Prognostic value of metabolic syndrome in patients with heart failure and malnutrition.

BACKGROUND: Malnutrition is severely associated with worst prognosis of patients with heart failure (HF). Malnourished patients with the metabolic syndrome (MS) can result in a double burden of malnutrition. We aimed to investigate the impact of the MS on clinical outcomes in malnourished HF patients. METHODS: We examined 529 HF patients at risk of malnutrition with a mean age of (66 ± 10) years and 78% (415) were male. Nutritional status defined primarily by the prognostic nutritional index (PNI), with PNI < 40 being defined as malnutrition. The follow-up endpoint was cardiovascular death or all-cause death. RESULTS: During the 36-month follow-up, survival rates for cardiovascular and all-cause death were significantly lower in the MS group than in the non-MS group (log-rank P < 0.01). Multivariate Cox proportional hazards regression models showed that MS was independently associated with cardiovascular death (HR:1.759, 95%CI:1.351-2.291, p < 0.001) and all-cause death (HR:1.326, 95%CI:1.041-1.689, p = 0.022) in malnourished patients with HF. MS significantly increased the predictive value of cardiovascular death (AUC:0.669, 95%CI:0.623-0.715, p < 0.001) and all-cause death (AUC:0.636, 95%CI:0.585-0.687, p < 0.001) on the basis of established risk factors. The predictive effect of MS on cardiovascular death was independent of sex, age, functional class and left ventricular ejection fraction. CONCLUSIONS: In malnourished patients with HF, MS is an independent risk factor for cardiovascular and all-cause mortality. MS significantly enhance the predictive value for clinical events in patients.

Analysis of Clinical Outcome and Prognosis of C-reactive Protein Combined with Albumin in Patients with Acute Myocardial Infarction.

Objective: This study aims to assess the combined predictive value of C-reactive protein (CRP) and albumin (ALB) for major adverse cardiovascular events (MACE) post-percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI). Methods: We analyzed data from continuously enrolled AMI patients who underwent emergency PCI at the First Affiliated Hospital of Xinjiang Medical University over six years, employing logistic regression to derive a predictive equation for in-hospital mortality and out-of-hospital MACE events. Primary endpoints: In-hospital death and out-of-hospital major adverse cardiovascular events. The patients were followed up for 1, 3, 6, and 12 months after discharge. The average follow-up time was 41 months. Results: Among the 601 patients studied, we observed 16 in-hospital deaths and 131 out-of-hospital MACE events. Multivariate logistic regression analysis showed that the independent predictors of out-of-hospital MACE events were age (OR=1.067, 95% CI 1.013-1.124, P = .028), C-reactive protein (OR=1.012, 95% CI 1.000-1.025, P = .045) and albumin (OR=0.874, 95% CI 0.785-0.973, P = .014). Our multivariate logistic regression analysis identified age, CRP, and albumin as independent predictors, with the combined equation yielding an ROC curve area of 0.85, effectively stratifying patients into high-risk and low-risk groups. Subsequent follow-up results validated this risk stratification approach. Conclusion: The study underscores the efficacy of combining CRP and albumin levels as a predictive measure for in-hospital death and out-of-hospital MACE events in AMI patients post-PCI.

A Distortion Correction Method Based on Actual Camera Imaging Principles.

In the human-robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.

A Hierarchical Motion Planning Method for Mobile Manipulator.

This paper focuses on motion planning for mobile manipulators, which includes planning for both the mobile base and the manipulator. A hierarchical motion planner is proposed that allows the manipulator to change its configuration autonomously in real time as needed. The planner has two levels: global planning for the mobile base in two dimensions and local planning for both the mobile base and the manipulator in three dimensions. The planner first generates a path for the mobile base using an optimized A* algorithm. As the mobile base moves along the path with the manipulator configuration unchanged, potential collisions between the manipulator and the environment are checked using the environment data obtained from the on-board sensors. If the current manipulator configuration is in a potential collision, a new manipulator configuration is searched. A sampling-based heuristic algorithm is used to effectively find a collision-free configuration for the manipulator. The experimental results in simulation environments proved that our heuristic sampling-based algorithm outperforms the conservative random sampling-based method in terms of computation time, percentage of successful attempts, and the quality of the generated configuration. Compared with traditional methods, our motion planning method could deal with 3D obstacles, avoid large memory requirements, and does not require a long time to generate a global plan.

Joint Calibration Method for Robot Measurement Systems.

Robot measurement systems with a binocular planar structured light camera (3D camera) installed on a robot end-effector are often used to measure workpieces' shapes and positions. However, the measurement accuracy is jointly influenced by the robot kinematics, camera-to-robot installation, and 3D camera measurement errors. Incomplete calibration of these errors can result in inaccurate measurements. This paper proposes a joint calibration method considering these three error types to achieve overall calibration. In this method, error models of the robot kinematics and camera-to-robot installation are formulated using Lie algebra. Then, a pillow error model is proposed for the 3D camera based on its error distribution and measurement principle. These error models are combined to construct a joint model based on homogeneous transformation. Finally, the calibration problem is transformed into a stepwise optimization problem that minimizes the sum of the relative position error between the calibrator and robot, and analytical solutions for the calibration parameters are derived. Simulation and experiment results demonstrate that the joint calibration method effectively improves the measurement accuracy, reducing the mean positioning error from over 2.5228 mm to 0.2629 mm and the mean distance error from over 0.1488 mm to 0.1232 mm.

Long noncoding RNA MALAT1 polymorphism predicts MACCEs in patients with myocardial infarction.

BACKGROUND: Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) participates in the occurrence and development of cardiovascular and cerebrovascular diseases such as stroke and coronary heart disease by regulating inflammatory reactions, programmed cell death, and other pathological processes. Previous studies revealed that the MALAT1 gene polymorphism was associated with cardiac and cerebrovascular diseases. However, the prognostic role of the MALAT1 polymorphism in major adverse cardiac and cerebrovascular events (MACCEs) remains unknown. Therefore, this study intends to explore the association between the MALAT1 rs3200401 polymorphism and MACCEs. METHOD: We enrolled 617 myocardial infarction (MI) patients and 1125 control participants who attended the First Affiliated Hospital of Xinjiang Medical University from January 2010 to 2018. SNPscan™ typing assays were used to detect the MALAT1 rs3200401 genotype. During the follow-up, MACCEs were recorded. Kaplan-Meier curves and univariate and multivariate Cox survival analyses were used to explore the correlation between MALAT1 gene polymorphisms and the occurrence of MACCEs. RESULTS: Among the total participants and MI patients, the frequencies of the T allele (total Participants 19.5% vs. 15.3%, P = 0.047, MI patients 20.7% vs. 14.1%, P = 0.014) and CT + TT genotypes (total Participants 37.4% vs. 28.1%, P = 0.013, MI patients 39.5% vs. 25.8%, P = 0.003) were significantly higher in subjects with MACCEs than in subjects without MACCEs. However, in control participants, the frequencies of the T allele (16.6% vs. 16.0%, P = 0.860) and CT + TT genotypes (31.4% vs. 29.3%, P = 0.760) were not higher in subjects with MACCEs than in subjects without MACCEs. In addition, among the total participants and MI patients, the Kaplan-Meier curve analysis indicated that the subjects with rs3200401 CT + TT genotypes had a higher incidence of MACCEs than CC genotype carriers (P = 0.015, P = 0.001). Nevertheless, similar results were not observed in the control participants (P = 0.790). Multivariate Cox regression indicated that compared with patients with the CC genotype, patients with CT + TT genotypes had a 1.554-fold increase in MACCE risk (hazard ratio: 1.554, 95% confidence interval: 1.060-2.277, P = 0.024). CONCLUSIONS: The MALAT1 rs3200401 CT + TT genotypes could be a risk factor for MACCEs in MI patients, suggesting that the MALAT1 gene may become a biomarker for poor prognosis in MI patients.

Physician-Friendly Tool Center Point Calibration Method for Robot-Assisted Puncture Surgery.

After each robot end tool replacement, tool center point (TCP) calibration must be performed to achieve precise control of the end tool. This process is also essential for robot-assisted puncture surgery. The purpose of this article is to solve the problems of poor accuracy stability and strong operational dependence in traditional TCP calibration methods and to propose a TCP calibration method that is more suitable for a physician. This paper designs a special binocular vision system and proposes a vision-based TCP calibration algorithm that simultaneously identifies tool center point position (TCPP) and tool center point frame (TCPF). An accuracy test experiment proves that the designed special binocular system has a positioning accuracy of ±0.05 mm. Experimental research shows that the magnitude of the robot configuration set is a key factor affecting the accuracy of TCPP. Accuracy of TCPF is not sensitive to the robot configuration set. Comparison experiments show that the proposed TCP calibration method reduces the time consumption by 82%, improves the accuracy of TCPP by 65% and improves the accuracy of TCPF by 52% compared to the traditional method. Therefore, the method proposed in this article has higher accuracy, better stability, less time consumption and less dependence on the operations than traditional methods, which has a positive effect on the clinical application of high-precision robot-assisted puncture surgery.

A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations.

In manufacturing, traditional task pre-programming methods limit the efficiency of human-robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we establish a markerless vision capture system to acquire continuous human hand movements and develop a threshold-based heuristic segmentation algorithm to segment the complete movements into different movement primitives (MPs) which encode human hand movements with task-oriented models. For movement primitive learning, we adopt a Gaussian mixture model and Gaussian mixture regression (GMM-GMR) to extract the optimal trajectory encapsulating sufficient human features and utilize dynamical movement primitives (DMPs) to learn for trajectory generalization. In addition, we propose an improved visuo-spatial skill learning (VSL) algorithm to learn goal configurations concerning spatial relationships between task-relevant objects. Only one multioperation demonstration is required for learning, and robots can generalize goal configurations under new task situations following the task execution order from demonstration. A series of peg-in-hole experiments demonstrate that the proposed task-learning strategy can obtain exact pick-and-place points and generate smooth human-like trajectories, verifying the effectiveness of the proposed strategy.

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review.

As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object's features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

A Force-Sensing System on Legs for Biomimetic Hexapod Robots Interacting with Unstructured Terrain.

The tiger beetle can maintain its stability by controlling the interaction force between its legs and an unstructured terrain while it runs. The biomimetic hexapod robot mimics a tiger beetle, and a comprehensive force sensing system combined with certain algorithms can provide force information that can help the robot understand the unstructured terrain that it interacts with. This study introduces a complicated leg force sensing system for a hexapod robot that is the same for all six legs. First, the layout and configuration of sensing system are designed according to the structure and sizes of legs. Second, the joint toque sensors, 3-DOF foot-end force sensor and force information processing module are designed, and the force sensor performance parameters are tested by simulations and experiments. Moreover, a force sensing system is implemented within the robot control architecture. Finally, the experimental evaluation of the leg force sensor system on the hexapod robot is discussed and the performance of the leg force sensor system is verified.

Value of Contrast-enhanced Ultrasound for Small Focal Liver Lesions.

Objective To explore the diagnostic value of contrast-enhanced ultrasound (CEUS)for small focal liver lesions (FLLs). Methods The clinical data,CEUS findings,and pathology of 69 patients with small FLLs were retrospectively analyzed. Patients were grouped according to size of FLLs and the performance of CEUS was compared. Results The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value of CEUS for the 69 FLLs were 94.2%,95.2%,93.8%,87.0%,and 97.8%,respectively. Rate of fast wash-out in portal vein phase was lower in group of diameters ≤2 cm than that in group of diameters>2 cm (P<0.05). Conclusions CEUS has a high diagnostic value for small FLLs.However,the CEUS findings of malignant lesions smaller than 2 cm are not typical in the portal phase and therefore the diagnosis should also be based on clinical features.

Bidirectional visual-tactile cross-modal generation using latent feature space flow model.

Inspired by visual-tactile cross-modal bidirectional mapping of the human brain, this paper introduces a novel approach to bidirectional mapping between visual and tactile data, an area not fully explored in the predominantly unidirectional existing studies. First, we adopt separate Variational AutoEncoder (VAE) models for visual and tactile data. Furthermore, we introduce a conditional flow model built on the VAE latent feature space, enabling cross-modal bidirectional mapping between visual and tactile data using one model. The experimental results show that our method achieves excellent performance in terms of the similarity between the generated data and the original data (Structural Similarity Index (SSIM) of visual data: 0.58, SSIM of tactile data: 0.80), the classification accuracy on generated data (visual data: 91.60%, tactile data: 88.05%), and the zero-shot classification accuracy between generated data and language (visual data: 44.49%, tactile data: 45.03%). To the best of our knowledge, the method proposed in this paper is the first one to utilize a single model to achieve bidirectional mapping between visual and tactile data. Our model and code will be made public after the acceptance of the paper.

Predictive Effect of Alternative Insulin Resistance Indexes on Adverse Cardiovascular Events in Patients with Metabolic Syndrome with Heart Failure.

Purpose: Metabolic Syndrome (MS) greatly increases the risk of heart disease and Heart Failure(HF). Insulin Resistance (IR) is considered to be the key to the pathophysiology of MS. The purpose of this study was to evaluate the predictive effect of different alternative indicators of IR on adverse cardiovascular events in patients with MS complicated with HF. Methods: Patients with HF who were diagnosed with MS in the heart center of the first affiliated Hospital of Xinjiang Medical University were selected continuously. The baseline data of the patients in the group were compared. The diagnostic value of alternative indexes of IR was evaluated by the working characteristic curve of subjects. The relationship between different alternative indicators of IR and survival rate was evaluated by survival curve. COX regression was used to analyze the effects of different alternative indicators of IR on the risk of end-point events. Results: The levels of TyG, TyG-BMI, TyG-WC, TG/HDL-C and METS-IR were significantly increased in patients with Major Adverse Cardiovascular Events (MACEs). Among the five alternative indexes of IR, METS-IR had the highest AUC (0.691, 95% CI:0.657-0.752, P < 0.001) in predicting MACEs. No matter which alternative index of IR was used, the survival rate of MACEs in High group was significantly decreased. TyG, TyG-BMI, TyG-WC, TG/HDL-C and METS-IR can independently predict the occurrence of MACEs events, even if some confounding factors are adjusted. Conclusion: Our study shows that alternative indicators of IR, especially METS-IR, are independently associated with adverse cardiovascular events in patients with MS and HF.

A self-adaptive parameter optimization algorithm in a real-time parallel image processing system.

Aiming at the stalemate that precision, speed, robustness, and other parameters constrain each other in the parallel processed vision servo system, this paper proposed an adaptive load capacity balance strategy on the servo parameters optimization algorithm (ALBPO) to improve the computing precision and to achieve high detection ratio while not reducing the servo circle. We use load capacity functions (LC) to estimate the load for each processor and then make continuous self-adaptation towards a balanced status based on the fluctuated LC results; meanwhile, we pick up a proper set of target detection and location parameters according to the results of LC. Compared with current load balance algorithm, the algorithm proposed in this paper is proceeded under an unknown informed status about the maximum load and the current load of the processors, which means it has great extensibility. Simulation results showed that the ALBPO algorithm has great merits on load balance performance, realizing the optimization of QoS for each processor, fulfilling the balance requirements of servo circle, precision, and robustness of the parallel processed vision servo system.

Prognostic Nutritional Index (PNI) as a Predictor in Patients with Metabolic Syndrome and Heart Failure.

Purpose: There is a lack of research on nutritional status and poor prognosis in patients with metabolic syndrome and heart failure. This study evaluated the relationship between nutritional status as defined by the PNI and adverse outcomes in patients with metabolic syndrome and heart failure. Methods: A total of 1048 heart failure patients with metabolic syndrome admitted to the Heart Center of the First Affiliated Hospital of Xinjiang Medical University from January 2015 to December 2019 were consecutively. PNI was used to assess their nutritional status. Results: A total of 51.0% of the patients were in the nonmalnutrition group (PNI≥45), 27.9% were in the mild malnutrition group (40≤PNI<45), and 21.1% of patients were in the malnutrition group (PNI<40). At 36 months of follow-up, after adjusting for other confounding factors, malnutrition (PNI<40) was independently associated with all-cause death (HR: 1.787, 95% CI: 1.451-2.201, P<0.001) and cardiovascular death (HR: 1.837, 95% CI: 1.467-2.301, P<0.001). PNI showed additional prognostic predictive value when included in the established risk factor model, both for all-cause death (AUC: 0.620, 95% CI: 0.579-0.661, P<0.001) and cardiovascular death (AUC: 0.596, 95% CI: 0.555-0.636, P<0.001). Conclusion: In patients with metabolic syndrome and heart failure, malnutrition assessed by PNI is an independent predictor for all-cause death and cardiovascular death, and PNI is negatively correlated with the occurrence of adverse outcomes.

Associations between urinary iodine concentration and the prevalence of metabolic disorders: a cross-sectional study.

Background: Few studies have examined the role of iodine in extrathyroidal function. Recent research has shown an association between iodine and metabolic syndromes (MetS) in Chinese and Korean populations, but the link in the American participants remains unknown. Purpose: This study aimed to examine the relationship between iodine status and metabolic disorders, including components associated with metabolic syndrome, hypertension, hyperglycemia, central obesity, triglyceride abnormalities, and low HDL. Methods: The study included 11,545 adults aged ≥ 18 years from the US National Health and Nutrition Examination Survey (2007-2018). Participants were divided into four groups based on their iodine nutritional status(ug/L), as recommended by the World Health Organization: low UIC, < 100; normal UIC, 100-299; high UIC, 300-399; and very high, ≥ 400. The Odds ratio (OR) for MetS basing the UIC group was estimated using logistic regression models for our overall population and subgroups. Results: Iodine status was positively associated with the prevalence of MetS in US adults. The risk of MetS was significantly higher in those with high UIC than in those with normal UIC [OR: 1.25; 95% confidence intervals (CI),1.016-1.539; p = 0.035). The risk of MetS was lower in the low UIC group (OR,0.82; 95% CI: 0.708-0.946; p = 0.007). There was a significant nonlinear trend between UIC and the risk of MetS, diabetes, and obesity in overall participants. Participants with high UIC had significantly increased TG elevation (OR, 1.24; 95% CI: 1.002-1.533; P = 0.048) and participants with very high UIC had significantly decreased risk of diabetes (OR, 0.83; 95% CI: 0.731-0.945, p = 0.005). Moreover, subgroup analysis revealed an interaction between UIC and MetS in participants aged < 60 years and ≥ 60 years, and no association between UIC and MetS in older participants aged ≥ 60 years. Conclusion: Our study validated the relationship between UIC and MetS and their components in US adults. This association may provide further dietary control strategies for the management of patients with metabolic disorders.

Liraglutide alleviates myocardial ischemia‒reperfusion injury in diabetic mice.

Diabetic patients are prone to acute myocardial infarction. Although reperfusion therapy can preserve the viability of the myocardium, it also causes fatal ischemia‒reperfusion injury. Diabetes can exacerbate myocardial ischemia‒reperfusion injury, but the mechanism is unclear. We aimed to characterize the effects of liraglutide on the prevention of ischemia‒reperfusion injury and inadequate autophagy. Liraglutide reduced the myocardial infarction area and improved cardiac function in diabetic mice. We further demonstrated that liraglutide mediated these protective effects by activating AMPK/mTOR-mediated autophagy. Liraglutide markedly increased p-AMPK levels and the LC3 II/LC3 I ratio and reduced p-mTOR levels and p62 expression. Pharmacological inhibition of mTOR increased cell viability and autophagy levels in high glucose and H/R-treated H9C2 cells. Overall, our study reveals that liraglutide acts upstream of the AMPK/mTOR pathway to effectively counteract high glucose- and H/R-induced cell dysfunction by activating AMPK/mTOR-dependent autophagy, providing a basis for the clinical prevention and treatment of ischemia‒reperfusion in diabetes.

Metabolic Score for Insulin Resistance (METS-IR) Predicts Adverse Cardiovascular Events in Patients with Type 2 Diabetes and Ischemic Cardiomyopathy.

Purpose: This study aimed to evaluate the association between metabolic score for insulin resistance (METS-IR) and adverse cardiovascular events in patients with ischemic cardiomyopathy (ICM) and type 2 diabetes mellitus (T2DM). Methods: METS-IR was calculated using the following formula: ln[(2 × fasting plasma glucose (mg/dL) + fasting triglyceride (mg/dL)] × body mass index (kg/m2)/(ln[high-density lipoprotein cholesterol (mg/dL)]). Major adverse cardiovascular events (MACEs) were defined as the composite outcome of nonfatal myocardial infarction, cardiac death, and rehospitalization for heart failure. Cox proportional hazards regression analysis was used to evaluate the association between METS-IR and adverse outcomes. The predictive value of METS-IR was evaluated by the area under the curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: The incidence of MACEs increased with METS-IR tertiles at a 3­year follow­up. Kaplan‒Meier curves showed a significant difference in event-free survival probability between METS-IR tertiles (P<0.05). Multivariate Cox hazard regression analysis adjusting for multiple confounding factors showed that when comparing the highest and lowest METS-IR tertiles, the hazard ratio was 1.886 (95% CI:1.613-2.204; P<0.001). Adding METS-IR to the established risk model had an incremental effect on the predicted value of MACEs (AUC=0.637, 95% CI:0.605-0.670, P<0.001; NRI=0.191, P<0.001; IDI=0.028, P<0.001). Conclusion: METS-IR, a simple score of insulin resistance, predicts the occurrence of MACEs in patients with ICM and T2DM, independent of known cardiovascular risk factors. These results suggest that METS-IR may be a useful marker for risk stratification and prognosis in patients with ICM and T2DM.

Long-term prognostic value of macrophage migration inhibitory factor in ST-segment elevation myocardial infarction patients with metabolic syndrome after percutaneous coronary intervention.

Metabolic syndrome (MetS) is a major risk factor for cardiovascular disease and negatively affecting the prognosis of patients with ST elevation myocardial infarction (STEMI). Macrophage migration inhibitory factor (MIF) is a multipotent cytokine involved in various cardiovascular and inflammatory diseases. In this prospective study, we investigate the value of MIF in the long-term prognosis of STEMI combined with MetS after emergency PCI. Circulating MIF levels were measured at admission, and major adverse cardiovascular and cerebrovascular events (MACCE) were monitored during the follow-up period of 4.9 (3.9-5.8) years. MACCE occurred in 92 patients (22.9%), which was significantly higher in MetS (69/255, 27.1%) than in the non-MS subgroup (23/146, 15.8%, P < 0.05). Patients with MetS developed MACCE had the highest admission MIF level. Kaplan-Meier survival analysis using the cutoff value of admission MIF (143 ng/ml) showed that patients with a higher MIF level had a greater incidence of MACCE than those with lower MIF levels in both the MetS (P < 0.0001) and non-MetS groups (P = 0.016). After adjustment for clinical variables, the value of MIF ≥ 143 ng/ml still had the predictive power for the MetS group [HR 9.56, 95% CI (5.397-16.944),P < 0.001]; nevertheless, it was not the case in the non-MetS group. Our findings indicated that MetS is a critical risk factor for adverse clinical outcomes in patients with STEMI, and a high admission MIF level has predictive power for the long-term MACCE, which is superior in STEMI patients with MetS and better than other traditional predictors.

Analysis and control of biped robot with variable stiffness ankle joints.

BACKGROUND: Biped robot locomotion is an active topic of research, and the walking stability is one of the research objectives. OBJECTIVE: This paper discusses the variable stiffness ankle joint and the walking control of a biped robot. METHODS: A design is introduced that achieves the ankle joint variable stiffness by using a pneumatic unit. The actuation system of the biped robot is based on the hybrid use of electric and pneumatic. The locomotion control architecture has been proposed to exploit natural leg dynamics in order to improve the biped robot walking stability. We also present a dynamic simulation which matches the biped robot and experiments with the real biped robot. RESULTS: The simulation and experiments result that introducing the variable stiffness ankle joint and the controller achieve a significant improvement in foot-ground impact and walking stability of the biped robot. CONCLUSION: The biped robot with variable stiffness ankle joints has a better walking performance under the control method.