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Objectives: Alzheimer's disease (AD) is a serious neurodegenerative disease. Although many therapeutic strategies have been studied, their clinical applications are immature. Moreover, these methods can only alleviate symptoms rather than cure it, posing a challenge to brain health in older adults worldwide. Curcumin (CUR) is a very promising natural compound for nerve protection and treatment. It can prevent and treat AD, and on the other hand, its fluorescence properties can be used in the diagnosis of AD. However, CUR is characterized by very low water solubility, fluid instability, rapid metabolism, low bioavailability and difficulty in penetrating the biological barriers, which limit its application. Nanocarriers are a potential material to improve the biocompatibility of CUR and its ability to cross biological barriers. Therefore, delivering CUR by nanocarriers is an effective method to achieve better efficacy. Methods: In this review, the preventive, therapeutic and diagnostic effects of CUR nanoformulations on AD, as well as various patents, clinical trials and experimental research progress in this field are discussed. The aim is to provide detailed reference and practical suggestions for future research. Results: CUR has a variety of pharmacological activities in the prevention and treatment of AD, and its nanoformulation can effectively improve solubility, bioavailability and the ability to penetrate the blood-brain barrier. Significant benefits have been observed in the current study. Discussion: CUR formulations have a good prospect in the prevention, diagnosis and treatment of AD, but the safety and principle of its administration need more detailed study in the future. [ABSTRACT FROM AUTHOR]

Sugar gourd shaped CeF3:Tb3+ has been successfully synthesized by polyol method. The phase, morphology and fluorescent properties of CeF3:Tb3+ were characterized by X‐ray diffraction (XRD), transmission electron microscopy (TEM), infrared spectroscopy (IR) and photoluminescence (PL) spectra, respectively. It can be seen that the volume changes of giethylene glycol (DEG) and H2O content did not affect the phase structure of CeF3:Tb3+. The emission spectrum of CeF3:Tb3+ shows the characteristic emissions of Tb3+ ion, indicating that an energy transfer from Ce3+ to Tb3+ occurs in CeF3:Tb3+, and the decay time of CeF3:Tb3+ was not affected by the change of DEG content. [ABSTRACT FROM AUTHOR]

To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suitable parameters and the best diagnostic rate. Based on the pre-processed infrared thermal images of the faulty bearing, 72 second-order statistical features were obtained as information for fault diagnosis. RFR considered the robustness of the features, and new sequences were obtained. BLS was optimized by GA for fault diagnosis. New sequence features were added to the model sequentially, one at a time. After satisfying the model conditions, the most appropriate number of features was selected as the first 20. The search results for the number of feature nodes, the number of feature node windows and the number of enhancement nodes for the BLS were 24, 19 and 544, respectively, and the fault diagnosis rate of 98.8889% was achieved. According to a comparison with CFR-GA-BLS, BLS, PSO-BLS and Grdy-BLS, our proposed model is more advantageous in the search for the best performance. The fault diagnosis accuracy is higher compared to SVM and RF. The speed of our proposed model is 207 times faster than 1DCNN and 10,147 times faster than 2DCNN. [ABSTRACT FROM AUTHOR]

The purpose of this study was to differentiate the retroperitoneal paragangliomas and schwannomas using computed tomography (CT) radiomics. This study included 112 patients from two centers who pathologically confirmed retroperitoneal pheochromocytomas and schwannomas and underwent preoperative CT examinations. Radiomics features of the entire primary tumor were extracted from non-contrast enhancement (NC), arterial phase (AP) and venous phase (VP) CT images. The least absolute shrinkage and selection operator method was used to screen out key radiomics signatures. Radiomics, clinical and clinical-radiomics combined models were built to differentiate the retroperitoneal paragangliomas and schwannomas. Model performance and clinical usefulness were evaluated by receiver operating characteristic curve, calibration curve and decision curve. In addition, we compared the diagnostic accuracy of radiomics, clinical and clinical-radiomics combined models with radiologists for pheochromocytomas and schwannomas in the same set of data. Three NC, 4 AP, and 3 VP radiomics features were retained as the final radiomics signatures for differentiating the paragangliomas and schwannomas. The CT characteristics CT attenuation value of NC and the enhancement magnitude at AP and VP were found to be significantly different statistically (P < 0.05). The NC, AP, VP, Radiomics and clinical models had encouraging discriminative performance. The clinical-radiomics combined model that combined radiomics signatures and clinical characteristics showed excellent performance, with an area under curve (AUC) values were 0.984 (95% CI 0.952–1.000) in the training cohort, 0.955 (95% CI 0.864–1.000) in the internal validation cohort and 0.871 (95% CI 0.710–1.000) in the external validation cohort. The accuracy, sensitivity and specificity were 0.984, 0.970 and 1.000 in the training cohort, 0.960, 1.000 and 0.917 in the internal validation cohort and 0.917, 0.923 and 0.818 in the external validation cohort, respectively. Additionally, AP, VP, Radiomics, clinical and clinical-radiomics combined models had a higher diagnostic accuracy for pheochromocytomas and schwannomas than the two radiologists. Our study demonstrated the CT-based radiomics models has promising performance in differentiating the paragangliomas and schwannomas. [ABSTRACT FROM AUTHOR]