ABSTRACT
Acupuncture was studied to investigate the mechanism of its effect on protease vitality and free radical damage in Type I CIA rats induced by type II collagen. The study divided rats into a control group (injected with physiological saline, n = 10), a model group (injected with type II collagen, n = 10), and an intervention group (injected with type II collagen + acupuncture ST36 and GB39, 3 times a week, for a total of 4 weeks, n = 10) based on the different injected drugs. Then, various indicators of the mice were experimentally tested using joint index scoring, H&E histological staining, protein blotting, and immunohistochemistry staining methods. Acupuncture ST36 and GB39 can reduce arthritis scores, histological staining scores, and increase MVD in CIA rats. And reduce protease levels, alleviate inflammation, synovial hyperplasia, and angiogenesis. In addition, the intervention group TNF-α, IL-1ß and IL-6 mRNA were reduced, and the clearance rates of hydrogen peroxide free radicals and nitric oxide free radicals were increased. The expression levels of ROS and MDA decrease, while the expression levels of SOD increase It has been proved that acupuncture at ST36 and GB39 can inhibit the release of ROS, reduce protease activity, inflammation, synovial hyperplasia, angiogenesis and free radical damage, thus reducing the severity of CIA (Collagen-Induced Arthritis) in rats.
ABSTRACT
Anxiety disorder is one of the most common mental diseases. It is mainly characterized by a sudden, recurring but indescribable panic, fear, tension and/or anxiety. Yangshendingzhi granules (YSDZ) are widely used in the treatment of anxiety disorders, but its active ingredients and underlying mechanisms are not yet clear. This study integrates network pharmacology and metabolomics to investigate the potential mechanism of action of YSDZ in a rat model of anxiety. First, potential active ingredients and targets were screened by network pharmacology. Then, predictions were verified by molecular docking, molecular dynamics and western blotting. Metabolomics was used to identify differential metabolites and metabolic pathways. All results were integrated for a comprehensive analysis. Network pharmacology analysis found that Carotene, ß-sitosterol, quercetin, Stigmasterol, and kaempferol in YSDZ exert anxiolytic effects mainly by acting on IL1ß, GABRA1, PTGS1, ESR1, and TNF targets. Molecular docking results showed that all the affinities were lower than -5 kcal/mol, and the average affinities were -7.7764 kcal/mol. Molecular dynamics simulation results showed that RMSD was lower than 2.5 A, and the overall conformational changes of proteins were small, indicating that the small molecules formed stable complexes with proteins. The results of animal experiments showed that YSDZ exerts anxiolytic effects by regulating GABRA1 and TNF-α, ameliorating pathological damage in hippocampal CA1, and regulating metabolic pathways such as thiamine, cysteine and methionine metabolism, lysine biosynthesis and degradation. Altogether, we reveal multiple mechanisms through which YSDZ exerts its anti-anxiety effects, which may provide a reference for its clinical application and drug development.
ABSTRACT
Depression is a serious psychological disorder with a rapidly increasing incidence in recent years. Clinically, selective serotonin reuptake inhibitors are the main therapy. These drugs, have serious adverse reactions, however. Traditional Chinese medicine has the characteristics of multiple components, targets, and pathways, which has huge potential advantages for the treatment of depression. The antidepressant potential of the herbal combination of Bupleurum chinense DC (Chaihu) and Paeonia lactiflora Pall (Baishao) has been extensively studied previously. In this review, we summarized the antidepressant active components and mechanism of Chaihu-Baishao herb pair. We found that it works mainly through relieving oxidative stress, regulating HPA axis, and protecting neurons. Nevertheless, current research of this combined preparation still faces many challenges. On one hand, most of the current studies only stay at the level of animal models, lacking of sufficient clinical double-blind controlled trials for further verification. In addition, studies on the synergistic effect between different targets and signaling pathways are scarce. On the other hand, this preparation has numerous defects such as poor stability, low solubility, and difficulty in crossing the blood-brain barrier.
Subject(s)
Bupleurum , Paeonia , Animals , Hypothalamo-Hypophyseal System , Pituitary-Adrenal System , Antidepressive Agents/pharmacology , Randomized Controlled Trials as TopicABSTRACT
Breast cancer is one of the main cancers that effect of the women's health. This cancer is one of the most important health issues in the world and because of that, diagnosis in the beginning and appropriate cure is very effective in the recovery and survival of patients, so image processing as a decision-making tool can assist physicians in the early diagnosis of cancer. Image processing mechanisms are simple and non-invasive methods for identifying cancer cells that accelerate early detection and ultimately increase the chances of cancer patients surviving. In this study, a pipeline methodology is proposed for optimal diagnosis of the breast cancer area in the mammography images. Based on the proposed method, after image preprocessing and filtering for noise reduction, a simple and fast tumors mass segmentation based on Otsu threshold segmentation and mathematical morphology is proposed. Afterward, for simplifying the final diagnosis, a feature extraction based on 22 structural features is utilized. To reduce and pruning the useless features, an optimized feature selection based on a new developed design of Water Strider Algorithm (WSA), called Guided WSA (GWSA). Finally, the features injected to an optimized SVM classifier based on GWSA for optimal cancer diagnosis. Simulations of the suggested method are applied to the DDSM database. A comparison of the results with several latest approaches are performed to indicate the method higher effectiveness.
Subject(s)
Breast Neoplasms , Algorithms , Breast , Breast Neoplasms/diagnostic imaging , Female , Humans , Mammography , Support Vector Machine , WaterABSTRACT
Urinary tract infection (UTI) is caused by the invasion of the pathogen in the urinary system that can manifest as symptomatic or asymptomatic bacteriuria. This study was conducted to investigate antibiotic resistance patterns, and the correlation between biofilm formations with virulence factors in uropathogenic E. coli isolates retrieved from UTI. We searched Scopus and Google Scholar, PubMed, Web of sciences for studies published in the English language between 1st 2005 to 31st December 2019. The Mesh terms and text words included "biofilms", OR "biofilm formation", AND "antibiotic resistance", OR "drug-resistance", OR "antimicrobial drug resistance", AND "urinary tract infections", OR "UTI", AND "biofilm related-genes", AND "virulence factors" AND "correlation", AND "Uropathogenic Escherichia coli", OR "Uropathogenic E. coli" AND "prevalence" AND "Iran". Data analyzed using Comprehensive Meta-Analysis (CMA) software. The random-effects model was used to calculate the pooled prevalence with 95% confidence interval (CI). The combined rates of biofilm formation in Uropathogenic E. coli (UPEC) isolates were achieved as 84.6% (95% CI: 72.7-91.9). Also, 24.8%, 26.1% and 44.6% of UPEC isolates were able to create strong, moderate and weak biofilm, respectively. The highest pooled antibiotic resistance was against Ampicillin followed by Tetracycline with resistance rates of 74.6% and 64.9%, respectively. Accordingly, some studies reported that biofilm production was significantly associated with antibiotic resistance and virulence genes (p < 0.05). This study showed a high tendency among UPEC isolates to form biofilm (more than 84%), also, most studies included in the present review reported a significant correlation between biofilm formation with antibiotic resistance and virulence factors.
