Development and external validation of a prognostic model for occult atrial fibrillation in patients with ischemic stroke

ObjectiveCurrently, the risk of occult atrial fibrillation (AF) could not be predicted in patients with acute ischemic stroke (AIS) using a simple scoring system. Therefore, in this study, we developed and externally validated a nomogram to predict occult AF in patients with AIS.MethodsIn this study, we prospectively conducted a development cohort study with data collected at our stroke center from July 2017 to February 2018, and an external validation cohort from March 2019 to December 2019.ResultsFollow-up data were collected from 177 participants (56.5% older than 65 years, 29.4% female) for generating the nomogram model. Multivariate logistic regression analysis was performed with AF as the dependent variable indicated that age >65 years, heart rate >100, C-reactive protein (CRP), N-terminal pro-B-type natriuretic peptide (NT-proBNP) >270, hemorrhagic transformation (HT) as independent variables for predicting the development of AF, and a nomogram was generated based on these factors. The area under the receiver operating characteristic curve (AUC-ROC) for the model was 0.937, the C-index was 0.926, and the AUC-ROC for the validation cohort was 0.913.ConclusionTo our knowledge, this is the first nomogram developed and externally validated in a stroke center cohort for individualized prediction of risk of developing AIS in patients with occult AF. This nomogram could provide valuable information for the screening of occult AF after a stroke

Breast cancer-derived K172N, D301V mutations abolish Na+/H+ exchanger regulatory factor 1 inhibition of platelet-derived growth factor receptor signaling

AbstractNa+/H+ exchanger regulatory factor 1 (NHERF1) is a scaffold protein known to interact with a number of cancer-related proteins. nherf1 Mutations (K172N and D301V) were recently identified in breast cancer cells. To investigate the functional properties of NHERF1, wild-type and cancer-derived nherf1 mutations were stably expressed in SKMES-1 cells respectively. NHERF1-wt overexpression suppressed the cellular malignant phenotypes, including proliferation, migration, and invasion. nherf1 Mutations (K172N and D301V) caused complete or partial loss of NHERF1 functions by affecting the PTEN/NHERF1/PDGFRβ complex formation, inactivating NHERF1 inhibition of PDGF-induced AKT and ERK activation, and attenuating the tumor-suppressor effects of NHERF1-wt. These results further demonstrated the functional consequences of breast cancer-derived nherf1 mutations (K172N and D301V), and suggested the causal role of NHERF1 in tumor development and progression

Increase a real wind farm productivity through optimizing wind turbines layout

In this paper, a 3D wake model considering both axial and radial wind speed variations along the incoming wind direction, and the non-flat topography effect on the wind flow, is developed for the layout optimization of a real onshore wind farm. The effectiveness of two methods (i.e., the discretization method that divides the wind scenario into small intervals and the Monte Carlo method which randomly generates the discrete wind speed samples), for evaluating the overall wind farm power production under the wind speed variation of Weibull distribution is investigated for the comparative study. It is found that the Monte Carlo power evaluation method achieves a better outcome than the traditional discretization method, with more total power output and less computational cost. Through the layout optimization with a total of 15 wind turbines installed, the individual wind turbine yields more than 533 kW power out of 536 kW theoretical wake-free power. As the installed wind turbine number increases, the maximum discrepancy of the actual wind turbine power output with respect to the theoretical power increases from 3 kW (with 15 wind turbines) to 6 kW (with 20 wind turbines). Through the comparative study of different wind rotation scenarios, it is found that the maximum discrepancy of individual actual wind turbine power outputs is 8 kW and 13 kW for a rotational angle of 30° and 60°, respectively. Under the scenario of 90° wind rotation scenario, the maximum individual power output deficit with 20 installed wind turbines is around 20 kW and the discrepancy of the total power output by comparing to the baseline wind model is maximally 1% proving the robustness of the optimization results with respect to the wind scenario variation

The risk of low energy availability in Chinese elite and recreational female aesthetic sports athletes

Background Low energy availability (LEA) is a medical condition observed in athletes, with a higher prevalence in aesthetic sports. For the first time, this study evaluated the relative prevalence of LEA in female elite athletes (ELA) and recreational athletes (REA) in aesthetic sports in China. Methods Female athletes from 6 sports (trampolining, rhythmic gymnastics, aerobics, dance sport, cheerleading and dance) were recruited, including ELA (n = 52; age = 20 ± 3) on Chinese national teams and REA (n = 114; Age = 20 ± 2) from Beijing Sport University. Participants completed 2 online questionnaires to assess LEA and eating disorder risk. These included the Low Energy Availability in Females Questionnaire (LEAF-Q), which provided information on injury history, gastrointestinal function and menstrual history, and the Eating Disorder Inventory-3 Referral Form (EDI-3 RF). For a sub-group of elite athletes (n = 14), body composition, bone mineral density, and blood serum were also quantified. Results A total of 41.6% of participants (n = 69) were at increased risk of LEA, and 57.2% of participants (n = 95) were classified as high in eating disorder risk. For ELA vs. REA, there was a significantly higher prevalence of LEA risk (55.8% vs. 35.1%; p = 0.012) and amenorrhea (53.8% vs. 13.3%; p < 0.001). Elite athletes at increased risk of LEA had significantly lower estradiol (p = 0.021) and whole-body BMD (p = 0.028). Pearson correlations indicated that the whole-body BMD (r = − 0.667, p = 0.009) correlated negatively with LEAF-Q score. Conclusions Results of this study indicate that there is a risk of LEA in female Chinese athletes within aesthetic sports, and significantly higher prevalence of increased LEA risk observed in ELA than in REA. Chinese coaches and sports medicine staff working elite female athletes in aesthetic sports should develop strategies to reduce the prevalence of LEA

Research Advances in Hierarchically Structured PVDF-Based All-Organic Composites for High-Energy Density Capacitors

Polymer film capacitors have been widely applied in many pulsed power fields owing to their fastest energy-released rates. The development of ferroelectric polyvinylidene fluoride (PVDF)-based composites has become one of the hot research directions in the field of high-energy storage capacitors. Recently, hierarchically-structured all-organic composites have been shown to possess excellent comprehensive energy storage performance and great potential for application. In this review, most research advances of hierarchically-structured all-organic composites for the energy storage application are systematically classified and summarized. The regulating strategies of hierarchically structured all-organic composites are highlighted from the perspective of preparation approaches, tailored material choices, layer thicknesses, and interfaces. Systematic comparisons of energy storage abilities are presented, including electric displacement, breakdown strength, energy storage density, and efficiency. Finally, we present the remaining problems of hierarchically structured all-organic composites and provide an outlook for future energy storage applications

Effectiveness of data-driven wind turbine wake models developed by machine/deep learning with spatial-segmentation technique

In this paper, the effectiveness of three machine/deep learning algorithms, namely, the artificial neural network (ANN), convolutional neural network (CNN) and U-shape neural network (Unet), in constructing wind turbine wake modeling is investigated. In order to enhance the performance of different neural networks, the spatial-segmentation technique for wake flow field is adopted which aims to divide the original wake field configuration (4D × 50D, D is the rotor diameter) into several small pieces (each with 4D × 6.25D). This is followed by separately training the subdivided small piece of wake flow fields and the resultant sub-models are consolidated to predict the whole wake flow field. Both wake velocity field and turbulence intensity field are predicted by the wake model to facilitate its applications to alleviate both wind turbine power losses and fatigue loads caused by wake interactions. Through comparative study, it is found that by using the spatial-segmentation technique it can significantly reduce the prediction error of the wake velocity but not for the prediction of turbulence intensity. Among the three selected network structures, ANN has the best prediction performance yielding the wake model with the maximum error of 11.6 % near to the rotor place, while for other regions it is generally below 8 %. By dividing the wake flow field into pieces, the maximum error located right behind the rotor reduces to 7.2 % with others less than 6 %. Through further repetitive training analysis, it proves a better and robust wake model can be achieved by ANN with the spatial segmentation. In comparison, the prediction error of turbulence intensity field is higher, but still fairly accurate for the far wake prediction with the error less than 5 %.</p

The first completed genome of species Prevotella bivia, assembled from a clinically derived strain PLW0727

ABSTRACT: Objectives: Prevotella bivia is a species that commonly colonizes various human body sites, and it is associated with lots of human infections. However, until now, no complete genome sequence of this species has been published. Here, we assembled the first complete genome of P. bivia from a clinically derived strain PLW0727, to characterize its general genomic features, and to profile the capacity in encoding antibiotic resistance and virulence factors. Methods: Whole-genome sequencing was performed using Illumina and Nanopore platforms. Hybrid assembly was conducted using flye v2.9.1 and Unicycler v0.4.9b. Assembly completeness was assessed using CheckM v1.0.12. Comprehensive genome annotation was performed using eggNOG-mapper v2.1.5 and PATRIC v3.6.10. Results: The complete genome of PLW0727 consists of two circular chromosomes, chr1 and chr2, exhibiting a completeness of 99.66%, a G+C content of 39.5%, and a total size of 2.43 Mb. Chr1 and chr2 have lengths of 1 272 652 bp and 1 155 021 bp, harbouring 1 132 CDSs and 1 055 CDSs, respectively. Completion of the genome significantly reduced the proportion of hypothetical CDS annotations compared with the draft genomes. A gene-encoding antibiotic resistance to beta-lactams (i.e., cfxA3) has been annotated in chr2. By providing the genome sequence, strain PLW0727 was identified as a human pathogen with a probability of 0.614 using the PathogenFinder. Furthermore, genes involved in virulence-related functions, including host cell adherence and capsule immune modulation were also annotated. Conclusions: This study assembles the first complete genome for P. bivia, providing valuable genomic insights into its phylogeny, pathogenicity, and antibiotic resistance. These findings have important implications for the clinical management and prevention of P. bivia infections

Nomogram to Predict Poor Outcome after Mechanical Thrombectomy at Older Age and Histological Analysis of Thrombus Composition

An easy scoring system to predict the risk of poor outcome after mechanical thrombectomy among the elderly is currently not available. Therefore, we aimed to develop a nomogram for predicting the probability of negative prognosis in aged patients with acute ischemic stroke undergoing thrombectomy. In addition, we sought to investigate the association between histological thrombus composition and stroke characteristics. To this end, we prospectively studied a developed cohort using data collected from a stroke center from November 2015 to December 2019. The main outcome was functional independence, defined as a modified Rankin Scale score≤2 at 90 days following a mechanical thrombectomy. A nomogram model based on multivariate logistic models was generated. The retrieved thrombi were stained with hematoxylin and eosin and assessed according to histological composition. Our results demonstrated that age≥72 years was independently associated with poor outcome. A total of 304 participants completed the follow-up data to generate the nomogram model. After multivariate logistic regression, five variables remained independent predictors of outcome, including older age, hemorrhagic transformation, thrombolysis in cerebral infarction score, National Institute of Health Stroke score, and neutrophil-to-lymphocyte ratio, and were used to generate the nomogram. The area under the receiver-operating characteristic curve of the model was 0.803. The clots from elderly subjects with large-artery atherosclerosis, anterior circulation, and successful recanalization groups had a higher percentage of fibrin compared to those of younger patients. This is the first nomogram to be developed and validated in a stroke center cohort for individualized prediction of poor outcome in elderly patients after mechanical thrombectomy. Clot composition provides valuable information on the underlying pathogenesis of oxidation in older patients

MAGI3 negatively regulates Wnt/β-catenin signaling and suppresses malignant phenotypes of glioma cells

Gliomas are the most common primary brain malignancies and are associated with a poor prognosis. Here, we showed that the PDZ domain-containing protein membrane-associated guanylate kinase inverted 3 (MAGI3) was downregulated at the both mRNA and protein levels in human glioma samples. MAGI3 inhibited proliferation, migration, and cell cycle progression of glioma cells in its overexpression and knockdown studies. By using GST pull-down and co-immunoprecipitation assays, we found that MAGI3 bound to β-catenin through its PDZ domains and the PDZ-binding motif of β-catenin. MAGI3 overexpression inhibited β-catenin transcriptional activity via its interaction with β-catenin. Consistently, MAGI3 overexpression in glioma cells C6 suppressed expression of β-catenin target genes including Cyclin D1 and Axin2, whereas MAGI3 knockdown in glioma cells U373 and LN229 enhanced their expression. MAGI3 overexpression decreased growth of C6 subcutaneous tumors in mice, and inhibited expression of β-catenin target genes in xenograft tumors. Furthermore, analysis based on the Gene Expression Omnibus (GEO) glioma dataset showed association of MAGI3 expression with overall survival and tumor grade. Finally, we demonstrated negative correlation between MAGI3 expression and activity of Wnt/β-catenin signaling through GSEA of three public glioma datasets and immunohistochemical staining of clinical glioma samples. Taken together, these results identify MAGI3 as a novel tumor suppressor and provide insight into the pathogenesis of gliom