Development and Validation of a Predictive Model for the Prognosis of Complications of Supracondylar Fractures of The Humerus in Children

Objective: Informing patient consultations and healthcare choices, clinical predictive models can offer patients tailored projections of the outcome. The most frequent elbow fractures in children are supracondylar humerus fractures, and clinical prediction models were still largely underutilized in these cases. By developing and verifying a prediction model to lower the risk of postoperative problems in children with supracondylar humerus fractures, this research sought to evaluate independent risk variables connected with the incidence of complications of supracondylar humerus fractures in children. Methods: We retrospectively studied 411 children with supracondylar humerus fractures treated surgically at our hospital from 2015 to 2019, and explored the independent risk factors affecting the prognosis of supracondylar humerus fractures in children in the study group using univariate and multifactorial Cox regression analysis, respectively. In addition, a prediction model based on the independent factors was constructed, a nomogram was made and data from the two cohorts were used to verify the feasibility and reliability of the model and visualize the data. Results: Height, older than eight years, weight, nerve damage, fracture type and with joystick technology of the child as independent risk factors influenced the prognosis of pediatric supracondylar humerus fractures in the modeling constructed by the training cohort, respectively. The results of the validation cohort were further screened for older than eight years, nerve injury and fracture type as independent prognostic factors. Conclusions: We were able to construct a predictive model based on a large genuine data sample, and clinical characteristics in this model could be used as independent predictors for reducing the occurrence of postoperative complications in supracondylar fractures. Combining basic vital signs and clinical risk factors into a simple and clear nomogram was more likely to result in the best treatment plan

The RNA Architecture of the SARS-CoV-2 3′-Untranslated Region

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the current COVID-19 pandemic. The 3′ untranslated region (UTR) of this β-CoV contains essential cis-acting RNA elements for the viral genome transcription and replication. These elements include an equilibrium between an extended bulged stem-loop (BSL) and a pseudoknot. The existence of such an equilibrium is supported by reverse genetic studies and phylogenetic covariation analysis and is further proposed as a molecular switch essential for the control of the viral RNA polymerase binding. Here, we report the SARS-CoV-2 3′ UTR structures in cells that transcribe the viral UTRs harbored in a minigene plasmid and isolated infectious virions using a chemical probing technique, namely dimethyl sulfate (DMS)-mutational profiling with sequencing (MaPseq). Interestingly, the putative pseudoknotted conformation was not observed, indicating that its abundance in our systems is low in the absence of the viral nonstructural proteins (nsps). Similarly, our results also suggest that another functional cis-acting element, the three-helix junction, cannot stably form. The overall architectures of the viral 3′ UTRs in the infectious virions and the minigene-transfected cells are almost identical

RNA-Targeting Splicing Modifiers: Drug Development and Screening Assays

RNA splicing is an essential step in producing mature messenger RNA (mRNA) and other RNA species. Harnessing RNA splicing modifiers as a new pharmacological modality is promising for the treatment of diseases caused by aberrant splicing. This drug modality can be used for infectious diseases by disrupting the splicing of essential pathogenic genes. Several antisense oligonucleotide splicing modifiers were approved by the U.S. Food and Drug Administration (FDA) for the treatment of spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD). Recently, a small-molecule splicing modifier, risdiplam, was also approved for the treatment of SMA, highlighting small molecules as important warheads in the arsenal for regulating RNA splicing. The cellular targets of these approved drugs are all mRNA precursors (pre-mRNAs) in human cells. The development of novel RNA-targeting splicing modifiers can not only expand the scope of drug targets to include many previously considered “undruggable” genes but also enrich the chemical-genetic toolbox for basic biomedical research. In this review, we summarized known splicing modifiers, screening methods for novel splicing modifiers, and the chemical space occupied by the small-molecule splicing modifiers

Application of Kalman Filter in Track Prediction of Shuttlecock

Abstract -This paper deals with the application of Kalman filter for optimizing and filtering the position signal of shuttlecock obtained by the vision servo system of 'Shuttlecock Robot' The Kalman filter algorithm is used to filter the shuttlecock position signal by taking the error of measurement and the error of shuttlecock motion model into account. Besides, by considering the requirement of fast moving control, we reduce dimensions of state vector by decomposition of shuttlecock motion to shorten the executive cycle. The simulation results show its affectivity on improving the accuracy of track prediction. It can also accomplish track prediction fast and accurately when applied on 'Shuttlecock Robot'

One at A Time: Multi-step Volumetric Probability Distribution Diffusion for Depth Estimation

Recent works have explored the fundamental role of depth estimation in multi-view stereo (MVS) and semantic scene completion (SSC). They generally construct 3D cost volumes to explore geometric correspondence in depth, and estimate such volumes in a single step relying directly on the ground truth approximation. However, such problem cannot be thoroughly handled in one step due to complex empirical distributions, especially in challenging regions like occlusions, reflections, etc. In this paper, we formulate the depth estimation task as a multi-step distribution approximation process, and introduce a new paradigm of modeling the Volumetric Probability Distribution progressively (step-by-step) following a Markov chain with Diffusion models (VPDD). Specifically, to constrain the multi-step generation of volume in VPDD, we construct a meta volume guidance and a confidence-aware contextual guidance as conditional geometry priors to facilitate the distribution approximation. For the sampling process, we further investigate an online filtering strategy to maintain consistency in volume representations for stable training. Experiments demonstrate that our plug-and-play VPDD outperforms the state-of-the-arts for tasks of MVS and SSC, and can also be easily extended to different baselines to get improvement. It is worth mentioning that we are the first camera-based work that surpasses LiDAR-based methods on the SemanticKITTI dataset