Notes

Issues associated with laser-assisted in situ keratomileusis.
Mechanical ventilation is an importmant life-sustaining treatment for patients with acute respiratory distress syndrome. Its clinical outcomes depend on patients' characteristics of lung recruitment. Vorinostat mouse Estimation of lung recruitment characteristics is valuable for the determination of ventilatory maneurvers and ventilator parameters. There is no easily-used, bedside method to assess lung recruitment characteristics. The present paper proposed a method to estimate lung recruitment characteristics from the static pressure-volume curve of lungs. The method was evaluated by comparing with published experimental data. Results of lung recruitment derived from the presented method were in high agreement with the published data, suggesting that the proposed method is capable to estimate lung recruitment characteristics. Since some advanced ventilators are capable to measure the static pressure-volume curve automatedly, the presented method is potential to be used at bedside, and it is helpful for clinicians to individualize ventilatory manuevers and the correpsonding ventilator parameters.To explore the focus and trends in real-world studies in Chinese through knowledge mapping method, databases CNKI, VIP, Wanfang and Sinomed were retrieved, with 1 757 relevant articles published before September 30rd, 2020 finally included, whose bibliographical records were imported into NoteExpress to avoid duplication and check relativity. VOSviewer, a bibliometric analysis tool, was used to analyze their development. It was found that real-world studies have mainly taken shape after 2010, in which traditional Chinese medicine research plays an important role. China Journal of Chinese Material Medica was the leading journal with 120 papers, the China Academy of Chinese Medical Sciences the most contribution institution with 338 papers, and Xie Yanming from the institution the most contribution author with 250 papers. This study helps clinicians and researchers in better understanding the evolution of real-world research over more than two decades in China.This study aims to explore the intraventricular pressure difference (IVPD) within left ventricle in patients with paroxysmal atrial fibrillation (PAF) by using the relative pressure imaging (RPI) of vector flow mapping (VFM). Twenty patients with paroxysmal atrial fibrillation (PAF) and thirty control subjects were enrolled in the study. Systolic and diastolic IVPD derived from VFM within left ventricle and conventional echocardiographic parameters were analyzed. It was found that the B-A IVPD of left ventricle in PAF patients showed the same pattern as controls-single peak and single valley during systole and double peaks and double valleys during diastole. Basal IVPD was the main component of base to apex IVPD (B-A IVPD). The isovolumetric systolic IVPD was associated with early systolic IVPD, early systolic IVPD was associated with late systolic IVPD, and late systolic IVPD was associated with isovolumic diastolic IVPD (all P less then 0.05). The B-A IVPD and basal IVPD during isovolumetric systole, early systole, late systole and isovolumetric diastole in PAF patients significantly decreased (all P less then 0.05). The study shows that the B-A IVPD pattern of the PAF group is the same as controls, but systolic B-A IVPD and basal IVPD are significantly reduced in PAF patients. VFM-derived RPI can evaluate left ventricular IVPD in PAF patients, providing a visually quantitative method for evaluating left ventricular hemodynamic mechanics in the patients with PAF.Lower extremity movement is a complex and large range of limb movement. Arterial stents implanted in lower extremity are prone to complex mechanical deformation, so the stent is required to have high comprehensive mechanical properties. In order to evaluate the mechanical property of different stents, in this paper, finite element method was used to simulate and compare the mechanical properties of six nitinol stents (Absolute Pro, Complete SE, Lifestent, Protégé EverFlex, Pulsar-35 and New) under different deformation modes, such as radial compression, axial compression/tension, bending and torsion, and the radial support performance of the stents was verified by experiments. The results showed that the comprehensive performance of New stent was better than other stents. Among which the radial support performance was higher than Absolute Pro and Pulsar-35 stent, the axial support performance was better than Complete SE, Lifestent and Protégé EverFlex stent, the flexibility was superior to Protégé Everflex stent, and the torsion performance was better than Complete SE, Lifestent and Protégé Everflex stent. The TTR2 type radial support force tester was used to test the radial support performance of 6 types, and the finite element analysis results were verified. The mechanical properties of the stent are closely related to the structural size. The result provides a reference for choosing a suitable stent according to the needs of the diseased location in clinical applications.In the study of oral orthodontics, the dental tissue models play an important role in finite element analysis results. Currently, the commonly used alveolar bone models mainly have two kinds the uniform and the non-uniform models. The material of the uniform model was defined with the whole alveolar bone, and each mesh element has a uniform mechanical property. While the material of the elements in non-uniform model was differently determined by the Hounsfield unit (HU) value of computed tomography (CT) images where the element was located. To investigate the effects of different alveolar bone models on the biomechanical responses of periodontal ligament (PDL), a clinical patient was chosen as the research object, his mandibular canine, PDL and two kinds of alveolar bone models were constructed, and intrusive force of 1 N and moment of 2 Nmm were exerted on the canine along its root direction, respectively, which were used to analyze the hydrostatic stress and the maximal logarithmic principal strain of PDL under different loads. Research results indicated that the mechanical responses of PDL had been affected by alveolar bone models, no matter the canine translation or rotation. Compared to the uniform model, if the alveolar bone was defined as the non-uniform model, the maximal stress and strain of PDL were decreased by 13.13% and 35.57%, respectively, when the canine translation along its root direction; while the maximal stress and strain of PDL were decreased by 19.55% and 35.64%, respectively, when the canine rotation along its root direction. The uniform alveolar bone model will induce orthodontists to choose a smaller orthodontic force. The non-uniform alveolar bone model can better reflect the differences of bone characteristics in the real alveolar bone, and more conducive to obtain accurate analysis results.By analyzing the physiological structure and motion characteristics of human ankle joint, a four degree of freedom generalized spherical parallel mechanism is proposed to meet the needs of ankle rehabilitation. Using the spiral theory to analyze the motion characteristics of the mechanism and based on the method of describing the position with spherical coordinates and the posture with Euler Angle, the inverse solution of the closed vector equation of mechanism position is established. The workspace of mechanism is analyzed according to the constraint conditions of inverse solution. The workspace of the moving spherical center of the mechanism is used to match the movement space of the tibiotalar joint, and the workspace of the dynamic platform is used to match the movement space of subtalar joint. Genetic algorithm is used to optimize the key scale parameters of the mechanism. The results show that the workspace of the generalized spherical parallel mechanism can satisfy the actual movement space of human ankle joint rehabilitation. The results of this paper can provide theoretical basis and experimental reference for the design of ankle joint rehabilitation robot with high matching degree.The existing retinal vessels segmentation algorithms have various problems that the end of main vessels are easy to break, and the central macula and the optic disc boundary are likely to be mistakenly segmented. To solve the above problems, a novel retinal vessels segmentation algorithm is proposed in this paper. The algorithm merged together vessels contour information and conditional generative adversarial nets. Firstly, non-uniform light removal and principal component analysis were used to process the fundus images. Therefore, it enhanced the contrast between the blood vessels and the background, and obtained the single-scale gray images with rich feature information. Secondly, the dense blocks integrated with the deep separable convolution with offset and squeeze-and-exception (SE) block were applied to the encoder and decoder to alleviate the gradient disappearance or explosion. Simultaneously, the network focused on the feature information of the learning target. Thirdly, the contour loss function was added to improve the identification ability of the blood vessels information and contour information of the network. Finally, experiments were carried out on the DRIVE and STARE datasets respectively. The value of area under the receiver operating characteristic reached 0.982 5 and 0.987 4, respectively, and the accuracy reached 0.967 7 and 0.975 6, respectively. Experimental results show that the algorithm can accurately distinguish contours and blood vessels, and reduce blood vessel rupture. The algorithm has certain application value in the diagnosis of clinical ophthalmic diseases.In order to overcome the shortcomings of high false positive rate and poor generalization in the detection of microcalcification clusters regions, this paper proposes a method combining discriminative deep belief networks (DDBNs) to automatically and quickly locate the regions of microcalcification clusters in mammograms. Firstly, the breast region was extracted and enhanced, and the enhanced breast region was segmented to overlapped sub-blocks. Then the sub-block was subjected to wavelet filtering. After that, DDBNs model for breast sub-block feature extraction and classification was constructed, and the pre-trained DDBNs was converted to deep neural networks (DNN) using a softmax classifier, and the network is fine-tuned by back propagation. Finally, the undetected mammogram was inputted to complete the location of suspicious lesions. By experimentally verifying 105 mammograms with microcalcifications from the Digital Database for Screening Mammography (DDSM), the method obtained a true positive rate of 99.45% and a false positive rate of 1.89%, and it only took about 16 s to detect a 2 888 × 4 680 image. The experimental results showed that the algorithm of this paper effectively reduced the false positive rate while ensuring a high positive rate. The detection of calcification clusters was highly consistent with expert marks, which provides a new research idea for the automatic detection of microcalcification clusters area in mammograms.Fetal electrocardiogram signal extraction is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of fetal electrocardiogram signal, this paper proposes a fetal electrocardiogram signal extraction method (GA-LSTM) based on genetic algorithm (GA) optimization with long and short term memory (LSTM) network. Firstly, according to the characteristics of the mixed electrocardiogram signal of the maternal abdominal wall, the global search ability of the GA is used to optimize the number of hidden layer neurons, learning rate and training times of the LSTM network, and the optimal combination of parameters is calculated to make the network topology and the mother body match the characteristics of the mixed signals of the abdominal wall. Then, the LSTM network model is constructed using the optimal network parameters obtained by the GA, and the nonlinear transformation of the maternal chest electrocardiogram signals to the abdominal wall is estimated by the GA-LSTM network.
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