Advances in the chemical constituents, pharmacological activity, and clinical application of Smilacis Glabrae Rhizoma: A review and predictive analysis of quality markers (Q-markers).

Smilacis Glabrae Rhizoma (SGR) is recognized in traditional Chinese medicine for its distinctive therapeutic properties and abundant supply. Its phytochemical profile is diverse, encompassing flavonoids, steroids, saccharides, phenolic glycosides, volatile constituents, organic acids, phenylpropanoids, stilbenoids, among others. Recent pharmacological investigations reveal that SGR possesses a broad spectrum of pharmacological effects with multifaceted clinical applications. This review collates the current knowledge on SGR's chemical composition, pharmacological activities, and its clinical utility. Utilizing network pharmacology and molecular docking approaches, this study provides a preliminary identification of potential quality markers (Q-Markers) within SGR. The findings suggest that compounds such as astilbin, isoengelitin, neoisoastilbin, neoastilbin, astragaloside, diosgenin, resveratrol, stigmasterol, ß-sitosterol, and quercetin in SGR are promising candidates for Q-Markers. While flavonoids are the most extensively studied, there is a pressing need to further explore the active monomeric compounds within SGR. The introduction of Q-Markers is instrumental in developing standardized quality metrics. Specifically, astilbin has been noted for its antitumor, antidiabetic, antihypertensive, anti-hyperuricemic, and hepatoprotective potential, warranting further research for therapeutic applications.

Protective Effects and Mechanisms of Melatonin on Stress Myocardial Injury in Rats.

ABSTRACT: Prolonged and intense stress can exceed the body's normal self-regulation and limited compensatory and repair capacity, resulting in pathological damage to the body. In this study, we established a rat stress myocardial injury (SMI) model to explore the protective effect of melatonin (MLT) on SMI and its possible mechanisms of action. Adult female Sprague Dawley (SD) rats were randomly divided into 5 groups: blank control group (NC), SMI group, MLT low-dose group, MLT medium-dose group, and MLT high-dose group, and 10 rats in each group were used to establish a SMI model by the water immersion restraint method. We observed the changes in body weight and tail vein glucose of each group. Serum levels of corticosterone (Cort), creatine kinase isoenzyme (CK-MB), and Troponin Ⅰ (Tn-Ⅰ) and activity of lactic acid dehydrogenase were measured by ELISA. Transcriptome sequencing was used to find differentially expressed genes in the control and model groups, and the results were verified by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). HE staining was used to visualize the pathological changes in the heart tissue of each group, and Western blot was used to study the differences in protein expression in the cardiomyocytes of each group to further corroborate the results. The body weight growth rate of rats in the SMI group was significantly lower than that of the NC group ( P < 0.01), and the body weight growth rate of rats in the MLT high-dose group was significantly higher than that of the SMI group ( P < 0.05) with no significant difference compared with the NC group rats. The mean blood glucose of rats in the SMI group was significantly higher compared with the NC group ( P < 0.001), while the mean blood glucose of rats in the MLT administration groups was dose-dependently reduced compared with the SMI group. By RNA-seq and bioinformatics tools such as KEGG and Gene ontology, we found that the circadian clock-related genes Ciart , Arnt1 , Per1 , and Dbp were significantly downregulated in the SMI group during water immersion stress, and differentially expressed genes were enriched in the p38MAPK signaling pathway and p53 signaling pathway. Moreover, genes related to inflammation and apoptosis were differentially expressed. ELISA results showed that Cort, CK-MB, and Tn-Ⅰ levels were significantly higher in the SMI group compared with the NC group ( P < 0.01) and melatonin reduced the levels of Cort, CK-MB, and Tn-Ⅰ and decreased lactic acid dehydrogenase activity in rat serum. HE staining results showed that melatonin could attenuate stress-generated myocardial injury. Western blot showed that melatonin reduced the expression of p38MAPK, p53, Bax, and caspase-3 and increased the expression of Bcl-2 protein in rat heart. Melatonin can inhibit myocardial injury caused by water immersion, and its mechanism of action may be related to the regulation of the expression of circadian clock genes such as Ciart , Arnt1 , Per1 , and Dbp ; the inhibition of the expression of proapoptotic proteins such as p38MAPK, p53, Bax, and caspase-3; and the increase of the expression of Bcl-2 antiapoptotic protein.

The Role of Potassium Channels in Chronic Stress-Induced Brain Injury.

Chronic stress-induced brain injury (CSBI) is the organic damage of brain tissue caused by long-term psychological and environmental stress. However, there is no effective drug for the treatment of CSBI. The present study aimed to investigate possible mechanisms of CSBI and to explore related therapeutic targets. A rat model of CSBI was established by combining chronic restraint and cold water immersion. Our CSBI model was validated via Nissl staining, Western blotting, and behavioral tests. RNA sequencing (RNA-seq) was used to identify differentially expressed genes (DEGs) within brain tissue during CSBI. Both Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed to determine signaling pathways associated with CSBI-induced DEGs. Agonists/antagonists were used to validate the pharmacodynamics of potential therapeutic targets. A combination of chronic restraint and cold water immersion successfully induced a rat model of CSBI, as indicated by various markers of brain injury and cell apoptosis that were verified via Nissl staining, Western blotting, and behavioral tests. RNA-seq analysis identified 1131 DEGs in CSBI rats. Of these DEGs, 553 genes were up-regulated and 778 genes were down-regulated. GO and KEGG pathway analyses revealed that significant DEGs were predominantly related to membrane-bound ion channels, among which the potassium channel function was found to be significantly affected. Pharmacological experiments revealed that retigabine, a voltage-gated potassium channel opener, demonstrated a protective effect in CSBI rats. Taken together, our findings suggest that potassium channel function is disrupted in CSBI, and that potassium channel regulators may function as anti-CSBI drugs.

A signal processing application for evaluating self-monitoring blood glucose strategies in a software agent model.

We propose the signal processing technique of calculating a cross-correlation function and an average deviation between the continuous blood glucose and the interpolation of limited blood glucose samples to evaluate blood glucose monitoring frequency in a self-aware patient software agent model. The diabetic patient software agent model [1] is a 24-h circadian, self-aware, stochastic model of a diabetic patient's blood glucose levels in a software agent environment. The purpose of this work is to apply a signal processing technique to assist patients and physicians in understanding the extent of a patient's illness using a limited number of blood glucose samples. A second purpose of this work is to determine an appropriate blood glucose monitoring frequency in order to have a minimum number of samples taken that still provide a good understanding of the patient's blood glucose levels. For society in general, the monitoring cost of diabetes is an extremely important issue, and these costs can vary tremendously depending on monitoring approaches and monitoring frequencies. Due to the cost and discomfort associated with blood glucose monitoring, today, patients expect monitoring frequencies specific to their health profile. The proposed method quantitatively assesses various monitoring protocols (from 6 times per day to 1 time per week) in nine predefined categories of patient agents in terms of risk factors of health status and age. Simulation results show that sampling 6 times per day is excessive, and not necessary for understanding the dynamics of the continuous signal in the experiments. In addition, patient agents in certain conditions only need to sample their blood glucose 1 time per week to have a good understanding of the characteristics of their blood glucose. Finally, an evaluation scenario is developed to visualize this concept, in which appropriate monitoring frequencies are shown based on the particular conditions of patient agents. This base line can assist people in determining an appropriate monitoring frequency based on their personal health profile.

The self-aware diabetic patient software agent model.

This work presents a self-aware diabetic patient software agent for representing a human diabetic patient. To develop a 24h, stochastic and self-aware patient agent, we extend the original seminal work of Ackerman et al. [1] in creating a mathematical model of human blood glucose levels in three aspects. (1) We incorporate the stochastic and unpredictable effects of daily living. (2) The Ackerman model is extended into the period of night-time. (3) Patients' awareness of their own conditions is incorporated. Simulation results are quantitatively assessed to demonstrate the effectiveness of lifestyle management, such as adjusting the amount of food consumed, meal schedule, intensity of exercise and level of medication. In this work we show through the simulation that the average blood glucose can be reduced by as much as 51% due to careful lifestyle management. Self monitoring blood glucose is also quantitatively evaluated. The simulation results show that the average blood glucose is further dropped by 25% with the assistance of blood glucose samples. In addition, the blood glucose is perfectly controlled in the target range during the simulation period as a result of joint efforts of lifestyle management and self monitoring blood glucose. This study focuses on demonstrating how human patients' behavior, specifically lifestyle and self monitoring of blood glucose, affects blood glucose controls on a daily basis. This work does not focus on the insulin-glucose interaction of an individual human patient. Our conclusion is that this self-aware patient agent model is capable of adequately representing diabetic patients and of evaluating their dynamic behaviors. It can also be incorporated into a multi-agent system by introducing other healthcare components so that more interesting insights such as the healthcare quality, cost and performance can be observed.