Finite element analysis of porously punched prosthetic short stem virtually designed for simulative uncemented hip arthroplasty

Background: There is no universal hip implant suitably fills all femoral types, whether prostheses of porous short-stem suitable for Hip Arthroplasty is to be measured scientifically. Methods: Ten specimens of femurs scanned by CT were input onto Mimics to rebuild 3D models; their *stl format dataset were imported into Geomagic-Studio for simulative osteotomy; the generated *.igs dataset were interacted by UG to fit solid models; the prosthesis were obtained by the same way from patients, and bored by punching bears designed by Pro-E virtually; cements between femora and prosthesis were extracted by deleting prosthesis; in HyperMesh, all compartments were assembled onto four artificial joint style as: (a) cemented long-stem prosthesis; (b) porous long-stem prosthesis; (c) cemented short-stem prosthesis; (d) porous short-stem prosthesis. Then, these numerical models of Finite Element Analysis were exported to AnSys for numerical solution. Results: Observed whatever from femur or prosthesis or combinational femora-prostheses, “Kruskal-Wallis” value p > 0.05 demonstrates that displacement of (d) ≈ (a) ≈ (b) ≈ (c) shows nothing different significantly by comparison with 600 N load. If stresses are tested upon prosthesis, (d) ≈ (a) ≈ (b) ≈ (c) is also displayed; if upon femora, (d) ≈ (a) ≈ (b) < (c) is suggested; if upon integral joint, (d) ≈ (a) < (b) < (c) is presented. Conclusions: Mechanically, these four sorts of artificial joint replacement are stabilized in quantity. Cemented short-stem prostheses present the biggest stress, while porous short-stem & cemented long-stem designs are equivalently better than porous long-stem prostheses and alternatives for femoral-head replacement. The preferred design of those two depends on clinical conditions. The cemented long-stem is favorable for inactive elders with osteoporosis, and porously punched cementless short-stem design is suitable for patients with osteoporosis, while the porously punched cementless short-stem is favorable for those with a cement allergy. Clinically, the strength of this study is to enable preoperative strategy to provide acute correction and decrease procedure time

Kidney Modelling for FDG Excretion with PET

The purpose of this study was to detect the physiological process of FDG's filtration from blood to urine and to establish a mathematical model to describe the process. Dynamic positron emission tomography scan for FDG was performed on seven normal volunteers. The filtration process in kidney can be seen in the sequential images of each study. Variational distribution of FDG in kidney can be detected in dynamic data. According to the structure and function, kidney is divided into parenchyma and pelvis. A unidirectional three-compartment model is proposed to describe the renal function in FDG excretion. The time-activity curves that were picked up from the parenchyma, pelvis, and abdominal aorta were used to estimate the parameter of the model. The output of the model has fitted well with the original curve from dynamic data

Na+-induced Ca2+ influx through reverse mode of Na+-Ca2+ exchanger in mouse ventricular cardiomyocyte

BACKGROUND: Dobutamine is commonly used for clinical management of heart failure and its pharmacological effects have long been investigated as inotropics via β-receptor activation. However, there is no electrophysiological evidence if dobutamine contributes inotropic action due at least partially to the reverse mode of Na+-Ca2+ exchanger (NCX) activation. METHODS: Action potential (AP), voltage-gated Na+ (INa), Ca2+ (ICa), and K+ (Ito and IK1) currents were observed using whole-cell patch technique before and after dobutamine in ventricular cardiomyocytes isolated from adult mouse hearts. Another sets of observation were also performed with Kb-r7943 or in the solution without [Ca2+]o. RESULTS: Dobutamine (0.1-1.0 μM) significantly enhanced the AP depolarization with prolongation of AP duration (APD) in a concentration-dependent fashion. The density of INa was also increased concentration-dependently without alternation of voltage-dependent steady-status of activation and inactivation, reactivation as well. Whereas, the activities for ICa, Ito, and IK1 were not changed by dobutamine. Intriguingly, the dobutamine-mediated changes in AP repolarization were abolished by 3 μM Kb-r7943 pretreatment or by simply removing [Ca2+]o without affecting accelerated depolarization. Additionally, the ratio of APD50/APD90 was not significantly altered in the presence of dobutamine, implying that effective refractory period was remain unchanged. CONCLUSIONS: This novel finding provides evidence that dobutamine upregulates of voltage-gated Na+ channel function and Na+ influx-induced activation of the reverse mode of NCX, suggesting that dobutamine may not only accelerate ventricular contraction via fast depolarization but also cause Ca2+ influx, which contributes its positive inotropic effect synergistically with β-receptor activation without increasing the arrhythmogenetic risk

Progressive Object Transfer Detection

Recent development of object detection mainly depends on deep learning with large-scale benchmarks. However, collecting such fully-annotated data is often difficult or expensive for real-world applications, which restricts the power of deep neural networks in practice. Alternatively, humans can detect new objects with little annotation burden, since humans often use the prior knowledge to identify new objects with few elaborately-annotated examples, and subsequently generalize this capacity by exploiting objects from wild images. Inspired by this procedure of learning to detect, we propose a novel Progressive Object Transfer Detection (POTD) framework. Specifically, we make three main contributions in this paper. First, POTD can leverage various object supervision of different domains effectively into a progressive detection procedure. Via such human-like learning, one can boost a target detection task with few annotations. Second, POTD consists of two delicate transfer stages, i.e., Low-Shot Transfer Detection (LSTD), and Weakly-Supervised Transfer Detection (WSTD). In LSTD, we distill the implicit object knowledge of source detector to enhance target detector with few annotations. It can effectively warm up WSTD later on. In WSTD, we design a recurrent object labelling mechanism for learning to annotate weakly-labeled images. More importantly, we exploit the reliable object supervision from LSTD, which can further enhance the robustness of target detector in the WSTD stage. Finally, we perform extensive experiments on a number of challenging detection benchmarks with different settings. The results demonstrate that, our POTD outperforms the recent state-of-the-art approaches.Comment: TIP 201

0^-+ Trigluon Glueball and its Implication for a Recent BES Observation

We calculate the mass of $0^{-+}$ triple-valence-gluon resonance, the trigluon glueball, with QCD sum rules. Its mass is found to be approximately in the region between 1.9 GeV and 2.7 GeV with some theoretical uncertainties. Moreover, it is likely that the new BES measurement of the $p \bar{p}$ enhancement near threshold in the $J/\psi$ decays exhibits the behavior of this trigluon state. Our analyzes favor the baryonium-gluonium mixing picture for the BES observation.Comment: 14 text pages; 2 eps-form figures.To appear in Phys.Lett.

Pro-inflammatory miR-223 mediates the cross-talk between the IL23 pathway and the intestinal barrier in inflammatory bowel disease

Genes that showed greater than four-fold upregulation in IBD. (XLSX 28 kb

UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase

Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a natural complement to these point cloud views and fully utilizing the comprehensive information of them benefits more robust perceptions. In this paper, we present a unified multi-modal LiDAR segmentation network, termed UniSeg, which leverages the information of RGB images and three views of the point cloud, and accomplishes semantic segmentation and panoptic segmentation simultaneously. Specifically, we first design the Learnable cross-Modal Association (LMA) module to automatically fuse voxel-view and range-view features with image features, which fully utilize the rich semantic information of images and are robust to calibration errors. Then, the enhanced voxel-view and range-view features are transformed to the point space,where three views of point cloud features are further fused adaptively by the Learnable cross-View Association module (LVA). Notably, UniSeg achieves promising results in three public benchmarks, i.e., SemanticKITTI, nuScenes, and Waymo Open Dataset (WOD); it ranks 1st on two challenges of two benchmarks, including the LiDAR semantic segmentation challenge of nuScenes and panoptic segmentation challenges of SemanticKITTI. Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase. It contains most of the popular outdoor LiDAR segmentation algorithms and provides reproducible implementations. The OpenPCSeg codebase will be made publicly available at https://github.com/PJLab-ADG/PCSeg.Comment: ICCV 2023; 21 pages; 9 figures; 18 tables; Code at https://github.com/PJLab-ADG/PCSe