Stability control of high-speed magnetic levitation turbomolecular pumps with shock-excited disturbance.

The disturbance suppression of magnetic levitation turbomolecular pumps is a critical problem in industrial applications. This work addresses the stability control of high-speed magnetic levitation turbomolecular pumps with shock-excited disturbance. A disturbance suppression method based on improved linear extended state observer is proposed to attenuate the impact of external low-frequency disturbing force on a magnetic levitation turbomolecular pump. Firstly, a linear extended state observer of an active magnetic bearing is obtained by analyzing the rotor dynamics model. Then, the detailed descriptions of external disturbance suppression method using linear extended state observers and adaptive notch filters are discussed for the system. The boundary condition of the parameters of the improved linear extended state observer is determined. The root loci of the closed-loop system with improved linear extended state observers is also investigated. Finally, simulation and experimental results on a magnetic levitation turbomolecular pump show the applicability of the proposed method. The results show that the proposed method can attenuate the rotor vibration displacement caused by impact by 46.9%.

Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes.

This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.

The intra-arterial selective cooling infusion system: A mathematical temperature analysis and in vitro experiments for acute ischemic stroke therapy.

INTRODUCTION: The neuroprotection of acute ischemic stroke patients can be achieved by intra-arterial selective cooling infusion using cold saline, which can decrease brain temperature without influencing the body core temperature. This approach can lead to high burdens on the heart and decreased hematocrit in the scenario of loading a high amount of liquid for longtime usage. Therefore, autologous blood is utilized as perfusate to circumvent those side effects. METHODS: In this study, a prototype instrument with an autologous blood cooling system was developed and further evaluated by a mathematical model for brain temperature estimation. RESULTS: Hypothermia could be achieved due to the adequate cooling capacity of the prototype system, which could provide the lowest cooling temperature into the blood vessel of 10.5°C at 25 rpm (209.7 ± 0.8 ml/min). And, the core body temperature did not alter significantly (-0.7 ~ -0.2°C) after 1-h perfusion. The cooling rate and temperature distributions of the brain were analyzed, which showed a 2°C decrease within the initial 5 min infusion by 44 ml/min and 13.7°C perfusate. CONCLUSION: This prototype instrument system could safely cool simulated blood in vitro and reperfuse it to the target cerebral blood vessel. This technique could promote the clinical application of an autologous blood perfusion system for stroke therapy.

The blood heat exchanger in intra-arterial selective cooling infusion for acute ischemic stroke: A computational fluid-thermodynamics performance, experimental assessment and evaluation on the brain temperature.

Intra-arterial selective cooling infusion with the autologous blood (IA-SCAI) is a promising therapeutic hypothermia induction method for conferring neuroprotection to acute ischemic stroke (AIS) patients. The blood heat exchanger (BHE) plays a crucial role in IA-SCAI's cooling capacity. However, there are no BHEs currently available that are specifically designed for the IA-SCAI, which requires a low blood flow to be compatible with cerebral hemodynamics. In an effort to develop a BHE for AIS patients, a prototype of a commercial BHE, Medtronic MYOtherm XP®, was mathematically modeled; specifically, computational fluid dynamics (CFD) was used to analyze its hemo- and thermo-dynamic characteristics under low blood flow including temperature distribution, velocity field and shear stress. Our numerical model predicted the hemolysis index to be 0.0041%-0.0581% inside the BHE with blood flows rates of 10 ml min-1-50 ml min-1. The in vitro heat transfer experiment showed that the BHE efficiently cooled the simulated blood from the initial 37 °C-5.8 °C within 150 s by using cold water (200 ml·min-1, 0 °C). The cooled simulated blood was able to cool the simulated blood in the middle cerebral artery of an artificial circulating system from 37 °C to 16.8 °C-33.7 °C depending on the blood perfusion rate (10-50 ml/min). A biological heat transfer mathematical model showed that brain tissue could be cooled by 2 °C within the initial 1min of infusion. This study verified the feasibility of using a commercial BHE for IA-SCAI and provided insights into its cooling capacity for therapeutic hypothermia.

A noise-immune reinforcement learning method for early diagnosis of neuropsychiatric systemic lupus erythematosus.

The neuropsychiatric systemic lupus erythematosus (NPSLE), a severe disease that can damage the heart, liver, kidney, and other vital organs, often involves the central nervous system and even leads to death. Magnetic resonance spectroscopy (MRS) is a brain functional imaging technology that can detect the concentration of metabolites in organs and tissues non-invasively. However, the performance of early diagnosis of NPSLE through conventional MRS analysis is still unsatisfactory. In this paper, we propose a novel method based on genetic algorithm (GA) and multi-agent reinforcement learning (MARL) to improve the performance of the NPSLE diagnosis model. Firstly, the proton magnetic resonance spectroscopy (1H-MRS) data from 23 NPSLE patients and 16 age-matched healthy controls (HC) were standardized before training. Secondly, we adopt MARL by assigning an agent to each feature to select the optimal feature subset. Thirdly, the parameter of SVM is optimized by GA. Our experiment shows that the SVM classifier optimized by feature selection and parameter optimization achieves 94.9% accuracy, 91.3% sensitivity, 100% specificity and 0.87 cross-validation score, which is the best score compared with other state-of-the-art machine learning algorithms. Furthermore, our method is even better than other dimension reduction ones, such as SVM based on principal component analysis (PCA) and variational autoencoder (VAE). By analyzing the metabolites obtained by MRS, we believe that this method can provide a reliable classification result for doctors and can be effectively used for the early diagnosis of this disease.

A modified active disturbance rejection control strategy based on cascade structure with enhanced robustness.

Active disturbance rejection control (ADRC) is considered to be a common control method in the presence of external disturbances and uncertainties. This work proposes a modified ADRC control strategy to further enhance robustness to uncertainties. First, the cascade structure of modified ADRC composed by two control loops is introduced. Two extended state observers that can be tuned independently are arranged in a cascade structure. The outer loop observer provides system state estimation to build the feedback controller, and the inner loop observer deals with the generalized disturbance. Then, the enhanced robustness is verified by the improved sensitivity in the intermediate frequency range. Finally, for the axial magnetic bearing system with unstable system dynamics, the variation trend in maximum sensitivity with different control parameters is explained in the simulation. The experiments are conducted under varying disturbances and parameter uncertainty to verify the enhanced control performance.

Design and evaluation of an air-insulated catheter for intra-arterial selective cooling infusion from numerical simulation and in vitro experiment.

Intra-arterial selective cooling infusion (IA-SCI) is a promising method for neuroprotection of patients with acute ischemic stroke. One shortcoming of IA-SCI is the elevated delivery temperature caused by the cold perfusate warming along the catheter pathway. Therefore, increasing the thermal resistance of the catheter is of significant importance. In this manuscript, an air-insulated catheter was designed and manufactured through extrusion molding technique. The computational fluid dynamics (CFD)-based thermo-/hemo-dynamics models were exploited to evaluate the thermal conductivity of the catheter. Compared with commercially available endovascular catheters, its thermal insulation property was analyzed through an in vitro experiment. The temperature of the 4°C perfusate (20 ml/min) increased to 14.2°C ± 0.2°C after being transferred to the distal tip of the air-insulated catheter, which was significantly lower than that (30°C) of commercially available alternatives. Moreover, the simulated blood (56% glycerin and 44% bi-distilled water, 37°C) in the middle cerebral artery of the artificial circulating system was cooled down to 29.7°C ± 0.1°C by this perfusate. The cooling process of the brain tissue was also estimated by a biological heat-transfer mathematical model, which showed a 2°C decrease within the initial 1 min infusion. This study demonstrated that the air-insulated catheter for IA-SCI was promising in vitro in terms of its high cooling efficiency and could be a competitive intervention catheter for therapeutic hypothermia.

Moving-gimbal effect suppression for AMB-rotor systems with improved dynamic response in control moment gyroscopes.

The moving-gimbal effect (MGE) is an important factor that limits the torque accuracy provided by magnetically suspended control moment gyroscope. This work addresses the issues concerning the dynamic behavior of active magnetic bearing (AMB) systems under MGE. First, the dynamics modeling of AMB-rotor system is established. Then, a linear extended state observer (LESO) based control is proposed to suppress the MGE. The dynamic response of PID control is significantly improved by replacing the integral term with LESO. The LESO gain tuning procedure is performed by analyzing the pole-zero assignment that determines the dynamic performance. The stability of LESO is proved by the convergence of estimation error dynamics. Finally, the proposed method shows effective suppression on MGE with less power consumptions in experiment.

Unsupervised Cross-Modality Domain Adaptation Network for X-Ray to CT Registration.

2D/3D registration that achieves high accuracy and real-time computation is one of the enabling technologies for radiotherapy and image-guided surgeries. Recently, the Convolutional Neural Network (CNN) has been explored to significantly improve the accuracy and efficiency of 2D/3D registration. A pair of intraoperative 2-D x-ray images and synthetic data from pre-operative volume are often required to model the nonconvex mappings between registration parameters and image residual. However, a large clinical dataset collection with accurate poses for x-ray images can be very challenging or even impractical, while exclusive training on synthetic data can frequently cause performance degradation when tested on x-rays. Thus, we propose to train a model on source domain (i.e., synthetic data) to build appearance-pose relationship first and then use an unsupervised cross-modality domain adaptation network (UCMDAN) to adapt the model to target domain (i.e., X-rays) through adversarial learning. We propose to narrow the significant domain gap by alignment in both pixel and feature space. In particular, the image appearance transformation and domain-invariance feature learning by multiple aspects are conducted synergistically. Extensive experiments on CT and CBCT dataset show that the proposed UCMDAN outperforms the existing state-of-the-art domain adaptation approaches.

[Treatment of degenerative medial meniscus injury of knee joint by arthroscopy combined with small needle knife to release superficial medial collateral ligament of knee joint].

OBJECTIVE: To explore the clinical effect of arthroscopic combined with small needle knife in the treatment of degenerative medial meniscus (MM) injury of knee joint by releasing the superficial layer of medial collateral ligament (SMCL). METHODS: From February 2016 to November 2018, 56 patients (56 knees) with limited pain, strangulation and flexion in medial knee joint space were selected. X-ray Kellgren-Lawrence grading was I-II. MRI showed medial meniscus injury(III degree) of knee joint. There were 30 males(30 knees) and 26 females(26 knees). Arthroscopic MM plasty and small needle knife were used to release SMCL. The Lysholm knee score was used to evaluate the effect of operation. RESULTS: All 56 patients were followed up, and the duration ranged from 3 to 24 months, with an average of 10 months. According to the Lysholm knee score standard, the final follow-up was compared with that of before operation. The results showed that the preoperative knee score was 37.24±1.32, the latest follow-up knee score was 85.72±5.28, the knee score was higher than that before the operation(P<0.05). CONCLUSIONS: Arthroscopy combined with small needle knife release of superficial medial collateral ligament in the treatment of degenerative medial knee meniscus injury can effectively improve the mechanical balance of the knee joint, improve Lysholm knee score in patients with knee meniscus injury, and promote the recovery of knee joint function, which has clinical value.

[Practices and Discusses on Medical Equipment Information Management of Military Hospital].

This paper briefly expounds the present situation of medical equipment information management in military hospitals. From basic structure and main function of the software systems, it gives a detailed introduction of the probes and researches on medical equipment information management in recent years. It analyzes the existing problems and shortcomings, combined with the previous achievements and effects, and then it introduces the ideas and goals of the new medical equipment management information system, which now being developed. Then it discusses the key problems of the new system which need to be solved.