Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study

Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured four noninvasive physiological and sweat biomarkers including heart rate, core temperature, sweat sodium concentration, and whole-body sweat rate. Sweat sodium concentration was measured from six body regions using absorbent patches. We used three machine learning models to determine the percentage of body weight loss as an indicator of dehydration with these biomarkers and compared the prediction accuracy. The results on this single subject show that these models gave similar mean absolute errors, while in general the nonlinear models slightly outperformed the linear model in most of the experiments. The prediction accuracy of using the whole-body sweat rate or heart rate was higher than using core temperature or sweat sodium concentration. In addition, the model trained on the sweat sodium concentration collected from the arms gave slightly better accuracy than from the other five body regions. This exploratory work paves the way for the use of these machine learning models to develop personalized health monitoring together with emerging, noninvasive wearable sensor devices

Multisensing Wearables for Real-Time Monitoring of Sweat Electrolyte Biomarkers During Exercise and Analysis on Their Correlation With Core Body Temperature

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. This work describes a wearable sweat biomonitoring patch incorporating printed electrochemical sensors into a plastic microfluidic sweat collector and data analysis that shows the real-time recorded sweat biomarkers can be used to predict a physiological biomarker. The system was placed on subjects carrying out an hour-long exercise session and results were compared to a wearable system using potentiometric robust silicon-based sensors and to commercially available HORIBA-LAQUAtwin devices. Both prototypes were applied to the real-time monitoring of sweat during cycling sessions and showed stable readings for around an hour. Analysis of the sweat biomarkers collected from the printed patch prototype shows that their real-time measurements correlate well (correlation coefficient ≥ 0.65) with other physiological biomarkers such as heart rate and regional sweat rate collected in the same session. We show for the first time, that the real-time sweat sodium and potassium concentration biomarker measurements from the printed sensors can be used to predict the core body temperature with root mean square error (RMSE) of 0.02 °C which is 71% lower compared to the use of only the physiological biomarkers. These results show that these wearable patch technologies are promising for real-time portable sweat monitoring analytical platforms, especially for athletes performing endurance exercise.Peer reviewe

Correction: Distinct germline genetic susceptibility profiles identified for common non-Hodgkin lymphoma subtypes

Correction to: Leukemia https://doi.org/10.1038/s41375-022-01711-0, published online 22 October 202

Musicians and the metric structure of words

*no abstrac

Musicians and the metric structure of words

*no abstrac

Influence of Musical Expertise on Segmental and Tonal Processing in Mandarin Chinese

A same-different task was used to test the hypothesis that musical expertise improves the discrimination of tonal and segmental (consonant, vowel) variations in a tone language, Mandarin Chinese. Two four-word sequences (prime and target) were presented to French musicians and nonmusicians unfamiliar with Mandarin, and event-related brain potentials were recorded. Musicians detected both tonal and segmental variations more accurately than nonmusicians. Moreover, tonal variations were associated with higher error rate than segmental variations and elicited an increased N2/N3 component that developed 100 msec earlier in musicians than in nonmusicians. Finally, musicians also showed enhanced P3b components to both tonal and segmental variations. These results clearly show that musical expertise influenced the perceptual processing as well as the categorization of linguistic contrasts in a foreign language. They show positive music-to-language transfer effects and open new perspectives for the learning of tone languages

Mining bi-sets in numerical data

Thanks to an important research effort the last few years, inductive queries on set patterns and complete solvers which can evaluate them on large 0/1 data sets have been proved extremely useful. However, for many application domains, the raw data is numerical (matrices of real numbers whose dimensions denote objects and properties). Therefore, using efficient 0/1 mining techniques needs for tedious Boolean property encoding phases. This is, e.g., the case, when considering microarray data mining and its impact for knowledge discovery in molecular biology. We consider the possibility to mine directly numerical data to extract collections of relevant bi-sets, i.e., couples of associated sets of objects and attributes which satisfy some user-defined constraints. Not only we propose a new pattern domain but also we introduce a complete solver for computing the so-called numerical bi-sets. Preliminary experimental validation is given. © Springer-Verlag Berlin Heidelberg 2007.status: publishe

NEW INSIGHTS INTO THE HEPATIC IRON PHENOTYPE OF BMP6 KNOCKOUT MICE

International audienceObjective: Bmp6 knockout (KO) mice progressively accumulate a significant amount of iron in their liver as they age due to a defect in hepcidin (Hamp) expression and an upregulation of the iron exporter ferroportin (Fpn). In this study, we conducted a comprehensive investigation of the hepatic iron overload phenotype, with specific emphasis on the cellular and subcellular localization of Fpn in Bmp6 KO mice. Materials and Methods: Livers obtained from Bmp6 knockout (KO) mice at different developmental stages were utilized for the quantification of iron content, investigation of iron distribution, histological analysis, histoimmunofluorescence assays performed on paraffin-embedded sections, confocal microscopy examinations, subcellular membrane fractionation, and western blot analysis. Results: In Bmp6 KO livers, iron overload increased with age and was not homogeneous, with certain hepatic lobes and specific areas in liver sections showing more pronounced iron accumulation. In young mice, iron accumulated mostly in the centrilobular zone where low Fpn expression was observed. Fpn was strongly detected in periportal Kupffer cells and at the apical membrane of periportal hepatocytes lining the sinusoidal capillaries. The zonal distribution of iron tended to disappear with age in strongly iron-overloaded areas, with the appearance of large cellular aggregates strongly positive for Fpn, iron, and ceroid/lipofuscin. At the subcellular level, hepatic Fpn seemed to concentrate in specific cell surface compartments and was enriched in a lipid raft fraction. Conclusions: Unregulated expression of Fpn on the cell surface of periportal macrophages and hepatocytes results in centrilobular iron overload within hepatocytes. In areas of pronounced iron overload, Fpn expression is present in lipogranulomas, identified as aggregations of macrophages accumulating hemosiderin and ceroid/lipofuscin pigments. These lesions likely form due to the phagocytosis of sideronecrotic/ferroptotic hepatocytes by macrophages. In contrast to the duodenal form of Fpn, both splenic and hepatic Fpn demonstrated robust enrichment within lipid rafts. The observed variations in the subcellular localization of Fpn could play a significant role in influencing the transporter's iron transport activity and/or its regulation by hepcidin

Glycol-split nonanticoagulant heparins are inhibitors of hepcidin expression in vitro and in vivo

Hepcidin controls systemic iron availability, and its excess contributes to the anemia of chronic diseases, the most prevalent anemia in hospitalized patients. We previously reported that heparins are efficient hepcidin inhibitors both in vitro and in vivo, but their anticoagulant activity limits therapeutic use. We studied nonanticoagulant heparins produced by N-acetylation and oxidation/reduction (glycol-split) that lost antithrombin-binding affinity. Four nonanticoagulant heparins inhibited hepcidin expression in hepatic HepG2 cells and primary hepatocytes. The 2 most potent ones used in mice suppressed liver hepcidin expression and serum hepcidin in 6 hours, with a significant decrease of spleen iron. This occurred also in lipopolysaccharide (LPS)-treated animals that mimic inflammation, as well as after chronic 1-week treatments, without evident adverse effects on coagulation. Heparin injections increased iron mobilization and facilitated the recovery from the anemia induced by heat-killed Brucella abortus, a model of inflammatory anemia. The heparins were used also in Bmp6(-/-) mice. A single dose of heparin reduced the already low level of hepcidin of these mice and prevented its induction by LPS. These nonanticoagulant compounds impair bone morphogenetic protein /sons of mothers against decapentaplegic signaling with no evident adverse effect in vivo, even when administered chronically. They may offer a strategy for the treatment of diseases with high hepcidin levels