals by several orders of magnitude. The sensitivity is spot-size-invariant, and high-performance gas sensing can be achieved on sensing spots as small as ∼10 µm. This great scalability combined with millions of pixels of a CMOS imager offers a promising platform for highly integrated chemical sensors. To prove its compatibility with mobile electronics, we have built a smartphone accessory based on this chemical CMOS sensor and demonstrated that personal health management can be achieved through the detection of gaseous biomarkers and pollutants. We anticipate that this new platform will pave the way for the widespread application of chemical sensing in mobile electronics and wearable devices.