Echinacea: Bioactive Compounds and Agronomy.

For centuries, medicinal plants have been used as sources of remedies and treatments for various disorders and diseases. Recently, there has been renewed interest in these plants due to their potential pharmaceutical properties, offering natural alternatives to synthetic drugs. Echinacea, among the world's most important medicinal plants, possesses immunological, antibacterial, antifungal, and antiviral properties. Nevertheless, there is a notable lack of thorough information regarding the echinacea species, underscoring the vital need for a comprehensive review paper to consolidate existing knowledge. The current review provides a thorough analysis of the existing knowledge on recent advances in understanding the physiology, secondary metabolites, agronomy, and ecology of echinacea plants, focusing on E. purpurea, E. angustifolia, and E. pallida. Pharmacologically advantageous effects of echinacea species on human health, particularly distinguished for its ability to safeguard the nervous system and combat cancer, are discussed. We also highlight challenges in echinacea research and provide insights into diverse approaches to boost the biosynthesis of secondary metabolites of interest in echinacea plants and optimize their large-scale farming. Various academic databases were employed to carry out an extensive literature review of publications from 2001 to 2024. The medicinal properties of echinacea plants are attributed to diverse classes of compounds, including caffeic acid derivatives (CADs), chicoric acid, echinacoside, chlorogenic acid, cynarine, phenolic and flavonoid compounds, polysaccharides, and alkylamides. Numerous critical issues have emerged, including the identification of active metabolites with limited bioavailability, the elucidation of specific molecular signaling pathways or targets linked to echinacoside effects, and the scarcity of robust clinical trials. This raises the overarching question of whether scientific inquiry can effectively contribute to harnessing the potential of natural compounds. A systematic review and analysis are essential to furnish insights and lay the groundwork for future research endeavors focused on the echinacea natural products.

Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning.

Accurate simulation of tumor growth during chemotherapy has significant potential to alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D simulation model encompassing angiogenesis and tumor growth was developed to identify the vascular endothelial growth factor (VEGF) concentration and visualize the formation of a microvascular network. Accordingly, three anti-angiogenic drugs (Bevacizumab, Ranibizumab, and Brolucizumab) at different concentrations were evaluated in terms of their efficacy. Moreover, comprehensive mechanisms of tumor cell proliferation and endothelial cell angiogenesis are proposed to provide accurate predictions for optimizing drug treatments. The evaluation of simulation output data can extract additional features such as tumor volume, tumor cell number, and the length of new vessels using machine learning (ML) techniques. These were investigated to examine the different stages of tumor growth and the efficacy of different drugs. The results indicate that brolucizuman has the best efficacy by decreasing the length of sprouting new vessels by up to 16%. The optimal concentration was obtained at 10 mol m-3 with an effectiveness percentage of 42% at 20 days post-treatment. Furthermore, by performing comparative analysis, the best ML method (matching the performance of the reference simulations) was identified as reinforcement learning with a 3.3% mean absolute error (MAE) and an average accuracy of 94.3%.

Comparative Analysis of Machine Learning and Numerical Modeling for Combined Heat Transfer in Polymethylmethacrylate.

This study has compared different methods to predict the simultaneous effects of conductive and radiative heat transfer in a polymethylmethacrylate (PMMA) sample. PMMA is a type of polymer utilized in various sensors and actuator devices. One-dimensional combined heat transfer is considered in numerical analysis. Computer implementation was obtained for the numerical solution of the governing equation with the implicit finite difference method in the case of discretization. Kirchhoff transformation was used to obtain data from a non-linear equation of conductive heat transfer by considering monochromatic radiation intensity and temperature conditions applied to the PMMA sample boundaries. For the deep neural network (DNN) method, the novel long short-term memory (LSTM) method was introduced to find accurate results in the least processing time compared to the numerical method. A recent study derived the combined heat transfer and temperature profiles for the PMMA sample. Furthermore, the transient temperature profile was validated by another study. A comparison proves the perfect agreement. It shows the temperature gradient in the primary positions, which provides a spectral amount of conductive heat transfer from the PMMA sample. It is more straightforward when they are compared with the novel DNN method. Results demonstrate that this artificial intelligence method is accurate and fast in predicting problems. By analyzing the results from the numerical solution, it can be understood that the conductive and radiative heat flux are similar in the case of gradient behavior, but the amount is also twice as high approximately. Hence, total heat flux has a constant value in an approximated steady-state condition. In addition to analyzing their composition, the receiver operating characteristic (ROC) curve and confusion matrix were implemented to evaluate the algorithm's performance.

Kinematic and Workspace Analysis of the Master Robot in the Sinaflex Robotic Telesurgery System.

Robotic telesurgery systems, including master and slave robots, have emerged in recent years to provide benefits for both surgeons and patients. Surgeons use the master manipulator to navigate the slave robot. The Sinaflex telesurgery system introduced recently by Sina Robotics and Medical Innovators Co., Ltd. consists of two main subsystems: master robotic surgery console and slave surgery robots. As the surgeon use the master robot's handles to control the slave surgery robots, it is important for the master robot to provide the ergonomic postures for the surgeon and also providing a large enough workspace and good manipulability for the surgeon to control it. So in this paper, workspace, manipulability and isotropy of each handle at the master robot of the Sinaflex telesurgery system are analyzed. To this end, the kinematic of the master manipulator is derived, and its Jacobian is calculated. Using the simulation environment, the workspace of the master handle is obtained and drawn. The manipulability of the robot for each points of the workspace is computed. According to the results attained from the simulation study, the most manipulability values lie between 0.1 and 0.9 where it is greater than 0.44 for more than 50% of the whole workspace points of the end effector, which is as large as 574×484×560 mm.