Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning

Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist

Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest

Sleep apnea (SA) is a common sleep disorder which could impair the human physiological system. Therefore, early diagnosis of SA is of great interest. The traditional method of diagnosing SA is an overnight polysomnography (PSG) evaluation. When PSG has limited availability, automatic SA screening with a fewer number of signals should be considered. The primary purpose of this study is to develop and evaluate a SA detection model based on electrocardiogram (ECG) and blood oxygen saturation (SpO2). We adopted a multimodal approach to fuse ECG and SpO2 signals at the feature level. Then, feature selection was conducted using the recursive feature elimination with cross-validation (RFECV) algorithm and random forest (RF) classifier used to discriminate between apnea and normal events. Experiments were conducted on the Apnea-ECG database. The introduced algorithm obtained an accuracy of 97.5%, a sensitivity of 95.9%, a specificity of 98.4% and an AUC of 0.992 in per-segment classification, and outperformed previous works. The results showed that ECG and SpO2 are complementary in detecting SA, and that the combination of ECG and SpO2 enhances the ability to diagnose SA. Therefore, the proposed method has the potential to be an alternative to conventional detection methods

Toe Box Shape of Running Shoes Affects In-Shoe Foot Displacement and Deformation: A Randomized Crossover Study

Background: Long-distance running is popular but associated with a high risk of injuries, particularly toe-related injuries. Limited research has focused on preventive measures, prompting exploration into the efficacy of raised toe box running shoes. Purpose: This study aimed to investigate the effect of running shoes with raised toe boxes on preventing toe injuries caused by distance running. Methods: A randomized crossover design involved 25 male marathon runners (height: 1.70 ± 0.02 m, weight: 62.6 + 4.5 kg) wearing both raised toe box (extended by 8 mm along the vertical axis and 3 mm along the sagittal axis) and regular toe box running shoes. Ground reaction force (GRF), in-shoe displacement, and degree of toe deformation (based on the distance change between the toe and the metatarsal head) were collected. Results: Wearing raised toe box shoes resulted in a significant reduction in vertical (p = 0.001) and antero–posterior (p = 0.015) ground reaction forces during the loading phase, with a notable increase in vertical ground reaction force during the toe-off phase (p p p p p = 0.002, lateral: p p p = 0.067). Conclusions: Raised toe box running shoes offer an effective means of reducing toe injuries caused by long-distance running

Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest

Sleep apnea (SA) is a common sleep disorder which could impair the human physiological system. Therefore, early diagnosis of SA is of great interest. The traditional method of diagnosing SA is an overnight polysomnography (PSG) evaluation. When PSG has limited availability, automatic SA screening with a fewer number of signals should be considered. The primary purpose of this study is to develop and evaluate a SA detection model based on electrocardiogram (ECG) and blood oxygen saturation (SpO2). We adopted a multimodal approach to fuse ECG and SpO2 signals at the feature level. Then, feature selection was conducted using the recursive feature elimination with cross-validation (RFECV) algorithm and random forest (RF) classifier used to discriminate between apnea and normal events. Experiments were conducted on the Apnea-ECG database. The introduced algorithm obtained an accuracy of 97.5%, a sensitivity of 95.9%, a specificity of 98.4% and an AUC of 0.992 in per-segment classification, and outperformed previous works. The results showed that ECG and SpO2 are complementary in detecting SA, and that the combination of ECG and SpO2 enhances the ability to diagnose SA. Therefore, the proposed method has the potential to be an alternative to conventional detection methods

Effects of Fatigue and Unanticipated Factors on Knee Joint Biomechanics in Female Basketball Players during Cutting

(1) This study examined the impact of fatigue and unanticipated factors on knee biomechanics during sidestep cutting and lateral shuffling in female basketball players, assessing the potential for non-contact anterior cruciate ligament (ACL) injuries. (2) Twenty-four female basketball players underwent fatigue induction and unanticipated change of direction tests, and kinematic and kinetic parameters were collected before and after fatigue with a Vicon motion capture system and Kistler ground reaction force (GRF) sensor. (3) Analysis using two-way repeated-measures ANOVA showed no significant interaction between fatigue and unanticipated factors on joint kinematics and kinetics. Unanticipated conditions significantly increased the knee joint flexion and extension angle (p p ≤ 0.05). One-dimensional statistical parametric mapping (SPM1d) results indicated significant differences in GRF during sidestep cutting and knee inversion and rotation moments during lateral shuffling post-fatigue. (4) Unanticipated factors had a greater impact on knee load patterns, raising ACL injury risk. Fatigue and unanticipated factors were independent risk factors and should be considered separately in training programs to prevent lower limb injuries

Enhancing proton exchange membrane water electrolysis by building electron/proton pathways

Proton exchange membrane water electrolysis (PEMWE) plays a critical role in practical hydrogen production. Except for the electrode activities, the widespread deployment of PEMWE is severely obstructed by the poor electron-proton permeability across the catalyst layer (CL) and the inefficient transport structure. In this work, the PEDOT:F (Poly(3,4-ethylenedioxythiophene):perfluorosulfonic acid) ionomers with mixed proton-electron conductor (MPEC) were fabricated, which allows for a homogeneous anodic CL structure and the construction of a highly efficient triple-phase interface. The PEDOT:F exhibits strong perfluorosulfonic acid (PFSA) side chain extensibility, enabling the formation of large hydrophilic ion clusters that form proton-electron transport channels within the CL networks, thus contributing to the surface reactant water adsorption. The PEMWE device employing membrane electrode assembly (MEA) prepared by PEDOT:F-2 demonstrates a competitive voltage of 1.713 ​V under a water-splitting current of 2 ​A ​cm−2 (1.746 ​V at 2A cm−2 for MEA prepared by Nafion D520), along with exceptional long-term stability. Meanwhile, the MEA prepared by PEDOT:F-2 also exhibits lower ohmic resistance, which is reduced by 23.4 ​% and 17.6 ​% at 0.1 ​A ​cm−2 and 1.5 ​A ​cm−2, respectively, as compared to the MEA prepared by D520. The augmentation can be ascribed to the superior proton and electron conductivity inherent in PEDOT:F, coupled with its remarkable structural stability. This characteristic enables expeditious mass transfer during electrolytic reactions, thereby enhancing the performance of PEMWE devices

Generation of ZZUi008-A, a transgene-free, induced pluripotent stem cell line derived from chorionic villi cells of a fetus with Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a common X-linked recessive disorder for which there is no present cure. In this paper, we reported the generation of ZZUi008-A, an induced pluripotent stem cell(iPSC) line derived from chorionic villus(CV) cells of a fetus with a deletion mutation in exon 33 of the dystrophin gene (DMD). The cell line was generated using feeder-free and virus-free conditions, and the established cell line retains the original DMD mutation, a normal karyotype, expresses pluripotency markers, able to differentiate into three lineages. This ZZUi008-A cell line could provide a promising tool to study this complex disease

Structurally Tunable Graphitized Mesoporous Carbon for Enhancing the Accessibility and Durability of Cathode Pt‐Based Catalysts for Proton Exchange Membrane Fuel Cells

Low Pt utilization and intense carbon corrosion of cathode catalysts is a crucial issue for high‐efficiency proton exchange membrane fuel cells due to the highly demanded long‐term durability and less acquisition/application cost. Herein, structurally tunable graphitized mesoporous carbon (GMC) is obtained by direct high‐temperature pyrolysis and in situ‐controlled mesopore formation; the structure‐optimized GMC1300‐1800 exhibits a mesopore size of 7.54 nm and enhanced corrosion resistance. Functionalized GMC1300‐1800 is loaded with small‐sized Pt nanoparticles (NPs) (1.5 nm) uniformly by impregnation method to obtain Pt/GMC1300‐1800 and form an “internal platinum structure” to avoid sulfonic acid groups poisoning as well as ensure O2/proton accessibility. Hence, the electrochemically active surface area (ECSA) of Pt/GMC1300‐1800 reaches 106.1 m2 g−1Pt, while mass activity and specific activity at 0.9 V are 2.1 and 1.4 times those of commercial Pt/C, respectively. Notably, the ECSA decay is less than 17% for both 30 000 cycles’ accelerated durability tests (ADTs) of Pt attenuation and carbon attenuation. Accordingly, the optimized mesoporous structure of GMC1300‐1800 significantly decreases the coverage of sulfonic acid groups on Pt NPs, leading to the highest peak power density in the single‐cell test. Density functional theory calculations demonstrate the synergistic effect between graphitization and mesoporosity on enhancing the accessibility and durability of the catalysts