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This study provides a reference for the application of hyperbolic model in microwave ablation of atrial fibrillation.The human spine injury and various lumbar spine diseases caused by vibration have attracted extensive attention at home and abroad. To explore the biomechanical characteristics of different approaches for lumbar interbody fusion surgery combined with an interspinous internal fixator, device for intervertebral assisted motion (DIAM), finite element models of anterior lumbar interbody fusion (ALIF), transforaminal lumbar interbody fusion (TLIF) and lateral lumbar interbody fusion (LLIF) are created by simulating clinical operation based on a three-dimensional finite element model of normal human whole lumbar spine. The fusion level is at L4-L5, and the DIAM is implanted between spinous process of L4 and L5. Transient dynamic analysis is conducted on the ALIF, TLIF and LLIF models, respectively, to compute and compare their stress responses to an axial cyclic load. The results show that compared with those in ALIF and TILF models, contact forces between endplate and cage are higher in LLIF model, where the von-Mises stress in endplate and DIAM is lower. This implies that the LLIF have a better biomechanical performance under vibration. After bony fusion between vertebrae, the endplate and DIAM stresses for all the three surgical models are decreased. It is expected that this study can provide references for selection of surgical approaches in the fusion surgery and vibration protection for the postsurgical lumbar spine.The effect of parasitic ions on the results of ultraviolet A (UVA) cross-linking in iontophoresis was still not clear. In this work, the porcine sclera was cross-linked by riboflavin lactate Ringer's solution (group A) and riboflavin normal saline (group B) in vitro, respectively. The concentration of parasitic ions in the solution was calculated. In addition, the average fluorescence intensity, penetration depth and concentration after the introduction of riboflavin and the mechanical properties of cross-linked sclera tissue were measured. The ranges of diffusion coefficient of the two solutions were also calculated, respectively. The results showed that more kinds of parasitic ions were detected in group A compared with group B, while the average fluorescence intensity, penetration depth and concentration of riboflavin and scleral elastic modulus in group B were significantly higher than those in group A when the penetration time was 10 minutes. Besides, the diffusion coefficient of riboflavin in group B was about 1.5 times larger than that in group A. The results suggested that the species of parasitic ions has a great impact on the permeability of riboflavin, and affects the mechanical properties of cross-linked sclera. The above results could provide a reference for improving the efficiency of riboflavin introduction and optimizing the formula of riboflavin in iontophoresis scleral cross-linking.To solve the problem of stent malapposition of intravascular stents, explore the design method of intravascular body-fitted stent structure and to establish an objective apposition evaluation method, the support and apposition performance of body-fitted stent in the stenotic vessels with different degrees of calcified plaque were simulated and analyzed. The traditional tube-mesh-like stent model was constructed by using computational aided design tool SolidWorks, and based on this model, the body-fitted stent model was designed by means of projection algorithm. selleck products Abaqus was used to simulate the crimping-expansion-recoil process of the two stents in the stenotic vessel with incompletely calcified plaque and completely calcified plaque respectively. A comprehensive method for apposition evaluation was proposed considering three aspects such as separation distance, fraction of non-contact area and residual volume. Compared with the traditional stent, the separation distances of the body-fitted stent in the incompletely calcified plaque model and the completely calcified plaque model were decreased by 21.5% and 22.0% respectively, the fractions of non-contact areas were decreased by 11.3% and 11.1% respectively, and the residual volumes were decreased by 93.1% and 92.5% respectively. The body-fitted stent improved the apposition performance and was effective in both incompletely and completely calcified plaque models. The established apposition performance evaluation method of stent considered more geometric factors, and the results were more comprehensive and objective.The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the F 1 index was 98.3%. The algorithm has high performance, meets the needs of clinical diagnosis, and has low algorithm complexity. It can use low-power embedded processors for real-time calculations, and it's suitable for real-time warning of wearable ECG monitoring equipment.General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram (EEG) has been widely used in anesthesia depth monitoring for abundant information and the ability of reflecting the brain activity. The paper proposes a method which combines wavelet transform and artificial neural network (ANN) to assess the depth of anesthesia. Discrete wavelet transform was used to decompose the EEG signal, and the approximation coefficients and detail coefficients were used to calculate the 9 characteristic parameters. Kruskal-Wallis statistical test was made to these characteristic parameters, and the test showed that the parameters were statistically significant for the differences of the four levels of anesthesia awake, light anesthesia, moderate anesthesia and deep anesthesia ( P less then 0.001). The 9 characteristic parameters were used as the input of ANN, the bispectral index (BIS) was used as the reference output, and the method was evaluated by the data of 8 patients during general anesthesia. The accuracy of the method in the classification of the four anesthesia levels of the test set in the 73 set-out method was 85.98%, and the correlation coefficient with the BIS was 0.977 0. The results show that this method can better distinguish four different anesthesia levels and has broad application prospects for monitoring the depth of anesthesia.Analyzing the influence of mixed emotional factors on false memory through brain function network is helpful to further explore the nature of brain memory. In this study, Deese-Roediger-Mc-Dermott (DRM) paradigm electroencephalogram (EEG) experiment was designed with mixed emotional memory materials, and different kinds of music were used to induce positive, calm and negative emotions of three groups of subjects. For the obtained false memory EEG signals, standardized low resolution brain electromagnetic tomography algorithm (sLORETA) was applied in the source localization, and then the functional network of cerebral cortex was built and analyzed. The results show that the positive group has the most false memories [(83.3 ± 6.8)%], the prefrontal lobe and left temporal lobe are activated, and the degree of activation and the density of brain network are significantly larger than those of the calm group and the negative group. In the calm group, the posterior prefrontal lobe and temporal lobe are activated, and the collectivization degree and the information transmission rate of brain network are larger than those of the positive and negative groups. The negative group has the least false memories [(73.3 ± 2.2)%], and the prefrontal lobe and right temporal lobe are activated. link2 The brain network is the sparsest in the negative group, the degree of centralization is significantly larger than that of the calm group, but the collectivization degree and the information transmission rate of brain network are smaller than the positive group. The results show that the brain is stimulated by positive emotions, so more brain resources are used to memorize and associate words, which increases false memory. The activity of the brain is inhibited by negative emotions, which hinders the brain's memory and association of words and reduces false memory.Image registration is of great clinical importance in computer aided diagnosis and surgical planning of liver diseases. Deep learning-based registration methods endow liver computed tomography (CT) image registration with characteristics of real-time and high accuracy. However, existing methods in registering images with large displacement and deformation are faced with the challenge of the texture information variation of the registered image, resulting in subsequent erroneous image processing and clinical diagnosis. To this end, a novel unsupervised registration method based on the texture filtering is proposed in this paper to realize liver CT image registration. Firstly, the texture filtering algorithm based on L0 gradient minimization eliminates the texture information of liver surface in CT images, so that the registration process can only refer to the spatial structure information of two images for registration, thus solving the problem of texture variation. Then, we adopt the cascaded network to register images with large displacement and large deformation, and progressively align the fixed image with the moving one in the spatial structure. In addition, a new registration metric, the histogram correlation coefficient, is proposed to measure the degree of texture variation after registration. Experimental results show that our proposed method achieves high registration accuracy, effectively solves the problem of texture variation in the cascaded network, and improves the registration performance in terms of spatial structure correspondence and anti-folding capability. link3 Therefore, our method helps to improve the performance of medical image registration, and make the registration safely and reliably applied in the computer-aided diagnosis and surgical planning of liver diseases.

Raman spectroscopy has emerged as a promising technique for a variety of biomedical applications. The unique ability to provide molecular specific information offers insight to the underlying biochemical changes that result in disease states such as cancer. However, one of the hurdles to successful clinical translation is a lack of international standards for calibration and performance assessment of modern Raman systems used to interrogate biological tissue.

To facilitate progress in the clinical translation of Raman-based devices and assist the scientific community in reaching a consensus regarding best practices for performance testing.

We reviewed the current literature and available standards documents to identify methods commonly used for bench testing of Raman devices (e.g., relative intensity correction, wavenumber calibration, noise, resolution, and sensitivity). Additionally, a novel 3D-printed turbid phantom was used to assess depth sensitivity. These approaches were implemented on three fiberoptic-probe-based Raman systems with different technical specifications.