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Cross-sectional as well as Test-Retest Characterization involving Family pet along with [(18)F]FP-(+)-DTBZ regarding β Mobile Muscle size Estimations throughout Diabetes.
The eyeball should be covered with a square flume filled with ultrasonic coupling gel for the sake of imaging during the experiment. The Maximal Information Coefficient algorithm could be applied to correct the measured results before post-processing calculation. PCPA The results indicated that feasible injection strategy of fluorescent particles and the correction strategy for image acquisition are critical to obtain nice experiment effects for the in vivo PIV measurement of anterior chamber flow field in the rabbit with acute intraocular hypertension.In order to conduct surface monitoring of the three-dimensional spine morphology of the human body in daily life, a spine morphology measuring method using "single camera, multi-view" to construct stereo vision is proposed. The images of the back of the human body with landmarks of spinous process are captured from multiple angles by moving a single camera, and based on the "Zhang Zhengyou calibration method" and the triangulation principle of binocular stereo vision, the spatial conversion matrices corresponding to each other between all images and the 3D coordinates of the landmarks are calculated. Then the spine evaluation angle used to evaluate the spine morphology is further calculated. The tests' results showed that the spine evaluation angle error of this method is within ±3°, and the correlation between the results and the X-ray film Cobb angles is 0.871. The visual detection algorithm used in this paper is non-radioactive, and because only one camera is used in the measurement process and there is no need to pre-set the camera's shooting pose, the operation is simple. The research results of this article can be used in a mobile phone-based intelligent detection system, which will be suitable for the group survey of scoliosis in communities, schools, families and other occasions, as well as for the long-term follow-up of confirmed patients. This will provide a reference for doctors to diagnose the condition, predict the development trend of the condition, and formulate treatment plans.Stress distribution of denture is an important criterion to evaluate the reasonableness of technological parameters, and the bite force derived from the antagonist is the critical load condition for the calculation of stress distribution. In order to improve the accuracy of stress distribution as much as possible, all-ceramic crown of the mandibular first molar with centric occlusion was taken as the research object, and a bite force loading method reflecting the actual occlusal situation was adopted. Firstly, raster scanning and three dimensional reconstruction of the occlusal surface of molars in the standard dental model were carried out. Meanwhile, the surface modeling of the bonding surface was carried out according to the preparation process. Secondly, the parametric occlusal analysis program was developed with the help of OFA function library, and the genetic algorithm was used to optimize the mandibular centric position. Finally, both the optimized case of the mesh model based on the results of occlusal optimization and the referenced case according to the cusp-fossa contact characteristics were designed. The stress distribution was analyzed and compared by using Abaqus software. The results showed that the genetic algorithm was suitable for solving the occlusal optimization problem. Compared with the reference case, the optimized case had smaller maximum stress and more uniform stress distribution characteristics. The proposed method further improves the stress accuracy of the prosthesis in the finite element model. Also, it provides a new idea for stress analysis of other joints in human body.Research on the deposition of inhaled particles in human pulmonary acinus region is important to the pathogenesis investigation, prevention and treatment of lung diseases. Most of the current research focus on the final deposition fraction of inhaled particles in human acinar region, but little is involved in their dynamic deposition characteristics. In this paper, five multi-alveolar models, G3-G7, were built. The evaluation parameter 1/4 deposition time was introduced to study the particle deposition speed. The deposition characteristics of particles in the diameter ranging 0.1-5 μm were numerically simulated and summarized under the influence of factors such as the generation and structure of model, particle diameter and respiratory mode, shedding some new light on the further research of transport of inhaled particles. The results showed that the generation and structure of model had a significance effect on the deposition of particles. 0.1 μm particles were dominated by Brownian diffusion, which experienced a high deposition fraction, a fast deposition speed and a logarithmic deposition curve, while 5 μm particles were dominated by gravitational sedimentation, with a high deposition fraction, a fast deposition speed and an S-shaped deposition curve. The deposition of 0.3-1 μm particles were influenced greatly by convention and varied with the change of respiratory mode. The research methods and results in this paper can provide theoretical basis and data support for the further exploration of the mechanism, prevention and treatment of lung diseases.As drug carriers, magnetic nanoparticles can specifically bind to tumors and have the potential for targeted therapy. It is of great significance to explore non-invasive imaging methods that can detect the distribution of magnetic nanoparticles. Based on the mechanism that magnetic nanoparticles can generate ultrasonic waves through the pulsed magnetic field excitation, the sound pressure wave equation containing the concentration information of magnetic nanoparticles was derived. Using the finite element method and the analytical solution, the consistent transient pulsed magnetic field was obtained. A three-dimensional simulation model was constructed for the coupling calculation of electromagnetic field and sound field. The simulation results verified that the sound pressure waveform at the detection point reflected the position of magnetic nanoparticles in biological tissue. Using the sound pressure data detected by the ultrasonic transducer, the B-scan imaging of the magnetic nanoparticles was achieved. The maximum error of the target area position was 1.56%, and the magnetic nanoparticles regions with different concentrations were distinguished by comparing the amplitude of the boundary signals in the image. Studies in this paper indicate that B-scan imaging can quickly and accurately obtain the dimensional and positional information of the target region and is expected to be used for the detection of magnetic nanoparticles in targeted therapy.Denoising methods based on wavelet analysis and empirical mode decomposition cannot essentially track and eliminate noise, which usually cause distortion of heart sounds. Based on this problem, a heart sound denoising method based on improved minimum control recursive average and optimally modified log-spectral amplitude is proposed in this paper. The proposed method uses a short-time window to smoothly and dynamically track and estimate the minimum noise value. The noise estimation results are used to obtain the optimal spectrum gain function, and to minimize the noise by minimizing the difference between the clean heart sound and the estimated clean heart sound. In addition, combined with the subjective analysis of spectrum and the objective analysis of contribution to normal and abnormal heart sound classification system, we propose a more rigorous evaluation mechanism. The experimental results show that the proposed method effectively improves the time-frequency features, and obtains higher scores in the normal and abnormal heart sound classification systems. The proposed method can help medical workers to improve the accuracy of their diagnosis, and also has great reference value for the construction and application of computer-aided diagnosis system.Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S1, systole, S2 and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F1) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.Repetitive transcranial magnetic stimulation(rTMS) is a painless and non-invasive method for stimulation and modulation in the field of cognitive neuroscience research and clinical neurological regulation. In this paper, adult Wistar rats were divided into the rTMS group and control group randomly. Rats in the rTMS group were stimulated with 5 Hz rTMS for 14 days, while the rats in the control group did not accept any stimulation. Then, the behavior and local field potentials (LFPs) were recorded synchronously when the rats perform a working memory (WM) task with T-maze. Finally, the time-frequency distribution and coherence characteristics of the LFPs signal in the prefrontal cortex (PFC) during working memory task were analyzed. The results showed that the rats in the rTMS group needed less training days to reach the task correction criterion than the control group (P less then 0.05). Compared with the control group, the rTMS group has higher energy (P less then 0.01) in θ band (4~12 Hz) and γ band (30~80 Hz). The coherence between the channel pairs decreases as the spatial distance of the channel pairs increases, and the rTMS group exhibits a higher coherence than the control group (P less then 0.01). It is concluded that 5 Hz rTMS can improve the excitability of rat prefrontal cortical neurons to a certain extent, and has a positive effect on the working memory ability of normal rats. The results of this paper may provide important theoretical support for further research on the mechanism of action of rTMS on WM.Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD ［age (8.27 ± 2.77) years］ and 23 normal children ［age (8.70 ± 2.58) years］ were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children.
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