Association between IL-2 Receptor and Severe Coronary Artery Calcification in Patients with Coronary Artery Disease.

Background: Coronary artery calcification (CAC) is a crucial marker for coronary atherosclerosis, and the extent of CAC is closely linked to the incidence and progression of cardiovascular diseases. The interleukin-2 (IL-2) receptor (IL-2R), which plays a critical role in mediating the proliferation and differentiation of immune cells, may also be involved in the development of CAC. The study aimed to investigate the relationship between IL-2R and CAC, with the goal of providing new insights into cardiovascular diseases. Methods: In this study, we enrolled 606 patients diagnosed with coronary artery disease to assess CAC. Based on coronary artery calcification score (CACS), patients were divided into two groups: the non-severe CAC group (CACS ≤ 400 Agatston units, AU) and the severe CAC group (CACS > 400 AU). Results: The results showed that IL-2R levels were significantly higher in patients with severe CAC compared to those with non-severe CAC (383 vs. 352 pg/mL, p = 0.002). Moreover, the level of IL-2R was positively correlated with the severity of CAC, independent of other clinical risk factors. According to Receiver Operating Characteristic (ROC) curve, the IL-2R prediction model demonstrated a good capability in distinguishing severe CAC with the Area Under the Curve (AUC) value of 0.726. Conclusions: Our study suggests that IL-2R is independently associated with the occurrence of severe CAC in coronary artery disease (CAD) patients. Additionally, IL-2R may play a crucial role in the development of advanced atherosclerosis. Consequently, therapeutic strategies targeting the IL-2/IL-2R pathway may be effective in preventing or treating CAD.

Identification and Validation of Plasma Metabolomics Reveal Potential Biomarkers for Coronary Heart Disease.

Coronary heart disease (CHD) is a prevalent and chronic life-threatening disease. However, there is no reliable way for early diagnosis and prevention of CHD so far. The precise molecular pathological mechanism of CHD remains obscure. Therefore, developing novel biomarkers is urgently needed.In order to evaluate the potential of untargeted plasma metabolomics in biomarker discovery for characterizing CHD, plasma metabolites from patients newly diagnosed with CHD and controls were profiled using liquid chromatography quadrupole time-of-flight mass spectrometry. Differential metabolites were identified using both univariate and multivariate statistical analyses. Metabolites with significant changes were subjected to binary logistic regression analysis, and a CHD prediction model was established. A total of 28 differential plasma metabolites were identified, of which the concentrations of 11 increased significantly and those of 17 decreased significantly in patients with CHD compared with controls. The altered metabolic pathways included reduced phospholipid metabolism, increased monoglyceride metabolism, and abnormal fatty acid metabolism. Furthermore, binary logistic regression showed that nine metabolites could be used as potential plasma biomarkers for the diagnosis of CHD. The prediction model based on these nine metabolites was then tested with an independent cohort of samples (area under the curve = 0.929).Our plasma metabolomics study not only yielded fundamental insights into dysregulated metabolism in CHD but also presented a combinatorial biomarker that might support the clinical diagnosis of CHD.

Long non coding RNA-UCA1 contributes to cardiomyocyte apoptosis by suppression of p27 expression.

BACKGROUND/AIMS: Urothelial carcinoma-associated 1 (UCA1) is a recently identified long non coding RNA (lncRNA). However, few studies have explored its role in cardiomyocytes after focal cardiac ischemia reperfusion injury (CIR). METHODS: Rat CIR models were established using ligation of the Lower Anterior Descending artery (LAD). Cell apoptosis and reactive oxygen species (ROS) production in cardiac tissues were explored using immunohistochemistry and DHE staining. lncRNA expression patterns were detected using microarray and validated by qPCR. Cell viability and apoptosis were examined using MTT assay and flow cytometry. RESULTS: CIR significantly induced cell apoptosis and ROS production in the rat model. The results of microarray demonstrated the reduced expression of UCA1, which was validated by qPCR. Follow-up experiments showed that UCA1 was involved in H2O2-induced cell apoptosis. We further showed that UCA1 negatively correlated with the expression of p27. Moreover, overexpression of p27 could induce primary cardiomyocyte apoptosis. CONCLUSIONS: Reduction of UCA1 levels plays a pro-apoptotic role in primary cardiomyocytes partially through stimulation of p27 protein expression. These results are in agreement with the observed levels of UCA1, p27 and apoptosis after cardiac I/R injury, suggesting that UCA1 might have an important role during I/R injury.

[Macrophage content detection in an experimental rabbit model of atherosclerotic plaque by optical coherence tomography].

OBJECTIVE: To evaluate the feasibility of detecting macrophage content on atherosclerotic plaques by optical coherence tomography (OCT) technique. METHODS: Thirty New Zealand white rabbits were equally divided into 3 groups at random: Control group (fed normal rabbit chow, n = 10); lipid diet group (fed regular chow supplemented with cholesterol, n = 10) and balloon injury+ lipid diet group (balloon catheter injury of the common carotid artery after 2 weeks lipid diet, n = 10). After 12 weeks, all rabbits underwent pharmacological triggering with Chinese Russell's viper venom (CRVV, 15 mg/kg, i.p.) and histamine (0.02 mg/kg, i.v.). Common carotid arteries were detected with OCT and the Movat pentachrome stain respectively. OCT and histological examination results were compared and the correlation was analyzed. RESULTS: The intra thickness measured by Movat pentachrome stain and by the OCT was (15.2 ± 0.9) µm and (20.2 ± 7.6) µm, the medial thickness was (434.2 ± 86.5) µm and (453.8 ± 87.2) µm, the plaque thickness was (330.2 ± 87.1) µm and (392.2 ± 134.5) µm, the fibrous cap thickness was (58.3 ± 5.6) µm and (61.2 ± 4.9) µm, respectively (all P > 0.05). The normalized standard deviation of the OCT signal (NSD) was compared with immunohistochemical detection. The OCT signal within the cap is relatively homogeneous for low macrophage density in high lipid diet group. For the raw OCT data, a correlation of r = 0.846 (P < 0.01) was found between OCT NSD and a CD68 area<10%, whereas for the base 10 logarithm OCT data, a correlation of r = 0.646 (P < 0.05) was found between OCT NSD and a CD68 area<10%. In balloon injury + high lipid diet group, the OCT signal within the cap was relatively heterogeneous for high macrophage content. For the raw OCT data, a correlation of r = 0.906 (P < 0.01) was found between OCT NSD and a CD68 area >10%, whereas for the base 10 logarithm OCT data, a correlation of r = 0.593 (P < 0.05) was found between OCT NSD and a CD68 area >10%. For the raw OCT signal NSD, a range of NSDs (7.12% to 7.35%) demonstrated 100% sensitivity and specificity (Kappa value 1.0) for differentiating caps containing >10% CD68 staining. For the base 10 logarithm OCT signal, NSD values ranging from 7.81% to 7.92% provided 70% sensitivity and 75% specificity (value 0.44) for identifying caps containing >10% CD68 staining. CONCLUSIONS: OCT is an effective tool to determine macrophage content in this model. OCT imaging can clearly visualize different types of atherosclerotic plaques and provide detailed information on plaque characteristics.

Application of Deep Learning Technology in Strength Training of Football Players and Field Line Detection of Football Robots.

The purpose of the study is to improve the performance of intelligent football training. Based on deep learning (DL), the training of football players and detection of football robots are analyzed. First, the research status of the training of football players and football robots is introduced, and the basic structure of the neuron model and convolutional neural network (CNN) and the mainstream framework of DL are mainly expounded. Second, combined with the spatial stream network, a CNN-based action recognition system is constructed in the context of artificial intelligence (AI). Finally, by the football robot, a field line detection model based on a fully convolutional network (FCN) is proposed, and the effective applicability of the system is evaluated. The results demonstrate that the recognition effect of the dual-stream network is the best, reaching 92.8%. The recognition rate of the timestream network is lower than that of the dual-stream network, and the maximum recognition rate is 88%. The spatial stream network has the lowest recognition rate of 86.5%. The processing power of the four different algorithms on the dataset is stronger than that of the ordinary video set. The recognition rate of the time-segmented dual-stream fusion network is the highest, which is second only to the designed network. The recognition rate of the basic dual-stream network is 88.6%, and the recognition rate of the 3D CNN is the lowest, which is 86.2%. Under the intelligent training system, the recognition accuracy rates of jumping, kicking, grabbing, and starting actions range to 97.6, 94.5, 92.5, and 89.8% respectively, which are slightly lower than other actions. The recognition accuracy rate of passing action is 91.3%, and the maximum upgrade rate of intelligent training is 25.7%. The pixel accuracy of the improved field line detection of the model and the mean intersection over union (MIoU) are both improved by 5%. Intelligent training systems and the field line detection of football robots are more feasible. The research provides a reference for the development of AI in the field of sports training.

A Bibliometric and Visualization Analysis of Motor Learning in Preschoolers and Children over the Last 15 Years.

Motor learning enables preschoolers and children to acquire fundamental skills that are critical to their development. The current study sought to conduct a bibliometric and visualization analysis to provide a comprehensive overview of motor-learning progress in preschoolers and children over the previous 15 years. The number of studies is constantly growing, with the United States and Australia, as well as other productive institutions and authors, at the leading edge. The dominant disciplines were Neurosciences and Neurology, Psychology, Rehabilitation, and Sport Sciences. The journals Developmental Medicine & Child Neurology, Human Movement Science, Physical Therapy, Neuropsychology, Journal of Motor Behavior, and Journal of Experimental Child Psychology have been the most productive and influential in this regard. The most common co-citations for clinical symptoms were for cerebral palsy, developmental coordination disorder, and autism. Research has focused on language impairment (speech disorders, explicit learning, and instructor-control feedback), as well as effective intervention strategies. Advances in brain mechanisms and diagnostic indicators, as well as new intervention and rehabilitation technologies (virtual reality, transcranial magnetic stimulation, and transcranial direct current stimulation), have shifted research frontiers and progress. The cognitive process is critical in intervention, rehabilitation, and new technology implementation and should not be overlooked. Overall, our broad overview identifies three major areas: brain mechanism research, clinical practice (intervention and rehabilitation), and new technology application.

Modern Physical Education and Its Influence on Students' Entrepreneurial Psychology in Sports Universities.

This study aims to explore the entrepreneurial psychology of physical Education (PE) students under the "Internet+"environment, to cultivate and improve the entrepreneurial consciousness of PE students, taking the realization of students' sense of self-efficacy as an intermediary factor. The new educational technology in modern PE is analyzed first. Specifically, the motion sensing technology based on human-computer natural interaction can be used for training, so that learners can effectively improve their physical skills. Subsequently, the current entrepreneurial situation of PE majors is discussed, with 188 students from Tianjin University of Sport and Guangzhou Sport University selected as research subjects. It is found that 62.2% of students have never been exposed to online entrepreneurship, and they are more afraid of entrepreneurial risks. In terms of entrepreneurial motivation, most students choose to start a business because of "personal ideals," and only 40 people choose to start a business because of economic factors. There is a significant positive correlation between entrepreneurial self-efficacy and entrepreneurial intention of college students majoring in PE, and the correlation coefficient is 0.488. At present, the teaching mode of sports universities focuses on the teaching of professional courses. However, students generally believe that the professional knowledge learned is not useful for future entrepreneurship. The entrepreneurial self-efficacy of college students tends to be positive, and there are notable differences in the entrepreneurial self-efficacy between boys and girls. The regression analysis of entrepreneurial self-efficacy and entrepreneurial intention of college students shows that entrepreneurial self-efficacy can effectively predict entrepreneurial intention. This research promotes the innovation and development of the sports industry under the background of "Internet+".