Notes

[Beckwith-Wiedemann over-growth syndrom].
A literature search found 10 similar cases and further biomechanical evidence that these fractures can occur from accidental falls.

While AHT should be kept in the differential diagnosis whenever BPSFs are seen, these injuries can occur as a result of accidental falls.
While AHT should be kept in the differential diagnosis whenever BPSFs are seen, these injuries can occur as a result of accidental falls.Acquired thrombocytopenic thrombotic purpura (aTTP) is an autoantibody-mediated disease against the enzyme A Disintegrin and Metalloprotease domain with ThromboSpondin-1 type motif 13, which until now has been treated with plasma exchange (PEX) and corticosteroids. A 29-year-old female patient, who presented with aTTP in the context of pregnancy, has developed multiple relapses after treatment with PEX, corticosteroids, and rituximab. Recently, caplacizumab, a nanobody against von Willebrand factor, has been approved for the treatment of aTTP. In our patient, caplacizumab achieved better disease control, with a lower platelet count restoration time, days of PEX and hospitalization duration, as compared to standard therapy, reproducing the results of clinical trials. Caplacizumab represents a significant advance in the treatment of aTTP, especially in cases of recurrent relapses.Extensive molecular dynamics simulations, using simple charged polymer models, have been employed to probe the collapse kinetics of a single flexible polyelectrolyte (PE) chain under implicit poor solvent conditions. We investigate the role of the charged nature of PE chain (A), valency of counterions (Z) on the kinetics of such PE collapse. Our study shows that the collapse kinetics of charged polymers are significantly different from those of the neutral polymer and that the finite-size scaling behaviour of PE collapse times does not follow the Rouse scaling as observed in the case neutral polymers and the critical exponent in the case of charged PE chains is less than that of neutral polymers and also exhibits dependence on counterion valency. The coarsening of clusters along the PE chain collapse occurs in multi-stages and exhibits contrasting exponents to that of neutral polymers faster in the early stages and slower in the later stages of collapse.Purpose. To overcome the insufficiency of conventional photodynamic therapy (PDT) for treating metastatic melanoma, the combination of smart nanoparticles and PDT with immunotherapy was used to achieve a higher efficiency by accumulating more photosensitizers in tumor areas and triggering stronger immune responses against tumors after PDT.Methods. In this study, we designed a nanoliposome co-encapsulation of chlorin E6 (Ce6) and SB-3CT to realize significant antitumoral proliferation and metastasis efficacy after laser irradiation in A375 cells. The morphology, size distribution, and loading efficiency of Ce6-SB3CT@Liposome (Lip-SC) were characterized. The reactive oxygen species (ROS) generation and cytotoxicity were evaluated in A375 cells, and the mechanisms of natural killer (NK) cell-mediated killing were assessed.Results. Lip-SC showed good stability and was well-dispersed with a diameter of approximately 140 nm in phosphate-buffered saline. The nanoliposomes could accumulate in tumor areas and induce apoptosis in cancer cells upon 660 nm light irradiation, which could trigger an immune response and induce the expression of NK group 2 member D (NKG2D) ligands. The subsequently released SB-3CT could further activate NK cells effectively and strengthen the immune system by inhibiting the shedding of soluble NKG2D ligands.Discussion. Taken together, the synergistic effects of SB-3CT on nanoliposomes for Ce6-mediated PDT were analyzed in detail to provide a new platform for future anti-melanoma treatment.Spin-orbit effects in heavy 5dtransition metal oxides, in particular, iridates, have received enormous current interest due to the prediction as well as the realization of a plethora of exotic and unconventional magnetic properties. While a bulk of these works are based on tetravalent iridates (d5), where the counter-intuitive insulating state of the rather extended 5dorbitals are explained by invoking strong spin-orbit coupling, the recent quest in iridate research has shifted to the other valencies of Ir, of which pentavalent iridates constitute a notable representative. In contrast to the tetravalent iridates, spin-orbit entangled electrons ind4systems are expected to be confined to theJ= 0 singlet state without any resultant moment or magnetic response. However, it has been recently predicted that, magnetism ind4systems may occur via magnetic condensation of excitations across spin-orbit-coupled states. In reality, the magnetism in Ir5+systems are often quite debatable both from theoretical as well as experimental point of view. Here we provide a comprehensive overview of the spin-orbit coupledd4model systems and its implications in the studied pentavalent iridates. In particular, we review here the current experimental and theoretical understanding of the double perovskite (A2BYIrO6,A= Sr, Ba,B= Y, Sc, Gd), 6H-perovskite (Ba3MIr2O9,M= Zn, Mg, Sr, Ca), post-perovskite (NaIrO3), and hexagonal (Sr3MIrO6) iridates, along with a number of open questions that require future investigation.Volumetric modulated arc therapy planning is a challenging problem in high-dimensional, non-convex optimization. Traditionally, heuristics such as fluence-map-optimization-informed segment initialization use locally optimal solutions to begin the search of the full arc therapy plan space from a reasonable starting point. These routines facilitate arc therapy optimization such that clinically satisfactory radiation treatment plans can be created in about 10 minutes. However, current optimization algorithms favor solutions near their initialization point and are slower than necessary due to plan overparameterization. In this work, arc therapy overparameterization is addressed by reducing the effective dimension of treatment plans with unsupervised deep learning. An optimization engine is then built based on low-dimensional arc representations which facilitates faster planning times.Quantifying parenchymal tissue changes in the lungs is imperative in furthering the study of radiation induced lung damage (RILD). Registering lung images from different time-points is a key step of this process. Traditional intensity-based registration approaches fail this task due to the considerable anatomical changes that occur between timepoints. This work proposes a novel method to successfully register longitudinal pre- and post-radiotherapy (RT) lung computed tomography (CT) scans that exhibit large changes due to RILD, by extracting consistent anatomical features from CT (lung boundaries, main airways, vessels) and using these features to optimise the registrations. Pre-RT and 12 month post-RT CT pairs from fifteen lung cancer patients were used for this study, all with varying degrees of RILD, ranging from mild parenchymal change to extensive consolidation and collapse. For each CT, signed distance transforms from segmentations of the lungs and main airways were generated, and the Frangi vesselness of large anatomical changes such as consolidation and atelectasis, outperforming the traditional registration approach both quantitatively and through thorough visual inspection.We introduce a method of exploring potential energy contours (PECs) in complex dynamical systems based on potentiostatic kinematics wherein the systems are evolved with minimal changes to their potential energy. We construct a simple iterative algorithm for performing potentiostatic kinematics, which uses an estimate curvature to predict new configuration-space coordinates on the PEC and a potentiostat term component to correct for errors in prediction. NMS-873 Our methods are then applied to atomic structure models using an interatomic potential for energy and force evaluations as would commonly be invoked in a molecular dynamics simulation. Using several model systems, we assess the stability and accuracy of the method on different hyperparameters in the implementation of the potentiostatic kinematics. Our implementation is open source and available within the atomic simulation environment package.Objective.This paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque.Approach.The regression models, collectively known as MuscleNET, take one of four forms ANN (forward artificial neural network), RNN (recurrent neural network), CNN (convolutional neural network), and RCNN (recurrent convolutional neural network). Inspired by conventional biomechanical muscle models, delayed kinematic signals were used along with sEMG signals as the machine learning model's input; specifically, the CNN and RCNN were modeled with novel configurations for these input conditions. The models' inputs contain either raw or filtered sEMG signals, which allowed evaluation of the filtering capabilities of the models. The models were trained using human experimental data and evaluated with different individual data.Main results.Results were compared in terms of regression error (using the root-mean-square) and model computation delay. The results indicate that the RNN (with filtered sEMG signals) and RCNN (with raw sEMG signals) models, both with delayed kinematic data, can extract underlying motor control information (such as joint activation torque or joint angle) from sEMG signals in pick-and-place tasks. The CNNs and RCNNs were able to filter raw sEMG signals.Significance.All forms of MuscleNET were found to map sEMG signals within 2 ms, fast enough for real-time applications such as the control of exoskeletons or active prostheses. The RNN model with filtered sEMG and delayed kinematic signals is particularly appropriate for applications in musculoskeletal simulation and biomechatronic device control.This article will review quantum particle creation in expanding universes. The emphasis will be on the basic physical principles and on selected applications to cosmological models. The needed formalism of quantum field theory in curved spacetime will be summarized, and applied to the example of scalar particle creation in a spatially flat universe. Estimates for the creation rate will be given and applied to inflationary cosmology models. Analog models which illustrate the same physical principles and may be experimentally realizable are also discussed.High surface area nickel oxide nanowires (NiO NWs), Fe-doped NiO NWs andα-Fe2O3/Fe-doped NiO NWs were synthesized with nanocasting pathway, and then the morphology, microstructure and components of all samples were characterized with XRD, TEM, EDS, UV-vis spectra and nitrogen adsorption-desorption isotherms. Owing to the uniform mesoporous template, all samples with the same diameter exhibit the similar mesoporous-structures. The loadedα-Fe2O3nanoparticles should exist in mesoporous channels between Fe-doped NiO NWs to form heterogeneous contact at the interface of n-typeα-Fe2O3nanoparticles and p-type NiO NWs. The gas-sensing results indicate that Fe-dopant andα-Fe2O3-loading both improve the gas-sensing performance of NiO NWs sensors.α-Fe2O3/Fe-doped NiO NWs sensors presented the highest response to 100 ppm ethanol gas (55.264) compared with Fe-doped NiO NWs (24.617) and NiO NWs sensors (3.189). The donor Fe-dopant increases the ground state resistance and the absorbed oxygen content in air.α-Fe2O3nanoparticles in electron depletion region result in the increasing resistance in ethanol gas and decreasing resistance in air.
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