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Keywords = biological rotary motor

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21 pages, 7314 KB  
Article
Neuroprotective Effects of Rosa roxburghii Tratt Juice Concentrate Powder in Parkinson’s Disease Mice via the PI3K/AKT Signaling Pathway
by Tong Jin, Long Liu, Faguang Kuang, Mingjie Chen, Haonan Chen, Jiapan Deng, Yikai Yang, Baofei Sun and Heng Luo
Pharmaceuticals 2026, 19(5), 711; https://doi.org/10.3390/ph19050711 - 30 Apr 2026
Viewed by 856
Abstract
Background: The absence of disease-modifying treatments for Parkinson’s disease (PD)—a neurodegenerative condition with escalating global incidence—represents a critical unmet medical need. Traditionally utilized for both dietary consumption and medicinal preparations, the fruit derived from Rosa roxburghii Tratt demonstrates a remarkably rich profile [...] Read more.
Background: The absence of disease-modifying treatments for Parkinson’s disease (PD)—a neurodegenerative condition with escalating global incidence—represents a critical unmet medical need. Traditionally utilized for both dietary consumption and medicinal preparations, the fruit derived from Rosa roxburghii Tratt demonstrates a remarkably rich profile of biologically active compounds, with flavonoids, triterpenoids, and organic acids representing the predominant classes. Experimental evidence indicates that these compounds elicit robust antioxidative, anti-inflammatory, and neuroprotective effects, making them promising candidates for neurodegenerative disease modulation. This study aimed to systematically evaluate the neuroprotective effects of Rosa roxburghii Tratt juice concentrate powder (RRJCP) across the preventive, interventional, and therapeutic stages of PD and to elucidate its underlying molecular mechanisms. Methods: Rosa roxburghii Tratt juice was subjected to rotary evaporation concentration and vacuum freeze-drying to obtain the juice concentrate powder. C57BL/6 mice were randomly assigned to three main groups (prevention, intervention, and treatment), each containing subgroups including a normal control, an MPTP model group, low-, medium-, and high-dose RRCJP groups (50, 100, and 200 mg/kg), and a positive control Madopar group, totaling 18 subgroups. A chronic MPTP-induced PD mouse model was established. Motor function was assessed via the open field test, pole test, and wire hang test. Substantia nigra neuronal morphology was examined by hematoxylin and eosin staining. The area of tyrosine hydroxylase (TH)-positive regions was measured by immunohistochemistry. The levels of oxidative stress indicators in serum were measured using biochemical kits. Network pharmacology was employed to predict core targets, and the expression of PI3K/AKT pathway and apoptosis-related proteins was determined by Western blotting. Results: Compared with the MPTP model group, RRCJP (200 mg/kg) significantly increased the total distance traveled in the open field, shortened the pole climbing time, and improved the wire hang score. It attenuated the morphological disorganization and nuclear pyknosis of substantia nigra neurons, increased the TH-positive area and TH protein expression, reduced serum MDA content, and elevated the activities of SOD and GSH-Px. Network pharmacology analysis indicated that the PI3K/AKT signaling pathway was among the core targets. Western blotting results further showed that the juice concentrate powder upregulated the expression of p-PI3K, p-AKT, and Bcl-2, while downregulating Bax and Cleaved Caspase-3 levels, which was consistent with the network pharmacology prediction. Conclusions: RRCJP exerts neuroprotective effects across the preventive, interventional, and therapeutic stages in PD model mice, the mechanisms of which may be associated with activation of the PI3K/AKT signaling pathway, attenuation of oxidative stress, and inhibition of neuronal apoptosis. Full article
(This article belongs to the Section Natural Products)
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14 pages, 2450 KB  
Article
Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning
by Ryo Kanada, Atsushi Tokuhisa, Koji Tsuda, Yasushi Okuno and Kei Terayama
Biomolecules 2020, 10(3), 482; https://doi.org/10.3390/biom10030482 - 21 Mar 2020
Cited by 12 | Viewed by 5250
Abstract
Accompanied with an increase of revealed biomolecular structures owing to advancements in structural biology, the molecular dynamics (MD) approach, especially coarse-grained (CG) MD suitable for macromolecules, is becoming increasingly important for elucidating their dynamics and behavior. In fact, CG-MD simulation has succeeded in [...] Read more.
Accompanied with an increase of revealed biomolecular structures owing to advancements in structural biology, the molecular dynamics (MD) approach, especially coarse-grained (CG) MD suitable for macromolecules, is becoming increasingly important for elucidating their dynamics and behavior. In fact, CG-MD simulation has succeeded in qualitatively reproducing numerous biological processes for various biomolecules such as conformational changes and protein folding with reasonable calculation costs. However, CG-MD simulations strongly depend on various parameters, and selecting an appropriate parameter set is necessary to reproduce a particular biological process. Because exhaustive examination of all candidate parameters is inefficient, it is important to identify successful parameters. Furthermore, the successful region, in which the desired process is reproducible, is essential for describing the detailed mechanics of functional processes and environmental sensitivity and robustness. We propose an efficient search method for identifying the successful region by using two machine learning techniques, Bayesian optimization and active learning. We evaluated its performance using F1-ATPase, a biological rotary motor, with CG-MD simulations. We successfully identified the successful region with lower computational costs (12.3% in the best case) without sacrificing accuracy compared to exhaustive search. This method can accelerate not only parameter search but also biological discussion of the detailed mechanics of functional processes and environmental sensitivity based on MD simulation studies. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)
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