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Improvement in Asthma attack Signs and also Pulmonary Purpose in youngsters After SARS-CoV-2 Episode.
Intracellular refractive index (RI) is an essential biophysical parameter, which best represents the mass and the distribution of proteins in the cell interior, including high-density accumulations in membraneless organelles. For RI measurements, a number of sophisticated techniques have been developed; however most of the new approaches are either insufficiently sensitive to intracellular variations of proteins distribution or are not compatible with live cell studies. Here, we outline the fluorescence lifetime imaging (FLIM) strategy for high resolution mapping of subcellular RI. We provide an example of our recent studies in which we utilize FLIM for measurements and monitoring of local RI in the major membraneless organelles within live cultured cells.3D print technology provided an opportunity to achieve fast and accurate fabrication of wearable sensor arrays. In this paper, high-sensitivity flexible and stretchable silver coated carbon nanotubes (Ag@CNT) wearable strain sensor arrays are fabricated using 3D printing technology and composite nanomaterial synthesis. Ag@CNTs with uniform and compact particles were synthesized with different sizes of carbon nanotubes (CNTs) via reduction method. Strain sensor arrays were fabricated accurately and efficiently with the aid of 3D printed molds. Sensors with different Ag@CNTs were then compared comprehensively and it was found that Ag@CNT (Short) sensor, which had a GF of 62.8 in 0 to 14.44% stretch range and a GF of 831.3 in 14.44% to 21.11% stretch range, can significantly enhanced the detection of small movements. These wearable strain sensor arrays were utilized in the application of TCM pulse diagnosis and gesture recognition. © 2020 IOP Publishing Ltd.While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic obstructive pulmonary disease (COPD), it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. Deep CNN transferred multiple instance learning (DCT-MIL) is proposed to identify COPD via CT images. After the lung is divided into 8 sections along the axial direction, one random axial CT image is taken out from each section as one instance. With one instance as the input, the activations of neural layers of AlexNet trained by natural images are extracted as features. After dimension reduction through principle component analysis (PCA), features of all instances are input into three MIL methods Citation k-Nearest-Neighbor (Citation-KNN), multiple instance support vector machine, and expectation-maximization diverse density. Moreover, the performance dependence of the resulted models on the depth of the neural layer where activations are extracted and the number of features is investigated. The proposed DCT-MIL achieves an exceptional performance with an accuracy of 99.29% and area under curve of 0.9826 while using 100 principle components of features extracted from the fourth convolutional layer and Citation-KNN. It outperforms not only DCT-MIL models using other settings and the pre-trained AlexNet with fine-tuning by montages of 8 lung CT images, but also other state-of-art methods. Deep CNN transferred multiple instance learning is suited for identification of COPD using CT images. It can help finding subgroups with high risk of COPD from large populations through CT scans ordered doing lung cancer screening. © 2020 Institute of Physics and Engineering in Medicine.Flexible porous carbon nanofibers containing nickel nanoparticles were synthesized by direct carbonization of electrospun Ni-MOFs/polyacrylonitrile fibers. The as-synthesized composites nanofibers were employed as binder-free electrodes, and exhibits high specific capacitance (up 672 F g-1 at current density of 2 A g-1) and superior rate capability (57% capacitance retention from current density of 2 to 10 A g-1), which may be attributed to its binder-free nature, unique one-dimensional (1-D) structure and highly dispersed of electrochemical active nickel nanoparticles. Furthermore, a symmetric supercapacitor was assembled by using the fiber electrodes in 6 M KOH, and the energy density of 17.8 Wh  kg-1 were achieved in a potential window of 1.5 V. This self-standing fiber with abundant mesopores and macropores is expected to become a promising electrode material of high-performance supercapacitors. © 2020 IOP Publishing Ltd.Respiratory-gated radiotherapy treatments of lung tumors reduce the irradiated normal tissue volume and potentially lower the risk of side effects. However, in clinical routine, the gating signal is usually derived from external markers or other surrogate signals and may not always correlate well with the actual tumor position. This study uses the kV-imaging system of a LINAC in combination with a multiple template matching algorithm for markerless real-time detection of the tumor position in a dynamic anthropomorphic lung phantom. The tumor was realized by a small container filled with polymer dosimetry gel, the so-called gel tumor. A full end-to-end test for a gated treatment was performed and the geometric and dosimetric accuracy was validated. The accuracy of the tumor detection algorithm in SI- direction was found to be (2.3±1.6) mm and the gel tumor was automatically detected in 98 out of 100 images. The measured 3D dose distribution showed a uniform coverage of the gel tumor and comparison with the treatment plan revealed a high 3D γ-passing rate of 86.7 % (3%/3mm). The simulated treatment confirmed the employed margin sizes for residual motion within the gating window and serves as an end-to-end test for a gated treatment based on a markerless fluoroscopic real-time tumor detection. © 2020 Institute of Physics and Engineering in Medicine.PURPOSE Recent developments in MR to synthetic CT (sCT) conversion have shown that treatment planning is possible without an initial planning CT. Promising conversion results have been demonstrated recently using conditional Generative Adversarial Networks (cGANs). FI-6934 However, the performance is generally only tested on images from one MR scanner, which neglects the potential of neural networks to find general high-level abstract features. In this study, we explored the generalizability of the generator models, trained on a single field strength scanner, to data acquired with higher field strengths. METHODS T2-weighted 0.35T MRIs and CTs from 51 patients treated for prostate (40) and cervical cancer (11) were included. 25 of them were used to train four different generators (SE-ResNet, DenseNet, U-Net, and Embedded Net). Further, an ensemble model was created from the four network outputs. The models were validated on 16 patients from a 0.35T MR scanner. Further, the trained models were tested on the Gold Atlas dataset, containing T2-weighted MR scans of different field strengths; 1,5T(7) and 3T(12), and 10 patients from the 0.
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