Objective To investigate the protective effect of protocatechuic acid(PCA)on the PC12 cell model of Alzheimer’s disease(AD)and to explore its mechanism . Methods Amyloid beta peptide 1-42(Aβ1-42)fiber polymers were identified by immunofluorescence. After PC12 cells were stimulated with the Aβ1-42 fiber polymers, the cellular morphology was observed at different time points of hour 0, 3, 6, 9, 12, 24 , and the cellular viability was tested by methyl thiazolyl tetrazolium(MTT)assay to monitor the modeling condition. The effect of PCA on PC12 cells was detected after PC12 cells were pretreated with the different contentions of PCA. Autophagy-related marker Beclin1 protein level was detected by Western blotting method to investigate the protective mechanism of PCA. Results Aggregated white Aβ1-42 mass was stable at hour 12 and 24, and showed no significant difference between the two time points, the cell damage rate being 40%. Therefore, we defined culturing time being 12 and 24 hours as the modeling condition of AD model. The cell viability was increased with 200-800 μmol/L of PCA after culturing for 24 hours(P<0.01) , and the Western blotting results showed that the Beclin1 protein expression was up-regulated by PCA. Conclusion PCA prevents PC12 cells from Aβ1-42-induced toxicity, the mechanism being related with the increase of cellular autophagy.

Human-derived lysozyme is a general term for a group of naturally occurring alkaline proteins in the human body that are capable of lysing bacterial cell walls. Its action is characterized by its ability to cleave the β-(1,4)-glycosidic bond between N-acetylglucosamine and N-acetylmuramic acid in peptidoglycan. Human-derived lysozyme has a variety of properties such as antibacterial, anti-inflammatory, antiviral and immune enhancing, and is therefore widely used in the domestic and international pharmaceutical markets. This review summarizes the structural features, expression sites, biological functions of human-derived lysozymes and its market applications.
Humans ; Muramidase ; Anti-Bacterial Agents

Objective To establish simple screening models for nonalcoholic fatty liver disease (NAFLD) in the adult Blang population. Methods Based on the survey data of metabolic diseases in the Blang people aged 18 years or above in 2017, 2993 respondents were stratified by sex and age (at an interval of 5 years) and then randomly divided into modeling group with 1497 respondents and validation group with 1496 respondents. Related information was collected, including demographic data, smoking, drinking, family history of diseases and personal medical history, body height, body weight, waist circumference, and blood pressure, and related markers were measured, including fasting plasma glucose, 2-hour postprandial plasma glucose or blood glucose at 2 hours after glucose loading, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transpeptidase. The chi-square test was used for comparison of categorical data between two groups. Logistic regression analysis was used to establish the screening model. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, and negative predictive value were used to evaluate the screening performance of established models versus existing models in the study population, and the DeLong method was used for comparison of AUC. Results Three screening models for NAFLD were established based on physical and biochemical measurements, i.e., simple noninvasive model 1 (age, body mass index, and waist circumference), noninvasive model 2 with the addition of blood pressure, and model 3 with the combination of hematological parameters (diabetes and ALT/AST). In the modeling group, the three models had an AUC of 0.881 (95% confidence interval [ CI ]: 0.864-0.897), 0.892 (95% CI : 0.875-0.907), and 0.894 (95% CI : 0.877-0.909), respectively, and there was a significant difference between model 1 and models 2/3 ( P =0.004 0 and P < 0.001); in the validation group, the three models had an AUC of 0.891 (95% CI : 0.874-0.906), 0.892 (95% CI : 0.875-0.907), and 0.893 (95% CI : 0.876-0.908), respectively, and there was no significant difference between the three groups ( P > 0.05). Based on the overall consideration of screening performance, invasiveness, and cost, the simple noninvasive model 1 was considered the optimal screening model for NAFLD in this population. Model 1 had the highest Youden index at the cut-off value of 5 points, and when the score of ≥5 points was selected as the criteria for NAFLD, the model had a sensitivity of 86.5%, a specificity of 79.7%, a positive predictive value of 50.3%, and a negative predictive value of 96.1% in the modeling group and a sensitivity of 85.6%, a specificity of 80.6%, a positive predictive value of 51.7%, and a negative predictive value of 95.8% in the validation group. Conclusion The NAFLD screening models established for the adult Blang population based on age and obesity indicators have relatively higher sensitivity, specificity, and negative predictive value, and this tool is of important practical significance for the intervention of NAFLD and its closely related metabolic diseases in this population.