Forzani, Erica

Ketones are well-known biomarkers of fat oxidation produced in the liver as a result of lipolysis. These biomarkers include acetoacetic acid and β-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.

Abstract In the initial stages of the COVID-19 pandemic, it became evident that singing carried a substantial risk of virus transmission. Consequently, new regulations were implemented to safeguard the population, and the adoption of singing masks emerged as a pivotal measure to curb the spread of COVID-19 during vocal performances. However, the discomfort and impediment to sensory expression caused by these masks posed significant challenges, particularly for individuals whose passions or professions revolved around singing. This study shows a novel solution to address these issues: the Aerosol Barrier System for Singing. The system features a transparent screen with an Impactor-Fan-Filter. The Impactor-Fan-Filter (IFF) is integrated into the transparent screen and strategically positioned within 1.5 feet of the singer's face. We demonstrated that the system effectively reduces the dispersion of aerosols generated during singing by over 99%. In addition, comparative sound analysis indicated that the system minimizes interference with sound waves, ensuring the preservation of sound quality. Furthermore, the transparent screen offers an unobstructed view of the singer, enhancing the overall performance experience. Our system adheres to several crucial criteria for an effective solution tailored to the needs of singers: it prioritizes safety and comfort, upholds sound quality, and facilitates an unrestricted view of the singer's entire face for the benefit of the audience, conductors, and ensemble members.

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.

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.

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).

Abstract 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.

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.

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.

Abstract Background: Elastomeric respirators are reusable and reliable protection from infectious aerosol particles such as SARS-CoV-2. There is a lack of safety data for use in sterile fields limiting application to operating room settings where high-risk aerosol generating procedures are performed. We hypothesized an equivalent reduction in sterile field contamination would be achieved using an elastomeric respirator covered by a surgical mask as compared to a standard surgical mask or N95.Methods: Randomized controlled crossover experiment with repeat measurement comparing microbial and aerosol contamination of operating room surfaces for elastomeric respirators, elastomeric respirators covered by a surgical mask, N95, surgical mask, and no mask. 80 experiments were performed by participants with randomized order and balanced crossover to all masking groups (n=16 per masking group). Participants executed droplet and aerosol generating procedures while wearing: (Group 1) elastomeric respirator with mask, (Group 2) elastomeric respirator only, (Group 3) N95, and (Group 4) surgical mask. Positive control was established with the participant unmasked (Group 5). Contamination was measured by microbial growth on settling plates and optical particle counters (0.2+ and 2 um+ particles).Results: There was a reduction in microbial contamination at the sterile field (p<0.001) for all masks (Groups 1-4) compared to unmasked (Group 5). The mean colony forming units (CFU) at the sterile field was 0 CFUs for elastomeric respirator (+/- mask), N95, and surgical mask versus unmasked growing 1.875 CFUs. Compared to the unmasked control, the elastomeric respirator (+/- mask), N95, and surgical masks all resulted in a -0.75 difference in contamination (95% CI -0.91 to -0.48, p < 0.001). No significant difference in contamination between the elastomeric respirator (+/- mask) and a surgical mask was detected. No significant difference in particle counts (0.2 µm+ and 2.0 µm+) between the elastomeric respirator (+/- mask) and a standard surgical mask.Conclusion and Relevance: Elastomeric respirators with or without mitigation are as effective as a surgical mask and N95 for reducing contamination at the sterile field in an operating room. Consideration should be given to expanding the use of elastomeric respirators to operating rooms during high-risk aerosol generating procedures.