Evaluation of POSSUM scoring system in the treatment of osteoporotic fracture of the hip in elder patients

ObjectiveTo evaluate the applicability of the modified physiological and operative severity score for enumeration of mortality and morbidity (POSSUM) scoring system in predicting mortality in the patients undergoing hip joint arthroplasty.MethodsA total of 295 patients with hip fractures were analyzed using the modified POSSUM surgical scoring system. The mean ages of the patients were 66.59 years in the complicative group, 62.28 years in noncomplicative group, 77.89 years in the death group and 63.25 years in the living group, respectively. The comparisons between the observed and predicted morbidity, between the observed and predicted mortality were made within 30 days after operation.ResultsThe average physiological scores and operative severity scores was 18.96 ± 4.83 and 13.47 ± 2.01 in complicative group, while 15.65 ± 3.66 and 11.74 ± 2.26 in noncomplicative group (P<0.05). The average physiological scores and operative severity scores was 25.56 ± 3.78 and 14.22 ± 0.67 in death group, while 16.46 ± 4.09 and 12.25 ± 2.33 in living group (P<0.05). Though POSSUM scoring system over-predicted the overall risk of death, its estimate was very close in the high risk groups (>10%). There was perfect consistence between the observed and the predicted morbidity as calculated by published predictor equation for morbidity, and consistence for mortality in the high risk band.ConclusionsModified POSSUM scoring system may be used to predict the morbidity in patients with hip fracture. Furthermore, POSSUM scoring system overpredicts the overall risk of death, but its estimate is close to the actual data in the high risk band (>10%)

Multiperson Detection and Vital-Sign Sensing Empowered by Space-Time-Coding RISs

Passive human sensing using wireless signals has attracted increasing attention due to its superiorities of non-contact and robustness in various lighting conditions. However, when multiple human individuals are present, their reflected signals could be intertwined in the time, frequency and spatial domains, making it challenging to separate them. To address this issue, this paper proposes a novel system for multiperson detection and monitoring of vital signs (i.e., respiration and heartbeat) with the assistance of space-time-coding (STC) reconfigurable intelligent metasurfaces (RISs). Specifically, the proposed system scans the area of interest (AoI) for human detection by using the harmonic beams generated by the STC RIS. Simultaneously, frequencyorthogonal beams are assigned to each detected person for accurate estimation of their respiration rate (RR) and heartbeat rate (HR). Furthermore, to efficiently extract the respiration signal and the much weaker heartbeat signal, we propose an improved variational mode decomposition (VMD) algorithm to accurately decompose the complex reflected signals into a smaller number of intrinsic mode functions (IMFs). We build a prototype to validate the proposed multiperson detection and vital-sign monitoring system. Experimental results demonstrate that the proposed system can simultaneously monitor the vital signs of up to four persons. The errors of RR and HR estimation using the improved VMD algorithm are below 1 RPM (respiration per minute) and 5 BPM (beats per minute), respectively. Further analysis reveals that the flexible beam controlling mechanism empowered by the STC RIS can reduce the noise reflected from other irrelative objects on the physical layer, and improve the signal-to-noise ratio of echoes from the human chest

Surgical management of 142 cases of split cord malformations associated with osseous divide

Objectives To investigate the key surgical points in treating split cord malformations associated with osseous divide and scoliosis (SCM-OD-S). Materials and methods The surgical options and methods of a total of 142 SCM-OD-S cases were retrospectively analyzed, and the surgical precautions and imaging diagnosis were also discussed. Results The 142 patients were performed osseous divide resection plus dural sac molding, which achieved good results and no serious complication such as spinal cord and nerve injury occurred; certain symptoms such as urination-defecation disorders, muscle strength subsidence, Pes Cavus, and toe movement disorder in partial patients achieved various degrees of relief, and it also created good conditions for next-step treatment against scoliosis. Conclusions The diagnosis of SCM-OD mainly depended on imaging inspection, routine magnetic resonance imaging (MRI) combined with computed tomography (CT) 3D reconstruction, which can comprehensively evaluate the types and features of diastematomyelia as well as other concomitant diseases. SCM alone needed no treatment, but surgery will be the only means of treating SCM-OD. Intraoperatively removing osseous divide step-by-step, as well as carefully freeing the spinal cord and remodeling the dural sac, can lay good foundations for relieving tethered cord, improving neurological symptoms, and further scoliosis orthomorphia, thus particularly exhibiting importance for the growth and development of adolescents

Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges

Reconfigurable intelligent surfaces (RISs) have flexible and exceptional performance in manipulating electromagnetic waves and customizing wireless channels. These capabilities enable them to provide a plethora of valuable activity-related information for promoting wireless human sensing. In this article, we present a comprehensive review of passive human sensing using radio frequency signals with the assistance of RISs. Specifically, we first introduce fundamental principles and physical platform of RISs. Subsequently, based on the specific applications, we categorize the state-of-the-art human sensing techniques into three types, including human imaging,localization, and activity recognition. Meanwhile, we would also investigate the benefits that RISs bring to these applications. Furthermore, we explore the application of RISs in human micro-motion sensing, and propose a vital signs monitoring system enhanced by RISs. Experimental results are presented to demonstrate the promising potential of RISs in sensing vital signs for manipulating individuals. Finally, we discuss the technical challenges and opportunities in this field

Single domain antibody multimers confer protection against rabies infection

Post-exposure prophylactic (PEP) neutralizing antibodies against Rabies are the most effective way to prevent infection-related fatality. The outer envelope glycoprotein of the Rabies virus (RABV) is the most significant surface antigen for generating virus-neutralizing antibodies. The small size and uncompromised functional specificity of single domain antibodies (sdAbs) can be exploited in the fields of experimental therapeutic applications for infectious diseases through formatting flexibilities to increase their avidity towards target antigens. In this study, we used phage display technique to select and identify sdAbs that were specific for the RABV glycoprotein from a naïve llama-derived antibody library. To increase their neutralizing potencies, the sdAbs were fused with a coiled-coil peptide derived from the human cartilage oligomeric matrix protein (COMP48) to form homogenous pentavalent multimers, known as combodies. Compared to monovalent sdAbs, the combodies, namely 26424 and 26434, exhibited high avidity and were able to neutralize 85-fold higher input of RABV (CVS-11 strain) pseudotypes in vitro, as a result of multimerization, while retaining their specificities for target antigen. 26424 and 26434 were capable of neutralizing CVS-11 pseudotypes in vitro by 90–95% as compared to human rabies immunoglobulin (HRIG), currently used for PEP in Rabies. The multimeric sdAbs were also demonstrated to be partially protective for mice that were infected with lethal doses of rabies virus in vivo. The results demonstrate that the combodies could be valuable tools in understanding viral mechanisms, diagnosis and possible anti-viral candidate for RABV infection

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

The JCMT BISTRO Survey: Studying the Complex Magnetic Field of L43

We present observations of polarized dust emission at 850 μm from the L43 molecular cloud, which sits in the Ophiuchus cloud complex. The data were taken using SCUBA-2/POL-2 on the James Clerk Maxwell Telescope as a part of the BISTRO large program. L43 is a dense (NH 10 22 2 ~ –1023 cm−2) complex molecular cloud with a submillimeter-bright starless core and two protostellar sources. There appears to be an evolutionary gradient along the isolated filament that L43 is embedded within, with the most evolved source closest to the Sco OB2 association. One of the protostars drives a CO outflow that has created a cavity to the southeast. We see a magnetic field that appears to be aligned with the cavity walls of the outflow, suggesting interaction with the outflow. We also find a magnetic field strength of up to ∼160 ± 30 μG in the main starless core and up to ∼90 ± 40 μG in the more diffuse, extended region. These field strengths give magnetically super- and subcritical values, respectively, and both are found to be roughly trans-Alfvénic. We also present a new method of data reduction for these denser but fainter objects like starless cores

Filamentary Network and Magnetic Field Structures Revealed with BISTRO in the High-Mass Star-Forming Region NGC2264 : Global Properties and Local Magnetogravitational Configurations

We report 850 $\mu$m continuum polarization observations toward the filamentary high-mass star-forming region NGC 2264, taken as part of the B-fields In STar forming Regions Observations (BISTRO) large program on the James Clerk Maxwell Telescope (JCMT). These data reveal a well-structured non-uniform magnetic field in the NGC 2264C and 2264D regions with a prevailing orientation around 30 deg from north to east. Field strengths estimates and a virial analysis for the major clumps indicate that NGC 2264C is globally dominated by gravity while in 2264D magnetic, gravitational, and kinetic energies are roughly balanced. We present an analysis scheme that utilizes the locally resolved magnetic field structures, together with the locally measured gravitational vector field and the extracted filamentary network. From this, we infer statistical trends showing that this network consists of two main groups of filaments oriented approximately perpendicular to one another. Additionally, gravity shows one dominating converging direction that is roughly perpendicular to one of the filament orientations, which is suggestive of mass accretion along this direction. Beyond these statistical trends, we identify two types of filaments. The type-I filament is perpendicular to the magnetic field with local gravity transitioning from parallel to perpendicular to the magnetic field from the outside to the filament ridge. The type-II filament is parallel to the magnetic field and local gravity. We interpret these two types of filaments as originating from the competition between radial collapsing, driven by filament self-gravity, and the longitudinal collapsing, driven by the region's global gravity.Comment: Accepted for publication in the Astrophysical Journal. 43 pages, 32 figures, and 4 tables (including Appendix

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages