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.

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.

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.

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).

Comparative study of a novel portable indirect calorimeter to a reference breath-by-breath instrument and its use in telemedicine settings.

BACKGROUND & AIMS: Resting Energy Expenditure (REE) quantitatively describes the calories used to support body function (e.g. breathing, blood circulation, etc.) at resting condition. Assessment of the REE is essential for successful weight management and the understanding of metabolic health. REE is typically determined via indirect calorimetry. Current biomedical indirect calorimetry technologies, utilizing assessment of oxygen consumption (VO2) and carbon dioxide production (VCO2) rates (which are typically in the form factor of a metabolic cart) are bulky and require on-site calibration and/or trained professionals to operate. We introduce a novel wearable medical device with FDA clearance to determine REE accurately, portable, and user-friendly format, which can be used both by health professionals in a clinical environment and by the patient at home. Previously, we have reported the validation of Breezing Med (also named as Breezing Pro™) through Douglas Bag Method, a gold standard for gas exchange measurement, and excellent agreement has been found between the two methods for the determination of REE, VO2, and VCO2 rates (Mora et al., 2020). Now we present the validation of Breezing Med against Medical Graphics (MGC) CPX Ultima™, a FDA 510 k cleared metabolic cart, which principle is based on breath-by-breath analysis. In addition, we present Breezing Med as a tool for daily measurement of metabolic rate by the lay person at home. METHODS: A) The validation study was executed via parallel measurement of 20 healthy participants under resting conditions using both the Breezing Med and the MGC Ultima CPX™ (10 min test). B) Breezing Med measurements were carried out by six subjects at home during stay-at-home order due to COVID-19 for 30 days. RESULTS: A) The resulting measurements from both devices was compared with correlation slope's and R-squared coefficients close to 1. B) Results were recorded and analyzed for variability. The pilot study demonstrated the advantage of Breezing Med device to be easy-to-use at home by lay people, which make the valuable device for telemedicine applications related to weight management from home. CONCLUSIONS: This result shows that the MGC Ultima CPX™ and Breezing Med are substantially equivalent for REE measurement; and an advantage of this device for metabolic assessment under the current COVID-19 pandemic situation, for people with impaired physical mobility, and for those who lives in rural areas or face impediments that limit physical access to care.

Development of a new aerosol barrier mask for mitigation of spread of SARS-CoV-2 and other infectious pathogens.

The COVID-19 pandemic has caused huge impact on public health and significantly changed our lifestyle. This is due to the fast airborne oro-nasal transmission of SARS-CoV-2 from the infected individuals. The generation of liquid aerosolized particles occurs when the COVID-19 patients speak, sing, cough, sneeze, or simply breathe. We have developed a novel aerosol barrier mask (ABM) to mitigate the spread of SARS-CoV-2 and other infectious pathogens. This Aerosol Barrier Mask is designed for preventing SARS-CoV-2 transmission while transporting patients within hospital facilities. This mask can constrain aerosol and droplet particles and trap them in a biofilter, while the patient is normally breathing and administrated with medical oxygen. The system can be characterized as an oxygen delivery and mitigation mask which has no unfiltered exhaled air dispersion. The mask helps to prevent the spread of SARS-CoV-2, and potentially other infectious respiratory pathogens and protects everyone in general, especially healthcare professionals.

Self-contained system for mitigation of contaminated aerosol sources of SARS-CoV-2.

Contaminated aerosols and micro droplets are easily generated by infected hosts through sneezing, coughing, speaking and breathing1-3 and harm humans' health and the global economy. While most of the efforts are usually targeted towards protecting individuals from getting infected,4 eliminating transmissions from infection sources is also important to prevent disease transmission. Supportive therapies for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) pneumonia such as oxygen supplementation, nebulizers and non-invasive mechanical ventilation all carry an increased risk for viral transmission via aerosol to healthcare workers.5-9 In this work, we study the efficacy of five methods for self-containing aerosols emitted from infected subjects undergoing nebulization therapies with a diverse spectrum on oxygen delivery therapies. The work includes five study cases: Case I: Use of a Full-Face Mask with biofilter in bilevel positive airway pressure device (BPAP) therapy, Case II: Use of surgical mask in High Flow Nasal Cannula (HFNC) therapy, Case III: Use of a modified silicone disposable mask in a HFNC therapy, Case IV: Use of a modified silicone disposable mask with a regular nebulizer and normal breathing, Case V: Use of a mitigation box with biofilter in a Non-Invasive Positive Pressure Ventilator (NIPPV). We demonstrate that while cases I, III and IV showed efficacies of 98-100%; cases II and V, which are the most commonly used, resulted with significantly lower efficacies of 10-24% to mitigate the dispersion of nebulization aerosols. Therefore, implementing cases I, III and IV in health care facilities may help battle the contaminations and infections via aerosol transmission during a pandemic.

Development of a new Aerosol Barrier Mask for mitigation of spread of SARS-CoV-2 and other infectious pathogens.

The COVID-19 pandemic has caused huge impact on public health and significantly changed our lifestyle. This is due to the fast airborne oro-nasal transmission of SARS-CoV-2 from the infected individuals. The generation of liquid aerosolized particles occurs when the COVID-19 patients speak, sing, cough, sneeze, or simply breathe. We have developed a novel aerosol barrier mask (ABM) to mitigate the spread of SARS-CoV-2 and other infectious pathogens. This Aerosol Barrier Mask is designed for preventing SARS-CoV-2 transmission while transporting patients within hospital facilities. This mask can constrain aerosol and droplet particles and trap them in a biofilter, while the patient is normally breathing and administrated with medical oxygen. The system can be characterized as an oxygen delivery and mitigation mask which has no unfiltered exhaled air dispersion. The mask helps to prevent the spread of SARS-CoV-2, and potentially other infectious respiratory pathogens and protects everyone in general, especially healthcare professionals.

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.

A system for contact free energy expenditure assessment under free-living conditions: monitoring metabolism for weight loss using carbon dioxide emission.

Weight disorders are strikingly prevalent globally and can contribute to a wide array of potentially fatal diseases spanning from type II diabetes to coronary heart disease. These disorders have a common cause: poor calorie balance. Since energy expenditure (EE) (kcal d-1) constitutes one half of the calorie balance equation (the other half being food intake), its measurement could be of great value to those suffering from weight disorders. A technique for contact free assessment of EE is presented, which only relies on CO2concentration monitoring within a sealed office space, and assessment of carbon dioxide production rate (VCO2). Twenty healthy subjects were tested in a cross-sectional study to evaluate the performance of the aforementioned technique in measuring both resting EE (REE) and exercise EE using the proposed system (the 'SmartPad') and a U.S. Food and Drug Administration (FDA) cleared gold standard reference instrument for EE measurement. For VCO2and EE measurements, the method showed a correlation slope of 1.00 and 1.03 with regression coefficients of 0.99 and 0.99, respectively, and Bland-Altman plots with a mean bias = -0.232% with respect to the reference instrument. Furthermore, two subjects were also tested as part of a proof-of-concept longitudinal study where EE patterns were simultaneously tracked with body weight, sleep, stress, and step counts using a smartwatch over the course of a month, to determine correlation between the aforementioned parameters and EE. Analysis revealed moderately high correlation coefficients (Pearson'sr) for stress (raverage= 0.609) and body weight (raverage= 0.597) for the two subjects. The new SmartPad method was demonstrated to be a promising technique for EE measurement under free-living conditions.

Physical Activity and Trajectory of Cognitive Change in Older Persons: Mayo Clinic Study of Aging.

BACKGROUND: Little is known about the association between physical activity (PA) and cognitive trajectories in older adults. OBJECTIVE: To examine the association between PA and change in memory, language, attention, visuospatial skills, and global cognition, and a potential impact of sex or Apolipoprotein E (APOE) ɛ4 status. METHODS: Longitudinal study derived from the population-based Mayo Clinic Study of Aging, including 2,060 cognitively unimpaired males and females aged ≥70 years. Engagement in midlife (ages 50-65) and late-life (last year) PA was assessed using a questionnaire. Neuropsychological testing was done every 15 months (mean follow-up 5.8 years). We ran linear mixed-effect models to examine whether mid- or late-life PA at three intensities (mild, moderate, vigorous) was associated with cognitive z-scores. RESULTS: Light intensity midlife PA was associated with less decline in memory function compared to the no-PA reference group (time x light PA; estimate [standard error] 0.047 [0.016], p = 0.004). Vigorous late-life PA was associated with less decline in language (0.033 [0.015], p = 0.030), attention (0.032 [0.017], p = 0.050), and global cognition (0.039 [0.016], p = 0.012). Females who were physically inactive in midlife experienced more pronounced cognitive decline than females physically active in midlife and males regardless of PA (p-values for time interaction terms with midlife PA levels and sex were all p < 0.05 for global cognition). APOE ɛ4 carriership did not moderate the association between PA and cognition. CONCLUSION: Engaging in PA, particularly of vigorous intensity in late-life, was associated with less pronounced decline in global and domain-specific cognition. This association may differ by sex.

Colorimetric Sensor for Online Accurate Detection of Breath Acetone.

Breath acetone (BrAce) is a validated biomarker of lipid oxidation and has been extensively studied for many applications, such as monitoring ketoacidosis in diabetes, guiding ketogenic diet, and measuring fat burning during exercise. Although many sensors have been reported for BrAce measurement, most of the contributions tested only synthetic or spiked breath samples, because of the complex components of human breath. Here, we show that online accurate detection of BrAce can be achieved using a colorimetric sensor. The high selectivity is enabled by the specific reaction between acetone and hydroxylamine sulfate, and the sensor has a high agreement with a reference instrument in ketosis monitoring. We anticipate that the colorimetric acetone sensor can be applied to various health-related applications.

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.

Respiration pattern recognition by wearable mask device.

Compared to heart rate, body temperature and blood pressure, respiratory rate is the vital sign that has been often overlooked, largely due to the lack of easily accessible tool for reliable and natural respiration monitoring. To address this unmet need, we designed and built a wearable, stand-alone, fully integrated mask device for accurate tracking of respiration in free-living conditions. The wearable mask device can provide comprehensive respiration information in a wearable and wireless manner. It can not only accurately measure respiratory rate, tidal volume, respiratory minute volume, and peak flow rate but also recognize unique respiration pattern of the subject via Principle Component Analysis (PCA) algorithms. The reported wearable mask device and respiratory pattern recognition algorithms could be widely used in routine clinical examination, lung function assessment, asthma and chronic obstructive pulmonary disease (COPD) management, metabolic rate measurement, capnography, spirometry, sleep pattern analysis, and biometrics.

Moving Electrons Purposefully through Single Molecules and Nanostructures: A Tribute to the Science of Professor Nongjian Tao (1963-2020).

Electrochemistry intersected nanoscience 25 years ago when it became possible to control the flow of electrons through single molecules and nanostructures. Many surprises and a wealth of understanding were generated by these experiments. Professor Nongjian Tao was among the pioneering scientists who created the methods and technologies for advancing this new frontier. Achieving a deeper understanding of charge transport in molecules and low-dimensional materials was the first priority of his experiments, but he also succeeded in discovering applications in chemical sensing and biosensing for these novel nanoscopic systems. In parallel with this work, the investigation of a range of phenomena using novel optical microscopic methods was a passion of his and his students. This article is a review and an appreciation of some of his many contributions with a view to the future.

Total Iron Measurement in Human Serum With a Novel Smartphone-Based Assay.

Background: Abnormally low or high blood iron levels are common health conditions worldwide and can seriously affect an individual's overall well-being. A low-cost point-of-care technology that measures blood iron markers with a goal of both preventing and treating iron-related disorders represents a significant advancement in medical care delivery systems. Methods: A novel assay equipped with an accurate, storable, and robust dry sensor strip, as well as a smartphone mount and (iPhone) app is used to measure total iron in human serum. The sensor strip has a vertical flow design and is based on an optimized chemical reaction. The reaction strips iron ions from blood-transport proteins, reduces Fe(III) to Fe(II), and chelates Fe(II) with ferene, with the change indicated by a blue color on the strip. The smartphone mount is robust and controls the light source of the color reading App, which is calibrated to obtain output iron concentration results. The real serum samples are then used to assess iron concentrations from the new assay, and validated through intra-laboratory and inter-laboratory experiments. The intra-laboratory validation uses an optimized iron detection assay with multi-well plate spectrophotometry. The inter-laboratory validation method is performed in a commercial testing facility (LabCorp). Results: The novel assay with the dry sensor strip and smartphone mount, and App is seen to be sensitive to iron detection with a dynamic range of 50 - [Formula: see text]/dL, sensitivity of 0.00049 a.u/[Formula: see text]/dL, coefficient of variation (CV) of 10.5%, and an estimated detection limit of [Formula: see text]/dL These analytical specifications are useful for predicting iron deficiency and overloads. The optimized reference method has a sensitivity of 0.00093 a.u/[Formula: see text]/dL and CV of 2.2%. The correlation of serum iron concentrations (N = 20) between the optimized reference method and the novel assay renders a slope of 0.95, and a regression coefficient of 0.98, suggesting that the new assay is accurate. Last, a spectrophotometric study of the iron detection reaction kinetics is seen to reveal the reaction order for iron and chelating agent. Conclusion: The new assay is able to provide accurate results in intra- and inter- laboraty validations, and has promising features of both mobility and low-cost manufacturing suitable for global healthcare settings.