Bioanalysis with blood has been one of the most significant diagnostic tools for modern medicine. However, owing to its invasive nature, it is not accessible to most people until annual physical examination or development of an obvious disease symptom. Application of this powerful tool to early diagnostics and treatment of chronic disease (e.g., diabetes (Vargas et al., 2019)) is highly restricted (Bariya et al., 2018). As a consequence, quite a few diseases are not properly diagnosed until they advance to the middle or late stage, and their treatment is much more challenging than in the early stage. Therefore, people are increasingly interested in non-invasive bioanalysis with externally secreted body fluids (e.g., sweat) for continuous health monitoring and early diagnostics of diseases, leading to development of various wearables (Gao et al., 2016; Keene et al., 2019; Koh et al., 2016; Moreddu et al., 2020).
Skin is the largest human organ characterized by a lot of external secretory glands such as eccrine, apocrine and sebaceous glands. For the eccrine gland, there are 2-4 million of them distributed all over the skin. One of the crucial functions of the eccrine gland is sweating for thermoregulation (Baker, 2019; Quinton, 1983). For this purpose, sweat is continuously generated on the skin even without any external stimulation. The normal sweat flow rate ranges from 10 to 250nLmin−1 cm−2 (Emaminejad et al., 2017; James et al., 2012; Lin et al., 2020), and sweat contains many types of biochemical species, which originate mainly from interstitial fluid so that their levels are closely correlated to physiological state. Therefore, real-time sweat bioanalysis can provide a sensitive means for non-invasive diagnostics and health management.
Although the collection of sweat is non-invasive, it is not user-friendly in most current practice. Without stimulation, secretion of sweat is of a very low rate, which is hard to observe or collect due to evaporation (Baker, 2019; Quinton, 1983). Consequently, stimulated sweat induced by iontophoresis (Emaminejad et al., 2017; Simmers et al., 2018), exercise (Tai et al., 2018; Yang et al., 2020), or heat (Zhang et al., 2019) for the sampling of substantial volume is often involved in current sweat analysis. For elderly people, patients and people with limited mobility, stimulated sweat by exercise or heat is problematic. Sweat collection by iontophoresis can be painful and may cause skin irritation. Frequent stimulation using drugs may lead to tolerance and decreased sweat rate. Therefore, the application of the stimulated sweat collection is highly restricted (Baker, 2019; Kim et al., 2019; Lin et al., 2020).
Furthermore, current interpretation of sweat test results relies on an established blood-sweat correlation so that the sweat target level can be converted to its corresponding blood level. A time lag is usually reported for target concentrations in blood and sweat (Baker, 2019; Emaminejad et al., 2017; James et al., 2012; Sonner et al., 2015). However, the levels of biochemical species in sweat are dramatically affected by a range of factors that can hardly be predicted or related to health, including reabsorption, evaporation, sweat rate, substances on the epidermis and even metabolism of eccrine glands (Baker, 2019; Kim et al., 2019; Quinton, 1983). The stimulation for sweat can remarkably distort the blood-sweat correlation and undermine the clinical significance of the sweat test results.
Therefore, the collection of representative samples plays a pivotal role in clinical application of sweat bioanalysis, which has drawn considerable research attention (Baker, 2019; Lin et al., 2020; Quinton, 1983). To avoid the problems of stimulated sweat, collection of unstimulated sweat for bioanalysis has been reported (Baker, 2019; Emaminejad et al., 2017; Kim et al., 2019). For example, Bariya and coworkers proposed a glove-based method to collect naturally secreted sweat for bioanalysis (Bariya et al., 2020). Nyein and coworkers reported a wearable microfluidic device for sampling and monitoring pH, Cl− and levodopa in unstimulated sweat (Nyein et al., 2021). Nagamine and coworkers used agarose gel to extract lactic acid in sweat for continuous potentiometric detection (Nagamine et al., 2019). Likewise, Shuyu and coworkers reported the use of agarose hydrogel for analysis of lactic acid and caffeine in sweat (Lin et al., 2020). However, most of these methods are limited to simple one-step detection, and multi-step bioanalysis for a wide range of valuable targets is still challenging.
Here we report a versatile method for on-demand sweat bioanalysis based on hydrogel-programmed wearables. Unlike previously reported hydrogel-based sensors, which depend on sweat diffusion in the hydrogel to the electrode surface (Moon et al., 2022; Saha et al. 2021, 2022; Sempionatto et al., 2021), the sensor we propose utilizes a thermoresponsive hydrogel that can not only passively collect the sweat but also actively release the sweat into a microfluidic channel. Our design facilitates the formation of a stable electrode-solution interface and sequential reactions. The hydrogel involved is a thermoresponsive poly(n-isopropyl acrylamide) (pNIPAM) polymer that is featured by a lower critical solution temperature (LCST). Under its LCST (i.e., 42°C), the hydrogel gradually absorbs sweat that is slowly secreted from the skin. Above the LCST, the hydrogel undergoes a phase transition and releases the absorbed sweat or preloaded reagents into a microfluidic channel for bioanalysis. The fluidic manipulation process is programmed by electric heating to accomplish not only one-step detection of glucose but also multi-step immunoassay of cortisol on the wearables. Using our method, on-demand bioanalysis can be performed within 1h without disturbing daily life from stimulated sweat collection. We also evaluate the clinical applicability of our method by comparing our test results with those obtained in blood samples or conventional stimulated sweat samples.
Chemicals and materials
N-isopropyl acrylamide (NIPAM), acrylamide (AM), poly (ethylene glycol) diacrylate (PEGDA), hydrocortisone, diamminedinitritoplatinum(II), sulfamic acid, ammonium sulfamate, 2-hydroxy-2-methylpopiophenone (HMPP) and disodium 3,3′-dithiobis(1-propanesulfonate) (DSP) were obtained from Aladdin (Shanghai, China). D-glucose, uric acid and urea were obtained from Macklin (Shanghai, China). Lactic acid (LA), glucose oxidase (GOx) and Nafion perfluorinated solution were obtained from Sigma-Aldrich.
Results and discussion
Unstimulated sweat has a much lower secretion rate, which is approximately an order of magnitude lower than the rate of sweat secretion by active stimulation (Taylor and Machado-Moreira, 2013). As shown in Fig. 1A, the pNIPAM-based hydrogel was used for continuous absorption of the unstimulated sweat on the skin before evaporation. The hydrogel was also thermally responsive so that it could undergo phase transition with electric heating to release the absorbed sweat into the microfluidic
Conclusion and outlook
In summary, we have developed a hydrogel-programmed wearable sweat bioanalysis platform. It was based on the thermoresponsive hydrogels that can absorb the natural sweat and release the solution by electric heating. Notably, the sweat collection requires no external stimulation such as heat or exercise, enabling user-friendly detection during daily life and maintaining blood-sweat glucose correlation. Without cumbersome pumps or manual operation, automated bioanalysis can be achieved almost at
Data and materials availability
All data are available in the main text or the Supplementary data.
CRediT authorship contribution statement
Yichen Chen: Methodology, Data curation, Formal analysis, Validation, Writing – original draft. Biao Ma: Writing – review & editing, Conceptualization, Methodology. Yinxiu Zuo: Methodology. Gangsheng Chen: Writing – review & editing, Conceptualization, Methodology. Qing Hao: Writing - review, Funding acquisition, Project administration. Chao Zhao: Funding acquisition, Project administration. Hong Liu: Writing – review & editing, Funding acquisition, Project administration.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
This work was supported by the Key Research and Development Program of Jiangsu Province (BE2021700), National Natural Science Foundation of China (62001104, 62271136), National Key Research and Development Plan (2022YFF1201803, 2021YFB2600800), Natural Science Foundation of Jiangsu Province (BK20200357), Science and Technology Development Program of Suzhou (SYG202117), Key Project and Open Research Fund of State Key Laboratory of Bioelectronics, the Fundamental Research Funds for the Central
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