347 research outputs found

    Data-driven estimation of EMG muscular activity and fatigue through infrared thermal imaging

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    Purpose: Superficial electromyography (sEMG) is the recording, from the surface of the body, of the electrical signal associated to muscle activation. Usually, sEMG is assessed through electrodes with electrolytic gel often causing skin irritation. To overcome this issue, capacitive contactless electrodes have been developed. However, contactless EMG sensors are still quite sensitive to motion artifacts and could be not comfortable for long monitoring. In this study, a non-invasive contactless method to assess muscular activity through infrared thermal imaging (IRI) is presented. Methods: 10 healthy participants (age: 21.8 ± 2.9 years) were enrolled in the study. The participants underwent to 5 series of bodyweight squat exercise until exhaustion separated by 1 min of rest. The vastus medialis activity was assessed through EMG system Encephalan Mini AP-10. Concurrently, the temperature of the same muscle was measured through thermal camera FLIR A655. Regarding the EMG, the Average Rectified Value (ARV) and the median frequency of the Power Spectral Density (MDF) were evaluated for each series. Specifically, ARV is indicative of muscular activity and MDF of the muscular fatigue. Concerning the IRI, the average and the standard deviation of the temperature in a temporal window of 10 s after each series, and the thermal spatial gradient of the considered region were computed. Several Machine Learning regressors were tested employing the IRI features as input and, separately, the ARV and MDF as output. The data were normalized (z-score) and the leave-one-subject-out cross validation was used to test the generalization performance of the models. Results: Concerning the ARV, the Gaussian Process Regression delivered the best performance, with a correlation coefficient r = 0.75 (p\ 0.001) and root mean square error (RMSE) of 0.02 mV. Regarding the MDF, the Support Vector Machine with a radial basis function kernel allowed to obtain the best regression (r = 0.66, p \0.001; RMSE=0.67 Hz). Conclusion: The proposed method estimated the EMG parameters indicative of muscular activity and fatigue. These results indicate that the muscular activity influences skin temperature, suggesting a modification of the superficial blood circulation linked to the muscular need of oxygen during exercising. These results could pave the way to the employment of contactless methods to monitor the muscular activity and evaluate fatigue in a non-invasive and comfortable manner in sports and clinical applications

    MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients

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    : Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10-3). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients

    Betaine Treatment Prevents TNF-α-Mediated Muscle Atrophy by Restoring Total Protein Synthesis Rate and Morphology in Cultured Myotubes

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    Skeletal muscle atrophy is represented by a dramatic decrease in muscle mass, and it is related to a lower life expectancy. Among the different causes, chronic inflammation and cancer promote protein loss through the effect of inflammatory cytokines, leading to muscle shrinkage. Thus, the availability of safe methods to counteract inflammation-derived atrophy is of high interest. Betaine is a methyl derivate of glycine and it is an important methyl group donor in transmethylation. Recently, some studies found that betaine could promote muscle growth, and it is also involved in anti-inflammatory mechanisms. Our hypothesis was that betaine would be able to prevent tumor necrosis factor-alpha (TNF-alpha)-mediated muscle atrophy in vitro. We treated differentiated C2C12 myotubes for 72 hr with either TNF-alpha, betaine, or a combination of them. After the treatment, we analyzed total protein synthesis, gene expression, and myotube morphology. Betaine treatment blunted the decrease in muscle protein synthesis rate exerted by TNF-alpha, and upregulated Mhy1 gene expression in both control and myotube treated with TNF-alpha. In addition, morphological analysis revealed that myotubes treated with both betaine and TNF-alpha did not show morphological features of TNF-alpha-mediated atrophy. We demonstrated that in vitro betaine supplementation counteracts the muscle atrophy led by inflammatory cytokines

    DE-PASS Best Evidence Statement (BESt): Modifiable determinants of physical activity and sedentary behaviour in children and adolescents aged 5-19 years-a protocol for systematic review and meta-analysis

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    Introduction Physical activity among children and adolescents remains insufficient, despite the substantial efforts made by researchers and policymakers. Identifying and furthering our understanding of potential modifiable determinants of physical activity behaviour (PAB) and sedentary behaviour (SB) is crucial for the development of interventions that promote a shift from SB to PAB. The current protocol details the process through which a series of systematic literature reviews and meta-analyses (MAs) will be conducted to produce a best-evidence statement (BESt) and inform policymakers. The overall aim is to identify modifiable determinants that are associated with changes in PAB and SB in children and adolescents (aged 5-19 years) and to quantify their effect on, or association with, PAB/SB. Methods and analysis A search will be performed in MEDLINE, SportDiscus, Web of Science, PsychINFO and Cochrane Central Register of Controlled Trials. Randomised controlled trials (RCTs) and controlled trials (CTs) that investigate the effect of interventions on PAB/SB and longitudinal studies that investigate the associations between modifiable determinants and PAB/SB at multiple time points will be sought. Risk of bias assessments will be performed using adapted versions of Cochrane's RoB V.2.0 and ROBINS-I tools for RCTs and CTs, respectively, and an adapted version of the National Institute of Health's tool for longitudinal studies. Data will be synthesised narratively and, where possible, MAs will be performed using frequentist and Bayesian statistics. Modifiable determinants will be discussed considering the settings in which they were investigated and the PAB/SB measurement methods used. Ethics and dissemination No ethical approval is needed as no primary data will be collected. The findings will be disseminated in peer-reviewed publications and academic conferences where possible. The BESt will also be shared with policy makers within the DE-PASS consortium in the first instance. Systematic review registration CRD42021282874.
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