Two Distinct Types of Sweat Profile in Healthy Subjects While Exercising at Constant Power Output Measured by a Wearable Sweat Sensor

Choi, Dong-Hoon; Kitchen, Grant; Kim, Ji Soo; Li, Yang; Kim, Kain; Jeong, In Cheol; Nguyen, Judy V.; Stewart, Kerry J.; Zeger, Scott L.; Searson, Peter C.

doi:10.1038/s41598-019-54202-1

Cited by 10 publications

(7 citation statements)

References 38 publications

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“…Multimarker Analysis by ML. We applied two common ML algorithms (RF 41 and NN 42 ) to illustrate the importance of multimarker approach (Figure 4a). Both algorithms were trained by 70% of the total data set under supervised learning and validated its performance by screening PCa from a blinded test set (30% of the total data set).…”

Section: Resultsmentioning

confidence: 99%

Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis

et al. 2020

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Screening for prostate cancer relies on the serum prostate-specific antigen test, which provides a high rate of false positives (80%). This results in a large number of unnecessary biopsies and subsequent overtreatment. Considering the frequency of the test, there is a critical unmet need of precision screening for prostate cancer. Here, we introduced a urinary multimarker biosensor with a capacity to learn to achieve this goal. The correlation of clinical state with the sensing signals from urinary multimarkers was analyzed by two common machine learning algorithms. As the number of biomarkers was increased, both algorithms provided a monotonic increase in screening performance. Under the best combination of biomarkers, the machine learning algorithms screened prostate cancer patients with more than 99% accuracy using 76 urine specimens. Urinary multimarker biosensor leveraged by machine learning analysis can be an important strategy of precision screening for cancers using a drop of bodily fluid.

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Section: Resultsmentioning

confidence: 99%

Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis

et al. 2020

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“…Monitoring of electrolyte loss requires measurement of electrolyte concentration (either Cl – or Na + ) and sweat rate. Wearable sensors for the measurement of sweat concentration are relatively advanced, − and hence the development of wearable sweat rate sensors has the potential to enable integrated measurement of electrolyte loss in real time.…”

Section: Discussionmentioning

confidence: 99%

A Capacitive Sweat Rate Sensor for Continuous and Real-Time Monitoring of Sweat Loss

et al. 2020

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Individualized measurement of sweat loss under heat stress is important in assessing physical performance and preventing heat-related illness for athletes or individuals working in extreme environments. The objective of this work was to develop a low-cost and easy-to-fabricate wearable sensor that enables accurate real-time measurement of sweat rate. A capacitive-type sensor was fabricated from two conducting parallel plates, plastic insulating layers, and a central microfluidic channel formed by laser cutting a plastic film. The device has no microfabricated electrodes and is assembled using adhesive tape. Sensor accuracy was validated at different flow rates and confirmed using an equivalent circuit model of the device. On-body measurements demonstrate the feasibility of real-time measurements and show good agreement with values determined from a conventional sweat collection device.

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“…The sweat chloride concentration is generally thought to increase with increasing sweat rate due to an associated decrease in the reabsorption efficiency in the sweat ducts 2,23,24,29 , which is thought to be dependent on factors such as fitness level, training status, and heat acclimation 29,30 . An approximately linear dependence has been reported for exercise-induced sweating 23,24 .…”

Section: Discussionmentioning

confidence: 99%

“…To establish a steady state response, we define the sweat chloride concentration in the first plateau region where the slope of the sweat profile is smaller than 0.1 mM min −1 for at least 5 minutes. Plateau regions were determined using a sequential linear regression (least squares fit) in a moving 5-minute window using a customized MATLAB code 30 . The start times of the plateau region for 12 subjects during the single and reverse step trials were 21.1 ± 7.3 and 18.0 ± 4.4 minutes, respectively.…”

Section: Discussionmentioning

confidence: 99%

The Dynamic Response of Sweat Chloride to Changes in Exercise Load Measured by a Wearable Sweat Sensor

Choi

Kitchen

Stewart

et al. 2020

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Wearable sensors enable the monitoring of an individual's sweat composition in real time. in this work, we recorded real-time sweat chloride concentration for 12 healthy subjects in three different protocols involving step changes in exercise load and compared the results to laboratory-based analysis. the sensor results reflected the changes in exercise load in real time. On increasing the exercise load from 100 W to 200 W the sweat chloride concentration increased from 12.0 ± 5.9 to 31.4 ± 16 mM (mean ± SD). On decreasing the load from 200 W to 100 W, the sweat chloride concentration decreased from 27.7 ± 10.5 to 14.8 ± 8.1 mM. The half-time associated with the change in sweat chloride, defined as the time at which the concentration reached half of the overall change, was about 6 minutes. While the changes in sweat chloride were statistically significant, there was no correlation with changes in sweat rate or other physiological parameters, which we attribute to intra-individual variation (SD = 1.6-8.1 mM). The response to exercise-induced sweating was significantly different to chemically-induced sweating where the sweat chloride concentration was almost independent of sweat rate. We speculate that this difference is related to changes in the open probability of the CFTR channel during exercise, resulting in a decrease in reabsorption efficiency at higher sweat rates. The most effective method of thermal regulation during exercise is sweating, however, excessive water and electrolyte losses can contribute to dehydration and electrolyte imbalances 1-4. Measurement of electrolyte loss during sweating usually involves absorption patches, collection devices (e.g. Macroduct), or plastic bags to collect samples to be sent to a laboratory for analysis 4-8. Since sweat collection times are generally in the range of 5 to 90 minutes, depending on the study 8 , laboratory analysis has limited capability for elucidating real-time, dynamic changes during exercise. Recent advances in wearable sweat sensors 9-13 have enabled real-time measurement of water and electrolyte loss during exercise. In addition, continuous measurement enables detection of transient changes that could be masked by the sweat collection time in conventional measurements. Finally, wearable sensors require very small volumes for measurement in comparison to standard laboratory-based tests. For example a sweat test for diagnosis of cystic fibrosis requires a minimum sweat volume of 15 µL 14 whereas a sweat sensor can detect sweat volumes less than 1 μL 15. Electrolytes regulate fluid balance in plasma and tissues and are involved in cell signaling 16 , and electrolyte imbalance can lead to a wide range of medical conditions 17,18. Chloride and sodium are the most abundant electrolytes and their concentrations are closely correlated 19. In this work, to assess the feasibility of using a wearable sensor to evaluate transient changes in real-time, we performed a systematic study of the dynamic response of sweat chloride to step changes in exercise load. We p...

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Two Distinct Types of Sweat Profile in Healthy Subjects While Exercising at Constant Power Output Measured by a Wearable Sweat Sensor

Cited by 10 publications

References 38 publications

Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis

Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis

A Capacitive Sweat Rate Sensor for Continuous and Real-Time Monitoring of Sweat Loss

The Dynamic Response of Sweat Chloride to Changes in Exercise Load Measured by a Wearable Sweat Sensor

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