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Introduction: The use of sweat as a biofluid for non-invasive sampling and diagnostics is a popular area of research. However, concentrations of cortisol, glucose, and cytokines have not been described across anatomical regions or as time progresses throughout exercise., Purpose: To determine regional and time course differences in sweat cortisol, glucose, and select cytokines (EGF, IFN-γ, IL-1β, IL-1α, IL-1ra, TNF-α, IL-6, IL-8, and IL-10)., Methods: Sweat was collected with absorbent patches from eight subjects (24-44 y; 80.2 ± 10.2 kg) on the forehead (FH), right dorsal forearm (RDF), right scapula (RS), and right triceps (RT) at 0-25 min, 30-55 min, and 60-85 min during 90 min of cycling (~ 82% HR max ) in a heated chamber (32 °C, 50% rh). ANOVA was used to determine the effect of site and time on outcomes. Data are reported as LS means ± SE., Results: There was a significant effect of location on sweat analyte concentrations with FH having higher values than most other regions for cortisol (FH: 1.15 ± 0.08 ng/mL > RDF: 0.62 ± 0.09 ng/mL and RT: 0.65 ± 0.12 ng/mL, P = 0.02), IL-1ra (P < 0.0001), and IL-8 (P < 0.0001), but lower concentrations for glucose (P = 0.01), IL-1α (P < 0.0001), and IL-10 (P = 0.02). Sweat IL-1β concentration was higher on the RS than RT (P < 0.0001). Sweat cortisol concentration increased (25 min: 0.34 ± 0.10 ng/mL < 55 min: 0.89 ± 0.07 ng/mL < 85 min: 1.27 ± 0.07 ng/mL; P < 0.0001), while EGF (P < 0.0001), IL-1ra (P < 0.0001), and IL-6 (P = 0.02) concentrations decreased over time., Conclusion: Sweat analyte concentrations varied with time of sampling and anatomical region, which is essential information to consider when conducting future work in this area., Clinical Trial Identifier: NCT04240951 registered January 27, 2020., (© 2023. The Author(s).)

This study determined the relative importance of several individual characteristics and dietary, environmental, and exercise factors in determining sweat [Na + ] during exercise. Data from 1944 sweat tests were compiled for a retrospective analysis. Stepwise multiple regression ( P < 0.05 threshold for inclusion) and T values were used to express the relative importance of each factor in a model. Three separate models were developed based on available independent variables: model 1 (1,944 sweat tests from 1,304 subjects); model 2 (subset with energy expenditure: 1,003 sweat tests from 607 subjects); model 3 (subset with energy expenditure, dietary sodium, and V̇o 2max : n = 48). Whole body sweat [Na + ] was predicted from forearm sweat patches in models 1 and 2 and directly measured using whole body washdown in model 3 . There were no significant effects of age group, race/ethnicity, relative humidity, exercise duration, pre-exercise urine specific gravity, exercise fluid balance, or dietary or exercise sodium intake on any model. Significant predictors in model 1 (adjusted r 2 = 0.17, P < 0.001) were season of the year (warm, T = -6.8), exercise mode (cycling, T = 6.8), sex (male, T = 4.9), whole body sweating rate ( T = 4.5), and body mass ( T = -3.0). Significant predictors in model 2 (adjusted r 2 = 0.19, P < 0.001) were season of the year (warm, T = -5.2), energy expenditure ( T = 4.7), exercise mode (cycling, T = 3.6), air temperature ( T = 3.0), and sex (male, T = 2.7). The only significant predictor in model 3 ( r 2 = 0.23, P < 0.001) was energy expenditure ( T = 3.8). In summary, the models accounted for 17%-23% of the variation in whole body sweat [Na + ] and energy expenditure and season of the year (proxy for heat acclimatization) were the most important factors. NEW & NOTEWORTHY This comprehensive analysis of a large, diverse data set contributes to our overall understanding of the factors that influence whole body sweat [Na + ]. The main finding was that energy expenditure was directly associated with whole body sweat [Na + ], potentially via the relation between energy expenditure and whole body sweating rate (WBSR). Warmer months (proxy for heat acclimatization) were associated with lower whole body sweat [Na + ]. Exercise mode, air temperature, and sex may also have small effects, but other variables (age group, race/ethnicity, fluid balance, sodium intake, relative V̇o 2max ) had no association with whole body sweat [Na + ]. Taken together, the models explained 17%-23% of the variation in whole body sweat [Na + ].

Purpose: To compare local sweating rate (LSR) and local sweat sodium ([Na + ]), chloride ([Cl - ]), and potassium ([K + ]) concentrations of tattooed skin and contralateral non-tattooed skin during exercise., Methods: Thirty-three recreational exercisers (17 men, 16 women) with ≥ 1 unilateral permanent tattoo on the torso/arms were tested during cycling, running, or fitness sessions (26 ± 4 °C and 54 ± 13% relative humidity). Forty-eight tattoos with a range of ink colors, ages (3 weeks to 20 years), and densities (10-100%) were included. Before exercise, the skin was cleaned with alcohol and patches (3 M Tegaderm + Pad) were placed on the tattooed and contralateral non-tattooed skin. LSR was calculated from sweat mass (0.80 ± 0.31 g), patch surface area (11.9 cm 2 ), and duration (62 ± 14 min). Sweat [Na + ], [Cl - ], and [K + ] were measured via ion chromatography., Results: Based on the analysis of variance results, there were no differences between tattooed and non-tattooed skin for LSR (1.16 ± 0.52 vs. 1.12 ± 0.53 mg/cm 2 /min; p = 0.51), sweat [Na + ] (60.2 ± 23.5 vs. 58.5 ± 22.7 mmol/L; p = 0.27), sweat [Cl - ] (52.1 ± 22.4 vs. 50.6 ± 22.0 mmol/L; p = 0.31), or sweat [K + ] (5.8 ± 1.6 vs. 5.9 ± 1.4 mmol/L; p = 0.31). Multiple regression analyses suggested that younger tattoos were associated with higher sweat [Na + ] (p = 0.045) and colorful tattoos were associated with higher sweat [Cl - ] (p = 0.04) compared with contralateral non-tattooed skin. Otherwise, there were no effects of LSR or tattoo characteristics on regression models for LSR or sweat electrolyte concentrations., Conclusion: There were no effects of tattoos on LSR and sweat [K + ] during exercise-induced sweating, but tattoo age and color had small effects on sweat [Na + ] and sweat [Cl - ], respectively., Clinical Trial Identifiers: NCT04240951 was registered on January 27, 2020 and NCT04920266 was registered on June 9, 2021., (© 2022. The Author(s).)

The purpose of this study was to compare a wearable microfluidic device and standard absorbent patch in measuring local sweating rate (LSR) and sweat chloride concentration ([Cl-]) in elite basketball players. Participants were 53 male basketball players (25 ± 3 years, 92.2 ± 10.4 kg) in the National Basketball Association's development league. Players were tested during a moderate-intensity, coach-led practice (98 ± 30 min, 21.0 ± 1.2 °C). From the right ventral forearm, sweat was collected using an absorbent patch (3M Tegaderm™ + Pad). Subsequently, LSR and local sweat [Cl-] were determined via gravimetry and ion chromatography. From the left ventral forearm, LSR and local sweat [Cl-] were measured using a wearable microfluidic device and associated smartphone application-based algorithms. Whole-body sweating rate (WBSR) was determined from pre- to postexercise change in body mass corrected for fluid/food intake (ad libitum), urine loss, and estimated respiratory water and metabolic mass loss. The WBSR values predicted by the algorithms in the smartphone application were also recorded. There were no differences between the absorbent patch and microfluidic patch for LSR (1.25 ± 0.91 mg·cm-2·min-1 vs. 1.14 ±0.78 mg·cm-2·min-1, p = .34) or local sweat [Cl-] (30.6 ± 17.3 mmol/L vs. 29.6 ± 19.4 mmol/L, p = .55). There was no difference between measured and predicted WBSR (0.97 ± 0.41 L/hr vs. 0.89 ± 0.35 L/hr, p = .22; 95% limits of agreement = 0.61 L/hr). The wearable microfluidic device provides similar LSR, local sweat [Cl-], and WBSR results compared with standard field-based methods in elite male basketball players during moderate-intensity practices.

Advanced capabilities in noninvasive, in situ monitoring of sweating rate and sweat electrolyte losses could enable real-time personalized fluid-electrolyte intake recommendations. Established sweat analysis techniques using absorbent patches require post-collection harvesting and benchtop analysis of sweat and are thus impractical for ambulatory use. Here, we introduce a skin-interfaced wearable microfluidic device and smartphone image processing platform that enable analysis of regional sweating rate and sweat chloride concentration ([Cl - ]). Systematic studies ( n = 312 athletes) establish significant correlations for regional sweating rate and sweat [Cl - ] in a controlled environment and during competitive sports under varying environmental conditions. The regional sweating rate and sweat [Cl - ] results serve as inputs to algorithms implemented on a smartphone software application that predicts whole-body sweating rate and sweat [Cl - ]. This low-cost wearable sensing approach could improve the accessibility of physiological insights available to sports scientists, practitioners, and athletes to inform hydration strategies in real-world ambulatory settings., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)

We have previously published equations to estimate whole-body (WB) sweat sodium concentration ([Na + ]) from regional (REG) measures; however, a cross-validation is needed to corroborate the applicability of these prediction equations between studies. The purpose of this study was to determine the validity of published equations in predicting WB sweat [Na + ] from REG measures when applied to a new data set. Forty-nine participants (34 men, 15 women; 75 ± 12 kg) cycled for 90 min while WB sweat [Na + ] was measured using the washdown technique. REG sweat [Na + ] was measured from seven regions using absorbent patches (3M Tegaderm + Pad). Published equations were applied to REG sweat [Na + ] to determine predicted WB sweat [Na + ]. Bland-Altman analysis of mean bias (raw and predicted minus measured) and 95% limits of agreement (LOA) were used to compare raw (uncorrected) REG sweat [Na + ] and predicted WB sweat [Na + ] to measured WB sweat [Na + ]. Mean bias (±95% LOA) between raw REG sweat [Na + ] and measured WB sweat [Na + ] was 10(±20), 0(±19), 9(±20), 22(±25), 23(±24), 0(±15), -4(±18) mmol/L for the dorsal forearm, ventral forearm, upper arm, chest, upper back, thigh, and calf, respectively. The mean bias (±95% LOA) between predicted WB sweat [Na + ] and measured WB sweat [Na + ] was 3(±14), 4(±12), 0(±14), 2(±17), -2(±16), 5(±13), 4(±15) mmol/L for the dorsal forearm, ventral forearm, upper arm, chest, upper back, thigh, and calf, respectively. Prediction equations improve the accuracy of estimating WB sweat [Na + ] from REG and are therefore recommended for use when determining individualized sweat electrolyte losses., (© 2020 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society.)

Rhodopseudomonas palustris, a nonsulphur purple photosynthetic bacteria, has been extensively investigated for its metabolic versatility including ability to produce hydrogen gas from sunlight and biomass. The availability of the finished genome sequences of six R. palustris strains (BisA53, BisB18, BisB5, CGA009, HaA2 and TIE-1) combined with online bioinformatics software for integrated analysis presents new opportunities to determine the genomic basis of metabolic versatility and ecological lifestyles of the bacteria species. The purpose of this investigation was to compare the functional annotations available for multiple R. palustris genomes to identify annotations that can be further investigated for strain-specific or uniquely shared phenotypic characteristics. A total of 2,355 protein family Pfam domain annotations were clustered based on presence or absence in the six genomes. The clustering process identified groups of functional annotations including those that could be verified as strain-specific or uniquely shared phenotypes. For example, genes encoding water/glycerol transport were present in the genome sequences of strains CGA009 and BisB5, but absent in strains BisA53, BisB18, HaA2 and TIE-1. Protein structural homology modeling predicted that the two orthologous 240 aa R. palustris aquaporins have water-specific transport function. Based on observations in other microbes, the presence of aquaporin in R. palustris strains may improve freeze tolerance in natural conditions of rapid freezing such as nitrogen fixation at low temperatures where access to liquid water is a limiting factor for nitrogenase activation. In the case of adaptive loss of aquaporin genes, strains may be better adapted to survive in conditions of high-sugar content such as fermentation of biomass for biohydrogen production. Finally, web-based resources were developed to allow for interactive, user-defined selection of the relationship between protein family annotations and the R. palustris genomes.

Foodborne illnesses caused by microbial and chemical contaminants in food are a substantial health burden worldwide. In 2007, human vibriosis (non-cholera Vibrio infections) became a notifiable disease in the United States. In addition, Vibrio species are among the 31 major known pathogens transmitted through food in the United States. Diverse surveillance systems for foodborne pathogens also track outbreaks, illnesses, hospitalization and deaths due to non-cholera vibrios. Considering the recognition of vibriosis as a notifiable disease in the United States and the availability of diverse surveillance systems, there is a need for the development of easily deployed visualization and analysis approaches that can combine diverse data sources in an interactive manner. Current efforts to address this need are still limited. Visual analytics is an iterative process conducted via visual interfaces that involves collecting information, data preprocessing, knowledge representation, interaction, and decision making. We have utilized public domain outbreak and surveillance data sources covering 1973 to 2010, as well as visual analytics software to demonstrate integrated and interactive visualizations of data on foodborne outbreaks and surveillance of Vibrio species. Through the data visualization, we were able to identify unique patterns and/or novel relationships within and across datasets regarding (i) causative agent; (ii) foodborne outbreaks and illness per state; (iii) location of infection; (iv) vehicle (food) of infection; (v) anatomical site of isolation of Vibrio species; (vi) patients and complications of vibriosis; (vii) incidence of laboratory-confirmed vibriosis and V. parahaemolyticus outbreaks. The additional use of emerging visual analytics approaches for interaction with data on vibriosis, including non-foodborne related disease, can guide disease control and prevention as well as ongoing outbreak investigations.

The draft nuclear genome sequence of the snail-transmitted, dimorphic, parasitic, platyhelminth Schistosoma mansoni revealed eight genes encoding proteins that contain the Universal Stress Protein (USP) domain. Schistosoma mansoni is a causative agent of human schistosomiasis, a severe and debilitating Neglected Tropical Disease (NTD) of poverty, which is endemic in at least 76 countries. The availability of the genome sequences of Schistosoma species presents opportunities for bioinformatics and genomics analyses of associated gene families that could be targets for understanding schistosomiasis ecology, intervention, prevention and control. Proteins with the USP domain are known to provide bacteria, archaea, fungi, protists and plants with the ability to respond to diverse environmental stresses. In this research investigation, the functional annotations of the USP genes and predicted nucleotide and protein sequences were initially verified. Subsequently, sequence clusters and distinctive features of the sequences were determined. A total of twelve ligand binding sites were predicted based on alignment to the ATP-binding universal stress protein from Methanocaldococcus jannaschii. In addition, six USP sequences showed the presence of ATP-binding motif residues indicating that they may be regulated by ATP. Public domain gene expression data and RT-PCR assays confirmed that all the S. mansoni USP genes were transcribed in at least one of the developmental life cycle stages of the helminth. Six of these genes were up-regulated in the miracidium, a free-swimming stage that is critical for transmission to the snail intermediate host. It is possible that during the intra-snail stages, S. mansoni gene transcripts for universal stress proteins are low abundant and are induced to perform specialized functions triggered by environmental stressors such as oxidative stress due to hydrogen peroxide that is present in the snail hemocytes. This report serves to catalyze the formation of a network of researchers to understand the function and regulation of the universal stress proteins encoded in genomes of schistosomes and their snail intermediate hosts.