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The traditional graduate admission method is to evaluate students' performance and interview results, but this method relies heavily on the subjective feelings of the evaluators, and these methods may not be able to comprehensively and objectively evaluate the qualifications and potential of the applicants. At present, artificial intelligence has played a key role in the reform of the education system, and the data processing function of artificial intelligence has greatly reduced the workload of screening work. Therefore, this study aims to optimize the graduate enrollment evaluation process by applying a new composite model, the random forest–improved sparrow search algorithm (RF–ISSA). The research used seven data sets including research, cumulative grade point average (CGPA), letter of recommendation (LOR), statement of purpose (SOP), university rating, TOEFL score, and graduate record examination (GRE) score, and carried out the necessary data pre-processing before the model construction. The experimental results show that the RMSE and R values of the composite model are 0.0543 and 0.9281, respectively. The predicted results of the model are very close to the actual data. In addition, the study found that the importance score of CGPA was significantly higher than other characteristics, and that this value has the most significant impact on the outcome of the graduate admissions assessment. Overall, this study shows that combining the integrated strategy sparrow search algorithm (ISSA) with hyperparameter optimization and focusing on the most influential features can significantly improve the predictive performance and applicability of graduate admissions models, providing a more scientific decision support tool for school admissions professionals. [ABSTRACT FROM AUTHOR]

With the popularity of higher education and the evolution of the workplace environment, graduate education has become a key choice for students planning their future career paths. Therefore, this study proposes to use the data processing ability and pattern recognition ability of machine learning models to analyze the relevant information of graduate applicants. This study explores three different models—backpropagation neural networks (BPNN), random forests (RF), and logistic regression (LR)—and combines them with the firefly algorithm (FA). Through data selection, the model was constructed and verified. By comparing the verification results of the three composite models, the model whose evaluation results were closest to the actual data was selected as the research result. The experimental results show that the evaluation result of the BPNN-FA model is the best, with an R value of 0.8842 and the highest prediction accuracy. At the same time, the influence of each characteristic parameter on the prediction result was analyzed. The results show that CGPA has the greatest influence on the evaluation results, which provides the evaluation direction and evaluation results for the evaluators to analyze the level of students' scientific research ability, as well as providing impetus to continue to promote the combination of education and artificial intelligence. [ABSTRACT FROM AUTHOR]

Objectives: To assess the feasibility of 5.0 T magnetic resonance imaging (MRI) in characterizing pancreatic cystic lesions (PCLs), compared with 3.0 T MRI and multidetector computed tomography (MDCT). Methods: Thirty-five patients with PCLs underwent 5.0 T MR alongside 3.0 T MR or MDCT. Two observers measured subjective and objective image quality scores. The consistency of two observers between 5.0 T and 3.0 T was calculated by intraclass correlation coefficients. The characteristics of PCLs and their specific diagnosis, as well as benignity/malignancy, were evaluated across MDCT, 3.0 T, and 5.0 T MRI. Results: The 5.0 T MR demonstrated significantly higher subjective image quality and SNR on T1WI compared to that in 3.0 T MR (p < 0.05). The 5.0 T MRI identified more cyst lesions than the 3.0 T MRI (40 and 32) and MDCT (82 and 56). The sensitivity, specificity, and accuracy for differentiating benign from malignant lesions with 5.0 T MRI (75%, 100%, and 91.4%, respectively) surpassed those of 3.0 T MRI and MDCT. The accuracy of the specific diagnosis of PCLs at 5.0 T MRI (80%) was superior to 3.0 T MRI and MDCT. Conclusions: 5.0 T MRI exhibits certain superiority in delineating details of PCLs and in clinical diagnostic accuracy, outperforming MDCT and 3.0 T MRI while maintaining sufficient image quality. [ABSTRACT FROM AUTHOR]

The polyphenol profile, antioxidant activity (particularly peroxyl radical-scavenging capacity (PSC) and cellular antioxidant activity (CAA)) and cytotoxicity of extracts from seven varieties of blue honeysuckle were compared in this study. Moreover, an analysis of correlations between individual polyphenol profiles and antioxidant activities was also conducted. Seventeen components were found in the investigated blue honeysuckle extracts, with anthocyanins being the prominent bioactive components among polyphenols. Fruit peel contained higher concentrations of individual anthocyanins compared to the fruit pulp. Beilei blue honeysuckle had the highest antioxidant activity. Correlation analysis showed that antioxidant activity values were highly associated with cyanidin-3-glucoside levels. Blue honeysuckle extracts were not cytotoxic over the range of doses tested. Among the varieties analyzed, Beilei possessed the highest antioxidant properties and was found to be the most appropriate source of natural antioxidants.

The high prevalence and complex etiology of renal diseases already impose a heavy disease burden on patients and society. In certain kidney diseases such as acute kidney injury and chronic kidney disease, current treatments are limited to slowing rather than stabilizing or reversing disease progression. Therefore, it is crucial to study the pathological mechanisms of kidney disease and discover new therapeutic targets and effective therapeutic drugs. As cell-free therapeutic strategies are continually being developed, extracellular vesicles derived from mesenchymal stem cells (MSC-EVs) have emerged as a hot topic for research in the field of renal diseases. Studies have demonstrated that MSC-EVs not only reproduce the therapeutic effects of MSCs but also localize to damaged kidney tissue. Compared to MSCs, MSC-EVs have several advantages, including ease of preservation, low immunogenicity, an inability to directly form tumors, and ease of artificial modification. Exploring the detailed mechanisms of MSC-EVs by developing standardized culture, isolation, purification, and drug delivery strategies will help facilitate their clinical application in kidney diseases. Here, we provide a comprehensive overview of studies about MSC-EVs in kidney diseases and discuss their limitations at the human nephrology level. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the efficacy and safety of Hengli® Chinese botulinum toxin type A (BTX-A; 100 U) in Chinese patients with overactive bladder. Methods: This study was a multicenter, randomized, double-blind, placebo-controlled trial in Chinese patients who were inadequately managed with anticholinergic medications. Eligible patients were randomized 2:1 to receive intradetrusor injections of Hengli® BTX-A (n = 144) or placebo (n = 72). The primary endpoint was the change in the number of daily micturition episodes at week 6 from baseline. The secondary efficacy endpoints included the average frequency of urgency and urinary incontinence (UI) episodes per day, urgency score, average micturition volume per day, OABSS, and QoL score. Results: In the Hengli® BTX-A group, there was a significantly greater reduction in the average number of micturition episodes per 24 h compared with the placebo group (3.28 vs. 1.43; p = 0.003). Moreover, there was a significantly greater improvement in the daily number of urgency episodes, micturition volume and OABSS score. An increased post-void residual urine volume, dysuria, and urinary tract infection represented adverse events (AEs) in the Hengli® BTX-A group. Most AEs were mild or moderate in severity. One patient in the BTX-A group initiated clean intermittent catheterization (CIC) during treatment. Conclusion: Hengli® BTX-A treatment was well-tolerated and resulted in significant improvements in OAB symptoms among Chinese patients inadequately managed by anticholinergics. Clinical Trial Registration: http://www.chinadrugtrials.org.cn/clinicaltrials.prosearch.dhtml, Identifier: CTR20131190. [ABSTRACT FROM AUTHOR]

Solution crystallization is an important separation unit operation in active pharmaceutical ingredient production. Solvent is one of the important factors affecting crystal morphology. How to select/design suitable solvents is still one of the most urgent problems in the crystallization field. In this article, a framework for crystallization solvent design based on the developed quantitative prediction model of crystal morphology is proposed. First, molecular dynamics is used to predict the crystal morphology in solvents. Next, solvent descriptors are selected by stepwise regression method. Then, the quantitative relationship between crystal aspect ratio and solvent descriptors is developed. Subsequently, computer‐aided molecular design method is integrated with the developed quantitative prediction model. The crystallization solvent design problem is expressed as a mixed‐integer nonlinear programming model with maximum yield as the objective function, which is solved by decomposition algorithm. Finally, the crystallization solvent design framework is applied to two cases and experimental verification is implemented. [ABSTRACT FROM AUTHOR]