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We assessed risk factors for postoperative urinary retention (UR) in elderly males with femoral bone fractures: 169 Japanese males (mean age 81.95 ± 1.19 years) who had undergone hip surgery at a municipal hospital (Toyama, Japan). A multiple logistic regression analysis was used to test possible risk factors for UR: age, body mass index, serum albumin, cognitive impairment, activities of daily living (ADL), and history of diabetes mellitus (DM). UR occurred in 24 (14.2%) of the 169 patients. A multivariate logistic regression analysis with age adjustment showed that ADL (odds ratio [OR] 3.88; 95% confidence interval [CI]: 1.2-12.5, p=0.023) was significantly associated with the development of UR, and a history of DM showed marginal significance for UR occurrence (OR 0.36, 95%CI: 0.11-10, p=0.064). These results suggests that ADL is a risk factor for UR development in elderly males who have undergone surgery for femoral neck or trochanter fractures.

Object detection plays an important role in autonomous driving systems. LiDAR is widely used in autonomous driving vehicles and robots as a sensor for environmental perception. Recently, the development of computational power and deep learning technology makes it possible to classify and locate objects from LiDAR point cloud in a single end-to-end learnable network. However, objects are sparsely distributed in large point cloud field, and are always been partly scanned by LiDAR, which pose a challenge for accurate and rapid object positioning and classification from the raw point cloud. In this paper, we introduce a new single-shot refinement neural network for fast and accurate 3D object detection from the raw LiDAR point cloud. Firstly, we exploit self-attention mechanism in main object detection branch to enhance object feature representation. Secondly, we apply deformable convolution for learning adaptive receptive fields to fully capture the features of rotating and partially visible objects. Thirdly, an object refinement branch is introduced to produce a finer regression of objects upon the primary estimation from the main detection branch. All proposed modules have been proven to effectively improve the accuracy of object detection. Our method is evaluated on KITTI 3D detection benchmark and achieves state-of-the-art results while maintains real-time efficiency. Furthermore, real-time test in autonomous driving vehicle demonstrates that our method is robust to 16 channels LiDAR and can meet the demands of accuracy, efficiency, and visibility of object detection in various urban scenarios.

To evaluate hematological effects of direct current (DC) and alternating current (AC) extremely low frequency (ELF) electric field exposure, this study investigated red blood cell (RBC) movement in whole blood. Video images of RBCs were recorded under a microscope using specially designed electrode systems. Video analysis software was then used to measure the RBC velocity. The noise level and measurement system stability were confirmed based on results of a no-field exposure experiment. Using the electrode system to produce a non-homogeneous electric field, different movements were found to occur in DC and AC field exposure. The RBCs moved in the directions of the electric field and the gradient of field distribution, respectively, in the DC and AC fields. Dependences of the RBC velocity on the field strength were, respectively, linear and quadratic in the DC and AC fields. These results suggest that electrophoretic and dielectrophoretic movements were, respectively, dominant in the DC and AC fields. The magnitude of the electric field necessary to cause these effects was found to be 10

Research on the bioactive components of herbal medicines have been conducted mainly on the secondary metabolites of herbal plants. Accordingly, limited information is available on primary metabolites (carbohydrates, amino acids, lipids, and nucleic acids) and their biological effects. Here, we focused on the heat-resistant RNA of a decoction of Glycyrrhizae Radix and showed its immunostimulatory effects. The RNA activated NF-κB/AP-1 and induced TNF-α production in murine macrophages. Further analysis revealed that the RNA was around 90 nucleotides long. RNA sequencing (RNA-Seq) by next generation sequencing (NGS) showed that approximately 30% of the NGS reads were mapped to the genome of Glycyrrhiza uralensis, which is plant material of Glycyrrhizae Radix. Further analysis of the other 70% of reads indicated that the RNA contained RNA sequences that could be mapped to various microorganisms. Together, these results propose nucleic acids as a new research field in the bioactive components of herbal medicines.

Background: The main purpose of this study is to exhaustively explore risk factors, including age, gender, and several clinical indices, for mortality in elderly patients with femoral neck fracture and to evaluate some of them using survival analyses. Methods: This was a retrospective study tracking 1 year for vital prognosis. Data were collected at post-operation from medical records of the cases. Survival analysis was conducted to investigate the risk factors for death, including albumin, urinary retention, activity of daily living (ADL), and cognitive disorder. Results: We recruited 318 patients with a history of hip surgery carried out at Toyama Municipal Hospital, in which 39 patients died for 1 year after discharge. The results showed a significant decrease in survival rate in low albumin, positive urinary retention, and low ADL (P < 0.01, by log-rank test). The hazard ratios (95% confidence interval) of albumin, urinary retention, ADL, and cognitive disorder were 0.36 (0.19 - 0.69), 0.4 (0.2 - 0.8), 0.29 (0.15 - 0.58) and 0.65 (0.32 - 1.29), respectively. Conclusions: This study demonstrated that albumin, urinary retention and ADL were the important risk factors for mortality, and suggested that the postoperative management of albumin, urinary retention and ADL is important, especially in elderly female patients receiving surgery of femoral neck and trochanteric fractures. J Clin Med Res. 2020;12(10):668-673 doi: https://doi.org/10.14740/jocmr4292

Herbal medicine is mainly prepared from boiling herbal water extracts. Many epoch-making immunosuppressant drugs, such as glycyrrhizic acid (old example) and FTY720 (current example), were developed from herbal secondary metabolites in the boiling water extract by partition with organic solvents. However, few immunostimulants have been discovered by this method. Instead of the usual method, we aimed to find a novel immunostimulant component by two unique methods in the research of herbal medicine: ultracentrifugation and electron microscopy. The immunostimulant was not a secondary metabolite, as expected, but the structure was a nanoparticle formed by a polysaccharide. In addition, we clarified the immune effect of the nanoparticle. Intake of the nanoparticle by phagocytosis resulted in immunostimulant effects by increasing the genes and proteins of inflammatory cytokines in macrophage cells. The immunostimulant effects were inhibited by a phagocytosis inhibitor, cytochalasin D. To the best of our knowledge, this study is the first to describe the discovery of a nanoparticle in boiling herbal water extracts and its immunostimulant properties. This study will provide additional understanding of the efficacy of herbal medicine, in that the immunostimulant nanoparticle universally exists in boiling herbal water extracts. Thus, traditional herbal medicine may be an oldest known nanomedicine. Furthermore, this study suggests that the immunostimulant nanoparticle simply can be obtained from herbal medicine only by ultracentrifugation. We hope that this simple strategy will substantially contribute to drug development, including vaccine adjuvant, in the future., Highlights • Nanoparticles are universally present in boiling herbal water extracts. • Nanoparticles are obtained from these extracts by ultracentrifugation. • Nanoparticles are mainly composed of polysaccharides. • Nanoparticles have an immunostimulatory effect on macrophage cells. • This effect is exhibited by the phagocytosis of exogenous nanoparticles.

Due to the increasing incidence of metabolic syndrome, the development of new therapeutic strategies is urgently required. One promising approach is to focus on the predisease state (so-called Mibyou in traditional Japanese medicine) before metabolic syndrome as a preemptive medical target. We recently succeeded in detecting a predisease state before metabolic syndrome using a mathematical theory called the dynamical network biomarker (DNB) theory. The detected predisease state was characterized by 147 DNB genes among a total of 24,217 genes in TSOD (Tsumura-Suzuki Obese Diabetes) mice, a well-accepted model of metabolic syndrome, at 5 weeks of age. The timing of the predisease state was much earlier than the onset of metabolic syndrome in TSOD mice reported to be at approximately 8–12 weeks of age. In the present study, we investigated whether the predisease state in TSOD mice can be inhibited by the oral administration of a Kampo formula, bofutsushosan (BTS), which is usually used to treat obese patients with metabolic syndrome in Japan, from 3 to 7 weeks of age. We found the comprehensive suppression of the early warning signals of the DNB genes by BTS at 5 weeks of age and later. Specifically, the standard deviations of 134 genes among the 147 DNB genes decreased at 5 weeks of age as compared to the nontreatment control group, and 80 of them showed more than 50% reduction. In addition, at 7 weeks of age, the body weight and blood glucose level were significantly lower in the BTS-treated group than in the nontreatment control group. The results of our study suggest a novel mechanism of BTS; it suppressed fluctuations of the DNB genes at the predisease state before metabolic syndrome and thus prevented the subsequent transition to metabolic syndrome. In conclusion, this study demonstrated the preventive and preemptive effects of a Kampo formula on Mibyou before metabolic syndrome for the first time based on scientific evaluation.

Using near-infrared (NIR) light with 700–1200 nm wavelength, transillumination images of small animals and thin parts of a human body such as a hand or foot can be obtained. They are two-dimensional (2D) images of internal absorbing structures in a turbid medium. A three-dimensional (3D) see-through image is obtainable if one can identify the depth of each part of the structure in the 2D image. Nevertheless, the obtained transillumination images are blurred severely because of the strong scattering in the turbid medium. Moreover, ascertaining the structure depth from a 2D transillumination image is difficult. To overcome these shortcomings, we have developed a new technique using deep learning principles. A fully convolutional network (FCN) was trained with 5,000 training pairs of clear and blurred images. Also, a convolutional neural network (CNN) was trained with 42,000 training pairs of blurred images and corresponding depths in a turbid medium. Numerous training images were provided by the convolution with a point spread function derived from diffusion approximation to the radiative transport equation. The validity of the proposed technique was confirmed through simulation. Experiments demonstrated its applicability. This technique can provide a new tool for the NIR imaging of animal bodies and biometric authentication of a human body.