RESUMEN
Polyphenols are a group of naturally occurring compounds that possess a range of biological properties capable of potentially mitigating or preventing the progression of age-related cognitive decline and Alzheimer's disease (AD). AD is a chronic neurodegenerative disease known as one of the fast-growing diseases, especially in the elderly population. Moreover, as the primary etiology of dementia, it poses challenges for both familial and societal structures, while also imposing a significant economic strain. There is currently no pharmacological intervention that has demonstrated efficacy in treating AD. While polyphenols have exhibited potential in inhibiting the pathological hallmarks of AD, their limited bioavailability poses a significant challenge in their therapeutic application. Furthermore, in order to address the therapeutic constraints, several polymer nanoparticles are being explored as improved therapeutic delivery systems to optimize the pharmacokinetic characteristics of polyphenols. Polymer nanoparticles have demonstrated advantageous characteristics in facilitating the delivery of polyphenols across the blood-brain barrier, resulting in their efficient distribution within the brain. This review focuses on amyloid-related diseases and the role of polyphenols in them, in addition to discussing the anti-amyloid effects and applications of polyphenol-based polymer nanoparticles.
RESUMEN
Objectives: Wilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods. Methods: The study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≥ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms. Results: In this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstem damage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur. Conclusions: To sum up, the prediction model constructed using machine learning methods to predict WD cirrhosis has high accuracy. The most important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making.
RESUMEN
Accurate knowledge of network topology is vital for network monitoring and management. Network tomography can probe the underlying topologies of the intervening networks solely by sending and receiving packets between end hosts: the performance correlations of the end-to-end paths between each pair of end hosts can be mapped to the lengths of their shared paths, which could be further used to identify the interior nodes and links. However, such performance correlations are usually heavily affected by the time-varying cross-traffic, making it hard to keep the estimated lengths consistent during different measurement periods, i.e., once inconsistent measurements are collected, a biased inference of the network topology then will be yielded. In this paper, we prove conditions under which it is sufficient to identify the network topology accurately against the time-varying cross-traffic. Our insight is that even though the estimated length of the shared path between two paths might be "zoomed in or out" by the cross-traffic, the network topology can still be recovered faithfully as long as we obtain the relative lengths of the shared paths between any three paths accurately.