129 research outputs found

    Chronic exercise interventions for executive function in overweight children: a systematic review and meta-analysis

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    ObjectivesTo systematically evaluate the effectiveness of chronic exercise in physical activity (PA) as an intervention for executive functions (EFs) in children.MethodsWe conducted a systematic search in the following online databases: Web of Science, Cochrane Library, PubMed, Embase, and EBSCOhost. The timing is from database inception to July 2023, following PRISMA guidelines. Our inclusion criteria required studies reporting executive function (EF) levels in overweight children (age 0–18 years) before and after interventions. The Cochrane risk of bias tool assessed study bias, and Egger's test examined publication bias. Subgroup analyses considered three moderators: intervention duration, weekly frequency, and session length.ResultsThe meta-analysis included a total of 10 studies with 843 participants. It revealed a statistically significant yet relatively small overall positive effect (g = 0.3, 95% CI 0.16–0.44, P < 0.01) of chronic exercise on EF in overweight children. Importantly, there was no significant heterogeneity (Q = 11.64, df = 12, P = 0.48; I2 = 0).ConclusionsChronic exercise interventions had a consistent positive impact on EF, irrespective of intervention duration, weekly frequency, or session length. However, given limitations in the number and design of studies, further high-quality research is needed to strengthen these conclusions.Systematic Review RegistrationPROSPERO identifier (CRD42023468588)

    Effects of Ultra-high Pressure Assisted Enzymatic Hydrolysis on Structure and Antioxidant Activity of Hemp Protein Isolate

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    Hemp Protein Isolate (HPI) was used as raw material to modify HPI through ultrahigh pressure assisted enzymatic hydrolysis reaction. The SDS-PAGE electrophoresis characteristics, surface hydrophobicity, sulfhydryl content, FTIR and endogenous fluorescence of the hydrolysate of hemp protein isolate (HPIH) were determined under different pressures to investigate the structural changes of the HPI before and after modification. The results showed that ultra-high pressure (UHP) (0.1, 100, 200, 300 MPa) treatment had a certain auxiliary effect on HPI enzymolysis reaction, and with the increase of pressure, the degree of enzymolysis reaction increased gradually, and the molecular weight decreased gradually. After HPI modification, the hydrophobic groups were gradually exposed, and the surface hydrophobicity increased first and then decreased with the increase of pressure, the change difference was significant (P<0.05). The surface hydrophobicity reached the maximum at 200 MPa. After enzymolysis, the free sulfhydryl content of HPIH decreased significantly (P<0.05), while the surface sulfhydryl content increased first and then decreased with the increase of pressure. The determination of amino acid composition and content of protein before and after modification showed that the amino acid composition of HPI remained unchanged before and after modification, but the contents of various amino acids decreased to varying degrees. According to the fourier infrared spectroscopy, compared with HPI, the absorption peak intensity, peak shape and peak area of HPIH changed to different degrees, indicating that the secondary structure of protein was changed by the ultra-high pressure assisted enzymatic hydrolysis reaction. The endogenous fluorescence spectra showed that the fluorescence intensity of HPIH increased and the maximum emission wavelength was redshifted, indicating that the tertiary structure of HPI was changed by the enzymatic hydrolysis reaction. The results of antioxidant activity showed that appropriate pressure treatment could effectively improve the antioxidant capacity of enzymatic hydrolysis products. When the pressure was 200 MPa, the reducing power of HPIH of DPPH· and ABTS+· reached the highest. In conclusion, ultrahigh pressure assisted enzymatic hydrolysis modification can significantly change the secondary and tertiary structure of HPI, exposing hydrophobic groups and other active groups, thereby improving its antioxidant properties

    Cardiovascular Magnetic Resonance in Marfan syndrome

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    A comparative study of multiple instance learning methods for cancer detection using T-cell receptor sequences

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    As a branch of machine learning, multiple instance learning (MIL) learns from a collection of labeled bags, each containing a set of instances. The learning process is weakly supervised due to ambiguous instance labels. Since its emergence, MIL has been applied to solve various problems including content-based image retrieval, object tracking/detection, and computer-aided diagnosis. In biomedical research, the use of MIL has been focused on medical image analysis and molecule activity prediction. We review and apply 16 methods to investigate the applicability of MIL to a novel biomedical application, cancer detection using T-cell receptor (TCR) sequences. This important application can be a viable approach for large-scale cancer screening, as TCRs can be easily profiled from a subject’s peripheral blood. We consider two feasible data-generating mechanisms, and for the purpose of performance evaluation, we simulate data under each mechanism, where we vary potentially important factors to mimic realistic situations. We also apply the methods to sequencing data of ten cancer types from The Cancer Genome Atlas, as an early proof of concept for distinguishing tumor patients from healthy individuals via TCR sequencing of peripheral blood. We find that given an appropriate MIL method is used, satisfactory performance with Area Under the Receiver Operating Characteristic Curve above 80% can be achieved for five in the ten cancers. Based on our numerical results, we make suggestions about selection of a proper method and avoidance of any method with poor performance. We further point out directions of future research as well as identify a pressing need of new MIL methodologies for improved performance (for some cancer types) and more explainable outcomes

    DataSheet1_Simulating cardiac signals on 3D human models for photoplethysmography development.PDF

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    Introduction: Image-based heart rate estimation technology offers a contactless approach to healthcare monitoring that could improve the lives of millions of people. In order to comprehensively test or optimize image-based heart rate extraction methods, the dataset should contain a large number of factors such as body motion, lighting conditions, and physiological states. However, collecting high-quality datasets with complete parameters is a huge challenge.Methods: In this paper, we introduce a bionic human model based on a three-dimensional (3D) representation of the human body. By integrating synthetic cardiac signal and body involuntary motion into the 3D model, five well-known traditional and four deep learning iPPG (imaging photoplethysmography) extraction methods are used to test the rendered videos.Results: To compare with different situations in the real world, four common scenarios (stillness, expression/talking, light source changes, and physical activity) are created on each 3D human. The 3D human can be built with any appearance and different skin tones. A high degree of agreement is achieved between the signals extracted from videos with the synthetic human and videos with a real human-the performance advantages and disadvantages of the selected iPPG methods are consistent for both real and 3D humans.Discussion: This technology has the capability to generate synthetic humans within various scenarios, utilizing precisely controlled parameters and disturbances. Furthermore, it holds considerable potential for testing and optimizing image-based vital signs methods in challenging situations where real people with reliable ground truth measurements are difficult to obtain, such as in drone rescue.</p

    An rGQD/chitosan nanocomposite-based pH-sensitive probe: application to sensing in urease activity assays

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    Herein, we developed a pH sensing platform based on reduced graphene quantum dots (rGQDs) and chitosan (CS). CS was used as the recognition element in the self-assembly of the rGQD/CS nanocomposite because of its fascinating pH-sensitivity, induced by the protonation and deprotonation of the -NH2 group on its edge. The -NH2 was easily protonated under acidic conditions, making CS positively charged. The negatively-charged rGQDs under weak acidic conditions thus could couple with CS through electrostatic attraction, leading to fluorescence quenching. When the pH was changed to basic, the CS became negatively charged, resulting in the disassembly of the rGQD/CS system and causing the system to exhibit a turn-on fluorescence signal. The proposed pH-sensing nanocomposite was successfully applied for sensitive and reliable pH measurements from 5.0 to 9.0. This nanocomposite system was further utilized for the sensitive detection of pH changes caused by the enzymatic activity of urease, thereby proving its utility as a fluorescence turn-on sensor for urease in the field of biochemical and environmental analysis. The photoluminescence (PL) intensity of the rGQD/CS system increased as the pH increased. The increased intensity is directly related to the urease activity in the assay system. Thus, a novel fluorescence turn-on biosensor for urease based on the disassembly of the rGQD/CS composite is proposed. The system response exhibited a nearly linear relationship with urease concentration in the range of 0.05-0.75 U mL(-1). The detection limit for urease was 0.036 U mL(-1). This is the first report of this type of sensor for urease detection. When applied to real sample analysis, the present strategy exhibited satisfactory results
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