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BackgroundOmicron (B.1.1.529), a variant of SARS-CoV-2, has emerged as a dominant strain in COVID-19 pandemic. This development has raised concerns about the effectiveness of vaccination to Omicron, particularly in the context of children and adolescents. Our study evaluated the efficacy of different COVID-19 vaccination regimens in children and adolescents during the Omicron epidemic phase.MethodsWe searched PubMed, Cochrane, Web of Science, and Embase electronic databases for studies published through March 2023 on the association between COVID-19 vaccination and vaccine effectiveness (VE) against SARS-CoV-2 infection in children and adolescents at the Omicron variant period. The effectiveness outcomes included mild COVID-19 and severe COVID-19. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was prospectively registered in PROSPERO (CRD42023390481).ResultsA total of 33 studies involving 16,532,536 children were included in the analysis. First, in children and adolescents aged 0–19 years, the overall VE of the COVID-19 vaccine is 45% (95% confidence interval [CI]: 40 to 50%). Subgroup analysis of VE during Omicron epidemic phase for different dosage regimens demonstrated that the VE was 50% (95% CI: 44 to 55%) for the 2-dose vaccination and 61% (95% CI: 45 to 73%) for the booster vaccination. Upon further analysis of different effectiveness outcomes during the 2-dose vaccination showed that the VE was 41% (95% CI: 35 to 47%) against mild COVID-19 and 71% (95% CI: 60 to 79%) against severe COVID-19. In addition, VE exhibited a gradual decrease over time, with the significant decline in the efficacy of Omicron for infection before and after 90 days following the 2-dose vaccination, registering 54% (95% CI: 48 to 59%) and 34% (95% CI: 21 to 56%), respectively.ConclusionDuring the Omicron variant epidemic, the vaccine provided protection against SARS-CoV-2 infection in children and adolescents aged 0–19 years. Two doses of vaccination can provide effective protection severe COVID-19, with booster vaccination additionally enhancing VE.

Introduction: Esophageal stenting is a widely used treatment for esophageal diseases, which can also be used for adjuvant therapy and feeding after chemotherapy for esophageal cancer. The structural parameters of the stent have a significant impact on its mechanical properties and patient comfort.Methods: In the present work, we reconstructed the esophagus model based on the patient’s computed tomography (CT) data, and designed stents with different structural parameters. We used 3D printing technology to achieve rapid production of the designed stents by using Thermoplastic polyurethane (TPU)/Poly-ε-caprolactone (PCL) blends as the materials. The mechanical properties and effects on the esophagus of polymer stents with four different structural parameters of diameter, wall thickness, length and flaring were investigated by in vitro tests of radial compression and migration of the stents, as well as by finite element simulations of the stent implantation process in the esophagus and of the stent migration process. An artificial neural network model was established to predict the radial force of the stent and the maximum equivalent stress of the esophagus during implantation based on these four structural parameters.Results: The results show that wall thickness was the structural parameter that had the greatest impact on the radial force of the stent (statistically significant, p < 0.01), and flaring was the structural parameter that had the greatest impact on the maximum equivalent stress of the esophageal wall after stent implantation (statistically significant, p < 0.01). No. 6 stent had a maximum radial force of 18.07 N, which exceeded that of commercial esophageal stents and had good mechanical properties. And the maximum equivalent force on the esophagus caused by its implantation was only 30.39 kPa, which can improve patient comfort. The predicted values of the constructed back propagation (BP) neural network model had an error of less than 10% from the true values, and the overall prediction accuracies were both above 97%, which can provide guidance for optimizing the design of the stent and for clinical research.Discussion: 3D printing technology presents a wide range of applications for the rapid fabrication of personalized TPU/PCL blend stents that are more suitable for individual patients.

ObjectiveThis study aimed to summarize the effectiveness of rhythmic auditory stimulation (RAS) for the treatment of gait and motor function in Parkinson's disease (PD) through a systematic review and meta-analysis.MethodsAll studies were retrieved from eight databases. The effects of RAS on PD were determined using the following indicators: gait parameters including step length, stride width, step cadence, velocity, stride length; motor function including 6 min walk test (6MWT) and timed up-and-go test (TUGT); the Unified Parkinson's Disease Rating Scale (UPDRS); and the Berg Balance Scale (BBS). The risk map of bias of the quality of the studies and the meta-analysis results of the indicators was prepared with RevMan 5.2 software.ResultsTwenty-one studies were included in the systematic review, and 14 studies were included in the meta-analysis. In the meta-analysis, the results of gait parameters, namely, velocity, step length, and stride length, were statistically significant (P < 0.05), whereas the results of cadence and stride width were not statistically significant (P ≧ 0.05). The results of 6MWT and TUGT for motor function as well as UPDRS-II, UPDRS-III, and BBS were statistically significant (P < 0.05).ConclusionsRAS could improve gait parameters, walking function, balance function, and daily living activities of individuals with PD. The application of RAS in conventional rehabilitation approaches can enhance motor performance in PD. Future studies should use a large sample size and a rigorous design to obtain strong conclusions about the advantages of RAS for the treatment of gait and motor function in PD.

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.