Meadowsmurphy4410

There was good primary patency at 12 months, and the results were sustained up to 36 months. It remains a useful modality for fistula salvage, avoiding surgical intervention.Coronavirus disease 2019 (COVID-19) has become a huge threat worldwide as a pandemic, which could also cause venous thromboembolism (VTE), including pulmonary embolism (PE). On the basis of the concept of the high risk for VTE in patients with COVID-19, some studies reported the potential benefit of anticoagulation for the primary prevention of VTE. However, optimal strategies for the prevention of VTE in COVID-19 still remain unknown. Additionally, ethnic differences may have notable implications in the presentation of VTE. Very recently, in the Japanese Society of Phlebology and Japanese Society of Pulmonary Embolism Research, a questionnaire surveillance for COVID-19 and VTE was conducted, which revealed that the vast majority of the institutions did not have specific recommendations for the prevention of VTE with anticoagulation, the incidence rate of VTE was 0.6% (7/1243), and that of PE was 0.4% (5/1243). The current questionnaire surveillance has suggested that the management strategies for the prevention of VTE by anticoagulation in COVID-19 could widely vary according to institutions, and the number of patients diagnosed as VTE in COVID-19 in Japan was quite small compared with reports from other countries. Further studies, including cohort/registry-based studies, are warranted to confirm these results.

Human growth hormone (hGH) is synthesized, stored, and secreted by somatotroph cells in the pituitary gland, and promotes human growth and metabolism. Compared to a normal pituitary, a GH-secreting pituitary adenoma can secrete excessive GH to cause pathological changes in body tissues. GH proteoform changes would be associated with GH-related disease pathogenesis.

This study aimed to elucidate changes in GH proteoforms between GH-secreting pituitary adenomas and control pituitaries for the predictive diagnostics, targeted prevention, and personalization of medical services.

The isoelectric point (pI) and relative molecular mass (Mr) are two basic features of a proteoform that can be used to effectively array and detect proteoforms with two-dimensional gel electrophoresis (2DGE) and 2DGE-based western blot. GH proteoforms were characterized with liquid chromatography (LC) and mass spectrometry (MS). Phosphoproteomics, ubiquitinomics, acetylomics, and bioinformatics were used to analyze post-translationaH. Ubiquitinomics identified ubiquitination at residue Lys96 in hGH. Acetylomics identified acetylation at reside Lys171 in hGH. Deamination was identified at residue Asn178 in hGH.

These findings provide the first hGH proteoform pattern changes in GH-secreting pituitary adenoma tissues compared to control pituitary tissues, and the status of partial PTMs in hGH proteoforms. click here Those data provide in-depth insights into biological roles of hGH in GH-related diseases, and identify hGH proteoform pattern biomarkers for treatment of a GH-secreting pituitary adenoma in the context of 3P medicine -predictive diagnostics, targeted prevention, and personalization of medical services.

The online version contains supplementary material available at 10.1007/s13167-021-00232-7.

The online version contains supplementary material available at 10.1007/s13167-021-00232-7.

To establish the morphological and functional parameters to predict the effectiveness of intravitreal injections (IVI) of ranibizumab in macular edema due to retinal vein occlusion and to develop a mathematical model forpersonalized treatment algorithms.

This is a retrospective study of 98 patients (98 eyes) with macular edema, who received IVI of ranibizumab and were followed up for 12months. Spectral optical coherence tomography scans and best corrected visual acuity (BCVA) assessments were conducted every 3months. Treatment outcome predictors were calculated based on logistic regression analysis.

The most significant prognostic factors for the long-term BCVA were baseline BCVA (OR 11.1,

 = 0.001), foveal volume (OR 10.8,

 = 0.001), destruction of external limiting membrane (OR 15.8,

 = 0.001), photoreceptor inner/outer segments (OR 11.1,

 = 0.001) and retinal pigment epithelium (OR 9.1,

 = 0.001). It has also been discovered that post-treatment BCVA correlated with the height of serous retito increase the treatment effectiveness and to prevent low vision that corresponds to the principles of predictive, preventive, and personalized medicine.

The papillomacular bundle (PMB) area is an important anatomical site associated with central vision. As preventive medicine and health screening examinations are now becoming commonplace, the incidental detection of papillomacular bundle defect (PMBD) on fundus photography has been increasing. However, clinical significance of incidental PMBD has not been well documented to date. Thus, through long-term and longitudinal observation, we aimed to investigate the risk factors for the development and progression of PMBD and its predictive role associated with systemic diseases and glaucoma.

This longitudinal study included subjects who had undergone standardized health screening. We retrospectively reviewed patients for whom PMBD had been detected in fundus photography and followed up for more than 5 years. For a comparative analysis, non-PMBD groups of age- and gender-matched healthy controls were selected.

A total of about 67,000 fundus photographs were analyzed for 8.0 years, and 587 PMBD eyes were foundis and preventive management of glaucoma.

PMBD is associated with ischemic effects. Although the majority of PMBD do not progress, some of cases are associated with glaucomatous damage in a long-term way. PMBD might be a personalized indicator representing ischemia-associated diseases and a predictive factor for diagnosis and preventive management of glaucoma.Deep learning has achieved great success in areas such as computer vision and natural language processing. In the past, some work used convolutional networks to process EEG signals and reached or exceeded traditional machine learning methods. We propose a novel network structure and call it QNet. It contains a newly designed attention module 3D-AM, which is used to learn the attention weights of EEG channels, time points, and feature maps. It provides a way to automatically learn the electrode and time selection. QNet uses a dual branch structure to fuse bilinear vectors for classification. It performs four, three, and two classes on the EEG Motor Movement/Imagery Dataset. The average cross-validation accuracy of 65.82%, 74.75%, and 82.88% was obtained, which are 7.24%, 4.93%, and 2.45% outperforms than the state-of-the-art, respectively. The article also visualizes the attention weights learned by QNet and shows its possible application for electrode channel selection.Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.), providing a basis for further face analysis, modification, and other applications. Interlinked Convolutional Neural Networks (iCNN) was proved to be an effective two-stage model for face parsing. However, the original iCNN was trained separately in two stages, limiting its performance. To solve this problem, we introduce a simple, end-to-end face parsing framework STN-aided iCNN(STN-iCNN), which extends the iCNN by adding a Spatial Transformer Network (STN) between the two isolated stages. The STN-iCNN uses the STN to provide a trainable connection to the original two-stage iCNN pipeline, making end-to-end joint training possible. Moreover, as a by-product, STN also provides more precise cropped parts than the original cropper. Due to these two advantages, our approach significantly improves the accuracy of the original model. Our model achieved competitive performance on the Helen Dataset, the standard face parsing dataset. It also achieved superior performance on CelebAMask-HQ dataset, proving its good generalization. Our code has been released at https//github.com/aod321/STN-iCNN.In order to overcome the security weakness of the discrete chaotic sequence caused by small Lyapunov exponent and keyspace, a general chaotic construction method by cascading multiple high-dimensional isomorphic maps is presented in this paper. Compared with the original map, the parameter space of the resulting chaotic map is enlarged many times. Moreover, the cascaded system has larger chaotic domain and bigger Lyapunov exponents with proper parameters. In order to evaluate the effectiveness of the presented method, the generalized 3-D Hénon map is utilized as an example to analyze the dynamical behaviors under various cascade modes. Diverse maps are obtained by cascading 3-D Hénon maps with different parameters or different permutations. It is worth noting that some new dynamical behaviors, such as coexisting attractors and hyperchaotic attractors are also discovered in cascaded systems. Finally, an application of image encryption is delivered to demonstrate the excellent performance of the obtained chaotic sequences.Brain-computer interface (BCI) system based on motor imagery (MI) usually adopts multichannel Electroencephalograph (EEG) signal recording method. However, EEG signals recorded in multi-channel mode usually contain many redundant and artifact information. Therefore, selecting a few effective channels from whole channels may be a means to improve the performance of MI-based BCI systems. We proposed a channel evaluation parameter called position priori weight-permutation entropy (PPWPE), which include amplitude information and position information of a channel. According to the order of PPWPE values, we initially selected half of the channels with large PPWPE value from all sampling electrode channels. Then, the binary gravitational search algorithm (BGSA) was used in searching a channel combination that will be used in determining an optimal channel combination. The features were extracted by common spatial pattern (CSP) method from the final selected channels, and the classifier was trained by support vector machine. The PPWPE + BGSA + CSP channel selection method is validated on two data sets. Results showed that the PPWPE + BGSA + CSP method obtained better mean classification accuracy (88.0% vs. 57.5% for Data set 1 and 91.1% vs. 79.4% for Data set 2) than All-C + CSP method. The PPWPE + BGSA + CSP method can achieve higher classification in fewer channels selected. This method has great potential to improve the performance of MI-based BCI systems.Acupuncturing the Zusanli (ST 36) point with different types of manual acupuncture manipulations (MAs) and different frequencies can evoke a lot of neural response activities in spinal dorsal root neurons. The action potential is the basic unit of communication in the neural response process. With the rapid development of the electrode acquisition technology, we can simultaneously obtain neural electrical signals of multiple neurons in the target area. So it is crucial to extract spike trains of each neuron from raw recorded data. To solve the problem of variability of the spike waveform, this paper adopts a optimization algorithm based on model to improve the wave-cluster algorithm, which can provide higher accuracy and reliability. Further, through this optimization algorithm, we make a statistical analysis on spike events evoked by different MAs. Results suggest that numbers of response spikes under reinforcing manipulations are far more than reducing manipulations, which mainly embody in synchronous spike activities.