Flavonoids and its derivatives from Callistephus chinensis flowers and their inhibitory activities against alpha-glucosidase

Inhibitors of carbohydrate-hydrolysing enzymes play an important role for the treatment of diabetes. One of the therapeutic methods for decreasing of postprandial hyperglycemia is to retard absorption of glucose by the inhibition of carbohydrate-hydrolysing enzymes, such as α-glucosidase, in the digestive organs. To investigate the therapeutic potential of compounds from natural sources, Callistephus chinensis flowers (CCF) were tested for inhibition of α-glucosidase, and acarboes was used as the positive control. The 70 % ethanol extract of CCF exhibited significant α-glucosidase inhibitory activities with IC50 value of 8.14 μg/ml. The stepwise polarity fractions of CCF were tested further for in vitro inhibition of α-glucosidase. The ethyl acetate (EtOAc) fraction exhibited the most significant inhibitory activity. Eight pure compounds, apigenin, apigenin-7-O-β-D-glucoside, kaempferol, hyperin, naringenin, quercetin, luteolin, and kaempferol-7-O-β-D-glucoside, were isolated (using enzyme assay-guide fractionation method) from the EtOAc fraction. Among these, quercetin was the most active one (IC50 values 2.04 μg/ml), and it appears that the inhibiting percentages are close to acarbose (IC50 values 2.24 μg/ml), the positive control, on α-glucosidase inhibition. HPLC/UV analysis indicated that the major components of CCF are kaempferol, hyperin and quercetin. The presented results revealed that CCF containing these eight flavonoids could be a useful natural source in the development of a novel α-glucosidase inhibitory agent against diabetic complications

A distance-aware approach for reliable out-of-distribution detection of wind turbine gearbox fault diagnosis

Fault diagnosis of wind turbine gearbox is essential to ensure operational efficiency and prevent costly downtime. However, conventional deep learning models often struggle with domain shift, where the distribution of testing data differs from that of training data. This issue is more pronounced with out-of-distribution inputs—data outside the conditions the model was trained on. These challenges can lead to unreliable diagnostic results and potentially hazardous situations. To address this, we introduce Spectral Normalization and Gaussian Process methods into Res2Net framework to enhance its ability to detect out-of-distribution data. Spectral Normalization and Gaussian Process improve the model’s ability to assess the distance between test and training data. This model can handle out-of-distribution data due to both epistemic and aleatory uncertainty. The experiment collected raw vibration signals from gearbox under varied conditions. Unknown faults simulated epistemic uncertainty, while noisy samples resulted in aleatory uncertainty. These signals were converted into images using the Gramian Angular Difference Field transformation. The resulting images were then fed into the Res2Net model, enhanced with Spectral Normalization and Gaussian Process. The model outputs include classification results and corresponding uncertainty values based on distance awareness. With quantified uncertainty values, the model can reflect the trustworthiness of the diagnostic results. By comparing these uncertainty values with predefined thresholds, it is possible to distinguish whether the data are out-of-distribution or not. Experiments have proven the superiority of the Distance-Aware Res2Net in out-of-distribution detection and fault diagnosis

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'