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The structure of hybutimibe was analyzed by ultraviolet absorption spectroscopy, infrared absorption spectroscopy, mass spectrometry, nuclear magnetic resonance (1H NMR, 13C NMR, DEPT, 1H-1H COSY, 1H-1H NOESY, 1H-13C HSQC and 1H-13C HMBC) spectroscopy and single crystal diffraction. The 1H and 13C NMR signals of hybutimibe were assigned. The crystal structure of hybutimibe was studied by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and powder X-ray diffraction (PXRD).

ObjectiveTo evaluate the value of the malignant subregion-based texture analysis in predicting Ki-67 status in breast cancer.Materials and methodsThe dynamic contrast-enhanced magnetic resonance imaging data of 119 histopathologically confirmed breast cancer patients (81 patients with high Ki-67 expression status) from January 2018 to February 2023 in our hospital were retrospectively collected. According to the enhancement curve of each voxel within the tumor, three subregions were divided: washout subregion, plateau subregion, and persistent subregion. The washout subregion and the plateau subregion were merged as the malignant subregion. The texture features of the malignant subregion were extracted using Pyradiomics software for texture analysis. The differences in texture features were compared between the low and high Ki-67 expression cohorts and then the receiver operating characteristic (ROC) curve analysis to evaluate the predictive performance of texture features on Ki-67 expression. Finally, a support vector machine (SVM) classifier was constructed based on differential features to predict the expression level of Ki-67, the performance of the classifier was evaluated using ROC analysis and confirmed using 10-fold cross-validation.ResultsThrough comparative analysis, 51 features exhibited significant differences between the low and high Ki-67 expression cohorts. Following feature reduction, 5 features were selected to build the SVM classifier, which achieved an area under the ROC curve (AUC) of 0.77 (0.68–0.87) for predicting the Ki-67 expression status. The accuracy, sensitivity, and specificity were 0.76, 0.80, and 0.68, respectively. The average AUC from the 10-fold cross-validation was 0.72 ± 0.14.ConclusionThe texture features of the malignant subregion in breast cancer were potential biomarkers for predicting Ki-67 expression level in breast cancer, which might be used to precisely diagnose and guide the treatment of breast cancer.

The liver, a complex and vital organ in the human body, is susceptible to various diseases, including metabolic disorders, acute hepatitis, cirrhosis, and hepatocellular carcinoma. In recent decades, these diseases have significantly contributed to global morbidity and mortality. Currently, liver transplantation remains the most effective treatment for hepatic disorders. Nucleic acid therapeutics offer a selective approach to disease treatment through diverse mechanisms, enabling the regulation of relevant genes and providing a novel therapeutic avenue for hepatic disorders. It is expected that nucleic acid drugs will emerge as the third generation of pharmaceuticals, succeeding small molecule drugs and antibody drugs. Lipid nanoparticles (LNPs) represent a crucial technology in the field of drug delivery and constitute a significant advancement in gene therapies. Nucleic acids encapsulated in LNPs are shielded from the degradation of enzymes and effectively delivered to cells, where they are released and regulate specific genes. This paper provides a comprehensive review of the structure, composition, and applications of LNPs in the treatment of hepatic disorders and offers insights into prospects and challenges in the future development of LNPs. [ABSTRACT FROM AUTHOR]

Flight simulation training is one of the most important methods in early-stage civil aviation flight training. In this regard, flight simulation competitions are effective tools for evaluating the flight skills of trainees. In this study, a model is developed for evaluating the flight skills of trainees by integrating GBDT (Gradient Boosting Decision Tree), PSO (Particle Swarm Optimization), and CNNs (Convolutional Neural Networks). Flight data from simulations is employed for model training. Initially, performance data and scores are gathered from a simulated flight competition platform. The GBDT algorithm is then applied to filter and identify essential flight parameters from the collected data. Subsequently, the PSO-CNN model is utilized to train on the extracted flight parameters. The proposed GBDT-PSO-CNN model achieves a recognition rate of 93.8% on the test dataset. This assessment system is of significant importance for improving the specific maneuvering skill level of flight trainees. [ABSTRACT FROM AUTHOR]