Identification of Potential Biomarkers and Metabolic Profiling of Serum in Ovarian Cancer Patients Using UPLC/Q-TOF MS

Background/Aims: Ovarian cancer (OC) is a malignant neoplasm of the female reproductive system with a high mortality rate. Identifying useful biomarkers and clarifying the molecular pathogenesis of OC are critical for early diagnosis and treatment. The aim of the study was to identify candidate biomarkers and explore metabolic changes of OC. Methods: A two-stage design was used in our study, with a discovery cohort of OC cases (n = 30) and controls (n = 30) and an independent cohort of cases (n = 17) and controls (n = 18) for validation. The serum metabolic profiling was investigated by ultra-performance liquid chromatography and quadrupole time-of-fight mass spectrometry with positive electrospray ionization. Results: A total of 18 metabolites closely related to OC were identified in the discovery stage, of which 12 were confirmed in the validation cohort. Metabolic pathways in OC related to these biomarkers included fatty acid β-oxidation, phospholipid metabolism, and bile acid metabolism, which are closely related to the proliferation, invasion, and metastasis of cancer cells. Multiple logistic regression analysis of these metabolites showed that 2-piperidinone and 1-heptadecanoylglycerophosphoethanolamine were potential biomarkers of OC, with high sensitivity (96.7%), specificity (66.7%), and area under the receiver operating characteristic curve value (0.894). Conclusion: These findings provide insight into the pathogenesis pathogenesis of OC and may be useful for clinical diagnosis and treatment

Efficient Combination of Complex Chromatography, Molecular Docking and Enzyme Kinetics for Exploration of Acetylcholinesterase Inhibitors from Poria cocos

Poria cocos (P. cocos) is a traditional Chinese medicinal product with the same origin as medicine and food. It has diuretic, anti-inflammatory and liver protection properties, and has been widely used in a Chinese medicine in the treatment of Alzheimer’s disease (AD). This study was conducted to explore the activity screening, isolation of acetylcholinesterase inhibitors (AChEIs), and in vitro inhibiting effect of P. cocos. The aim was to develop a new extraction process optimization method based on the Matlab genetic algorithm combined with a traditional orthogonal experiment. Moreover, bio−affinity ultrafiltration combined with molecular docking was used to screen and evaluate the activity of the AChEIs, which were subsequently isolated and purified using high-speed counter−current chromatography (HSCCC) and semi−preparative high-performance liquid chromatography (semi−preparative HPLC). The change in acetylcholinesterase (AChE) activity was tested using an enzymatic reaction kinetics experiment to reflect the inhibitory effect of active compounds on AChE and explore its mechanism of action. Five potential AChEIs were screened via bio−affinity ultrafiltration. Molecular docking results showed that they had good binding affinity for the active site of AChE. Meanwhile, the five active compounds had reversible inhibitory effects on AChE: Polyporenic acid C and Tumulosic acid were non-competitive inhibitors; 3−Epidehydrotumulosic acid was a mixed inhibitor; and Pachymic acid and Dehydrotrametenolic acid were competitive inhibitors. This study provided a basis for the comprehensive utilization of P. cocos and drug development for the treatment of AD

Efficient Combination of Complex Chromatography, Molecular Docking and Enzyme Kinetics for Exploration of Acetylcholinesterase Inhibitors from <i>Poria cocos</i>

Poria cocos (P. cocos) is a traditional Chinese medicinal product with the same origin as medicine and food. It has diuretic, anti-inflammatory and liver protection properties, and has been widely used in a Chinese medicine in the treatment of Alzheimer’s disease (AD). This study was conducted to explore the activity screening, isolation of acetylcholinesterase inhibitors (AChEIs), and in vitro inhibiting effect of P. cocos. The aim was to develop a new extraction process optimization method based on the Matlab genetic algorithm combined with a traditional orthogonal experiment. Moreover, bio−affinity ultrafiltration combined with molecular docking was used to screen and evaluate the activity of the AChEIs, which were subsequently isolated and purified using high-speed counter−current chromatography (HSCCC) and semi−preparative high-performance liquid chromatography (semi−preparative HPLC). The change in acetylcholinesterase (AChE) activity was tested using an enzymatic reaction kinetics experiment to reflect the inhibitory effect of active compounds on AChE and explore its mechanism of action. Five potential AChEIs were screened via bio−affinity ultrafiltration. Molecular docking results showed that they had good binding affinity for the active site of AChE. Meanwhile, the five active compounds had reversible inhibitory effects on AChE: Polyporenic acid C and Tumulosic acid were non-competitive inhibitors; 3−Epidehydrotumulosic acid was a mixed inhibitor; and Pachymic acid and Dehydrotrametenolic acid were competitive inhibitors. This study provided a basis for the comprehensive utilization of P. cocos and drug development for the treatment of AD

Evaluating the Spatial Risk of Bacterial Foodborne Diseases Using Vulnerability Assessment and Geographically Weighted Logistic Regression

Foodborne diseases are an increasing concern to public health; climate and socioeconomic factors influence bacterial foodborne disease outbreaks. We developed an “exposure–sensitivity–adaptability” vulnerability assessment framework to explore the spatial characteristics of multiple climatic and socioeconomic environments, and analyzed the risk of foodborne disease outbreaks in different vulnerable environments of Zhejiang Province, China. Global logistic regression (GLR) and geographically weighted logistic regression (GWLR) models were combined to quantify the influence of selected variables on regional bacterial foodborne diseases and evaluate the potential risk. GLR results suggested that temperature, total precipitation, road density, construction area proportions, and gross domestic product (GDP) were positively correlated with foodborne diseases. GWLR results indicated that the strength and significance of these relationships varied locally, and the predicted risk map revealed that the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas (60.6%) than rural areas (20.1%). Finally, distance from the coastline was negatively correlated with predicted regional risks. This study provides a spatial perspective for the relevant departments to prevent and control foodborne diseases

Evaluating the Spatial Risk of Bacterial Foodborne Diseases Using Vulnerability Assessment and Geographically Weighted Logistic Regression

Foodborne diseases are an increasing concern to public health; climate and socioeconomic factors influence bacterial foodborne disease outbreaks. We developed an &ldquo;exposure&ndash;sensitivity&ndash;adaptability&rdquo; vulnerability assessment framework to explore the spatial characteristics of multiple climatic and socioeconomic environments, and analyzed the risk of foodborne disease outbreaks in different vulnerable environments of Zhejiang Province, China. Global logistic regression (GLR) and geographically weighted logistic regression (GWLR) models were combined to quantify the influence of selected variables on regional bacterial foodborne diseases and evaluate the potential risk. GLR results suggested that temperature, total precipitation, road density, construction area proportions, and gross domestic product (GDP) were positively correlated with foodborne diseases. GWLR results indicated that the strength and significance of these relationships varied locally, and the predicted risk map revealed that the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas (60.6%) than rural areas (20.1%). Finally, distance from the coastline was negatively correlated with predicted regional risks. This study provides a spatial perspective for the relevant departments to prevent and control foodborne diseases