Background: Sarcopenia is an age-related disease characterized by a progressive loss of systemic muscle mass and/or decreased muscle strength and physical function. The occurrence of sarcopenia in patients with chronic diseases will not only cause further deterioration of diseases and adverse clinical outcomes, but also lead to high medical cost, suggesting a necessity and a great significance to explore the associated factors of sarcopenia in chronic patients in order to improve their quality of life. This study aimed to investigate factors affecting sarcopenia among older hospitalized patients with chronic diseases.Methods: A total of 121 older patients with chronic diseases admitted to the Department of Geriatrics of Affiliated Kunshan Hospital of Jiangsu University from May 2019 to April 2021 were enrolled. According to the diagnostic criteria of sarcopenia formulated by the Asian Working Group for Sarcopenia (AWGS), the subjects were divided into a sarcopenia group (n=57) and a non-sarcopenia group (n=64). We analyzed the associated factors including bone mineral density, nutritional biomarkers, hormone levels and inflammatory cytokines.Results: Compared to the non-sarcopenia group, the sarcopenia group was of older average age (P<0.001), exhibited a lower body mass index (BMI) (P<0.001), a lower bone mineral density (BMD) of the femoral neck (P<0.01), and a higher incidence of osteoporosis. In terms of hematology, the sarcopenia group exhibited significantly lower serum iron and zinc levels (both P<0.05), a higher growth hormone (GH) level (P<0.05), a significantly lower IGF-1 level (P<0.01), and a lower level of iron (P<0.01). Poor nutritional status (assessed via measurement of albumin and prealbumin levels) positively correlated with sarcopenia (P<0.01).Conclusions: Sarcopenia is closely associated with aging, and has a close relationship with osteoporosis.Anemia, malnutrition, vitamin and trace element deficiencies, changes in hormone levels, and chronic inflammation are correlated with sarcopenia. Patients with these features above call for the screenig of sarcopenia. Additionally, these characteristics may help providing clues for further research on the pathogenesis and risk factors of sarcopenia, along with disease prevention and intervention.

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Video-based scoring using neural networks is a very important means for evaluating many sports, especially figure skating. Although many methods for evaluating action quality have been proposed, there is no uniform conclusion on the best feature extractor and clip length for the existing methods. Furthermore, during the feature aggregation stage, these methods cannot accurately locate the target information. To address these tasks, firstly, we systematically compare the effects of the figure skating model with three different feature extractors (C3D, I3D, R3D) and four different segment lengths (5, 8, 16, 32). Secondly, we propose a Multi-Scale Location Attention Module (MS-LAM) to capture the location information of athletes in different video frames. Finally, we present a novel Multi-scale Location Attentive Long Short-Term Memory (MLA-LSTM), which can efficiently learn local and global sequence information in each video. In addition, our proposed model has been validated on the Fis-V and MIT-Skate datasets. The experimental results show that I3D and 32 frames per second are the best feature extractor and clip length for video scoring tasks. In addition, our model outperforms the current state-of-the-art method hybrid dynAmic-statiC conText-aware attentION NETwork (ACTION-NET), especially on MIT-Skate (by 0.069 on Spearman’s rank correlation). In addition, it achieves average improvements of 0.059 on Fis-V compared with Multi-scale convolutional skip Self-attentive LSTM Module (MS-LSTM). It demonstrates the effectiveness of our models in learning to score figure skating videos.

Background: Sarcopenia has become a heavy disease burden among the elderly. Lipid metabolism was reported to be involved in many degenerative diseases. This study aims to investigate the association between dysregulated lipid metabolism and sarcopenia in geriatric inpatients. Methods: This retrospective cohort study included 303 patients aged ≥ 60, of which 151 were diagnosed with sarcopenia. The level of total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), homocysteine (HCY), BMI, and fat percentage, were compared between sarcopenia and non-sarcopenia patients. The Spearman correlation coefficient was used to estimate the association between sarcopenia and the level of lipid metabolism. To determine risk factors related to sarcopenia, a multivariate logistic regression analysis was carried out, and a risk prediction logistic model was derived based on significant risk factors in multivariate logistic analysis. Results: We observed a trend of rising sarcopenia prevalence with increasing age, decreasing BMI, and fat percentage (p < 0.001, Cochran Armitage test). Multivariate logistic regression analysis revealed sarcopenia’s risk factors, including older age, male sex, lower levels of BMI, TC, and TG, and higher levels of LDL and HCY (p < 0.05). The multivariate logistic regression model showed the risk prediction value of sarcopenia, with an area under the receiver operating curve (AUC) of 0.7466. Conclusions: Our study provided thorough insight into the risk factors associated with sarcopenia. It demonstrated that an increase of lipid metabolism-related parameters (BMI, TG, TC), within normal reference ranges, may be protective against sarcopenia. The present study can illuminate the direction and significance of lipid metabolism-related factors in preventing sarcopenia.