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Summary: Addressing the joint control problem of pneumatic muscle-driven robots, this study aims to design a bionic reflex mechanism to enhance the robots’ adaptive capacity to various disturbances. Based on the biological reflex mechanism, we developed a spindle reflex and deep tendon reflex control system based on CPG (central pattern generator) to mitigate the sudden impact on the hip joint and the continuous blocking force on the knee joint, respectively. The spindle reflex controller incorporates the fast response of sliding mode control to effectively minimize the trajectory deviation of the hip joint under impact disturbances. The deep tendon reflex controller integrates RBF neural network adaptive control and the Tegotae framework to suppress excessive tension in the knee joint and augment the system’s adaptability to the blocking force disturbances. The experimental results confirm that the two reflex mechanisms significantly enhance the robustness and flexibility of the pneumatic muscle-driven robot in motion.

Background: Severe acute kidney injury (AKI) after total aortic arch replacement (TAAR) is related to adverse outcomes in patients with acute type A aortic dissection (ATAAD). However, the early prediction of severe AKI remains a challenge. This study aimed to develop a novel model to predict severe AKI after TAAR in ATAAD patients using machine learning algorithms. Methods: A total of 572 ATAAD patients undergoing TAAR were enrolled in this retrospective study, and randomly divided into a training set (70 %) and a validation set (30 %). Lasso regression, support vector machine-recursive feature elimination and random forest algorithms were used to screen indicators for severe AKI (defined as AKI stage III) in the training set, respectively. Then the intersection indicators were selected to construct models through artificial neural network (ANN) and logistic regression. The AUC-ROC curve was employed to ascertain the prediction efficacy of the ANN and logistic regression models. Results: The incidence of severe AKI after TAAR was 22.9 % among ATAAD patients. The intersection predictors identified by different machine learning algorithms were baseline serum creatinine and ICU admission variables, including serum cystatin C, procalcitonin, aspartate transaminase, platelet, lactic dehydrogenase, urine N-acetyl-β-d-glucosidase and Acute Physiology and Chronic Health Evaluation II score. The ANN model showed a higher AUC-ROC than logistic regression (0.938 vs 0.908, p

BackgroundThe occurrence of acute kidney injury (AKI) following cardiac surgery is common and linked to unfavorable consequences while identifying it in its early stages remains a challenge. The aim of this research was to examine whether the fibrinogen-to-albumin ratio (FAR), an innovative inflammation-related risk indicator, has the ability to predict the development of AKI in individuals after cardiac surgery.MethodsPatients who underwent cardiac surgery from February 2023 to March 2023 and were admitted to the Cardiac Surgery Intensive Care Unit of a tertiary teaching hospital were included in this prospective observational study. AKI was defined according to the KDIGO criteria. To assess the diagnostic value of the FAR in predicting AKI, calculations were performed for the area under the receiver operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsOf the 260 enrolled patients, 85 developed AKI with an incidence of 32.7%. Based on the multivariate logistic analyses, FAR at admission [odds ratio (OR), 1.197; 95% confidence interval (CI), 1.064–1.347, p = 0.003] was an independent risk factor for AKI. The receiver operating characteristic (ROC) curve indicated that FAR on admission was a significant predictor of AKI [AUC, 0.685, 95% CI: 0.616–0.754]. Although the AUC-ROC of the prediction model was not substantially improved by adding FAR, continuous NRI and IDI were significantly improved.ConclusionsFAR is independently associated with the occurrence of AKI after cardiac surgery and can significantly improve AKI prediction over the clinical prediction model.

ABSTRACTBackground Calcium plays a key role in many bio-homeostasis functions. Previous studies indicated that serum calcium is associated with diseases such as anaemia. However, the evidence on the association between serum calcium levels and anaemia risk is limited. Thus, the purpose of our study is to investigate the relationship between serum calcium and anaemia in US adults.Methods This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) including 15,519 participants, aged ≥18 years. The analyses were performed using multivariate logistic regression and a generalized additive model (GAM). Subgroup analysis, stratified by gender and age, was also performed.Results Among the sample of 15,519 individuals, 1565 (10.8%) had been diagnosed with anaemia. Both the univariate logistic regression model and multivariate logistic regression model showed a reverse relationship between serum calcium and anaemia risk. Moreover, a non-linear association between serum calcium and anaemia risk was observed using GAM and smooth curve fitting. The inflection point of serum calcium was at 2.3 mmol/L. GAM with penalized splines suggested a reverse association between serum calcium and the prevalence of anaemia when the concentration of serum calcium was below 2.3 mmol/L. In contrast, we found no statistically significant difference when serum calcium concentration was higher than the inflection point (2.3 mmol/L).Conclusion Lower serum calcium levels were associated with increased risk of anaemia risk. Moreover, we observed non-linear associations between serum calcium and anaemia risk. Our results need to be confirmed in future prospective studies.

ObjectivePelvic floor dysfunction (PFD) is highly prevalent among women. Pelvic floor ultrasound (PFU) is a critical method for assessing PFD. This study examined the knowledge, attitudes, and practice (KAP) of women of childbearing age regarding PFD and PFU.MethodsThis cross-sectional study was conducted between August 18, 2022, and September 20, 2022, in Sichuan, China. A total of 504 women of childbearing age participated in this study. A self-administered questionnaire was developed to assess KAP regarding PFD and PFU. Univariable and multivariable logistic regression analyses were conducted to assess the association between demographic characteristics and KAP.ResultsThe average scores for knowledge, attitudes, and practice were 12.53, 39.98, and 16.51 out of 17, 45, and 20, respectively. Despite adequate knowledge of PFD symptoms, aging-related risks, and PFD harms (correct rates > 80%), participants showed poor knowledge about the benefits of PFU, PFU types, and Kegel exercise (correct rates < 70%). High scores in knowledge and attitude (odds ratio = 1.23 and 1.11, P < 0.001 and P = 0.005, respectively) were independent predictors of good practice, while never having been pregnant (odds ratio = 0.10, P < 0.001), alcohol consumption (odds ratio = 0.09, P = 0.027), and not being diagnosed with PFD or an unclear diagnosis independently predicted poor practice (both odds ratio = 0.03, both P < 0.001).ConclusionWomen of childbearing age in Sichuan, China, showed moderate knowledge, positive attitude, and good practice regarding PFD and PFU. Knowledge, attitude, pregnancy history, alcohol consumption, and PFD diagnosis are associated with practice.

When intelligent mobile robots perform global path planning in complex and narrow environments, several issues often arise, including low search efficiency, node redundancy, non-smooth paths, and high costs. This paper proposes an improved path planning algorithm based on the rapidly exploring random tree (RRT) approach. Firstly, the target bias sampling method is employed to screen and eliminate redundant sampling points. Secondly, the adaptive step size strategy is introduced to address the limitations of the traditional RRT algorithm. The mobile robot is then modeled and analyzed to ensure that the path adheres to angle and collision constraints during movement. Finally, the initial path is pruned, and the path is smoothed using a cubic B-spline curve, resulting in a smoother path with reduced costs. The evaluation metrics employed include search time, path length, and the number of sampling nodes. To evaluate the effectiveness of the proposed algorithm, simulations of the RRT algorithm, RRT-connect algorithm, RRT* algorithm, and the improved RRT algorithm are conducted in various environments. The results demonstrate that the improved RRT algorithm reduces the generated path length by 25.32% compared to the RRT algorithm, 26.42% compared to the RRT-connect algorithm, and 4.99% compared to the RRT* algorithm. Moreover, the improved RRT algorithm significantly improves the demand for reducing path costs. The planning time of the improved RRT algorithm is reduced by 64.96% compared to that of the RRT algorithm, 40.83% compared to that of the RRT-connect algorithm, and 27.34% compared to that of the RRT* algorithm, leading to improved speed. These findings indicate that the proposed method exhibits a notable improvement in the three crucial evaluation metrics: sampling time, number of nodes, and path length. Additionally, the algorithm performed well after undergoing physical verification with an insect-like mobile robot in a real environment featuring narrow elevator entrances.

This study intends to evaluate the utilization potential of the combined Raman spectroscopy and machine learning approach to quickly identify the rainbow trout adulteration in Atlantic salmon. The adulterated samples contained various concentrations (0−100% w/w at 10% intervals) of rainbow trout mixed into Atlantic salmon. Spectral preprocessing methods, such as first derivative, second derivative, multiple scattering correction (MSC), and standard normal variate, were employed. Unsupervised algorithms, such as recursive feature elimination, genetic algorithm (GA), and simulated annealing, and supervised K-means clustering (KM) algorithm were used for selecting important spectral bands to reduce the spectral complexity and improve the model stability. Finally, the performances of various machine learning models, including linear regression, nonlinear regression, regression tree, and rule-based models, were verified and compared. The results denoted that the developed GA−KM−Cubist machine learning model achieved satisfactory results based on MSC preprocessing. The determination coefficient (R2) and root mean square error of prediction sets (RMSEP) in the test sets were 0.87 and 10.93, respectively. These results indicate that Raman spectroscopy can be used as an effective Atlantic salmon adulteration identification method; further, the developed model can be used for quantitatively analyzing the rainbow trout adulteration in Atlantic salmon.

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Background: After the 2009 influenza A(H1N1)pdm09 pandemic, China established its first severe acute respiratory infections (SARI) sentinel surveillance system. Methods: We analyzed data from SARI cases in 10 hospitals in 10 provinces in China from February 2011 to October 2013. Results: Among 5,644 SARI cases, 330 (6%) were influenza-positive. Among these, 62% were influenza A and 38% were influenza B. Compared with influenza-negative cases, influenza-positive SARI cases had a higher median age (20.0 years vs.11.0, p = 0.003) and were more likely to have at least one underlying chronic medical condition (age adjusted percent: 28% vs. 25%, p < 0.001). The types/subtypes of dominant strains identified by SARI surveillance was almost always among dominant strains identified by the influenza like illness (ILI) surveillance system and influenza activity in both systems peaked at the same time. Conclusions: Data from China's first SARI sentinel surveillance system suggest that types/subtypes of circulating influenza strains and epidemic trends among SARI cases were similar to those among ILI cases. [ABSTRACT FROM AUTHOR]