Objective To investigate the effects of QUE on proliferation and DNA synthesis of cultured retinal pigment epithelium(RPE) cells with or without EGF. Methods With or without EGF, cultured RPE cells were treated with QUE by various concentrations(200,100,50,1mu;mol/L) and with QUE 200mu;mol/L at different times(24-168 hr), cells proliferation and DNA synthesis were evaluated by cell count method and the uptake of thymidine. The viability of cells was determined by trypanblue exclusion. Results The best concentration of QUE which inhibits proliferation and DNA synthesis of PRE cells was 200mu;mol/L. The significant inhibition effect of QUE occurred at 48hr, and the best inhibition of QUE occurred at 96hr. QUE had more powerful effect of antiproliferation on RPE cells, and the viability of RPE cells was over85%. Conclusion The results suggested that QUE could inhibit the proliferation of RPE cells in a dose-dependent and time-dependent manner, especially inhibit the proliferation induced by EGF stimulating. QUE had no cyto-toxic effect on RPE cells cultured in vitro. (Chin J Ocul Fundus Dis,1999,15:27-29)
Objective To analyze the relationship between neuroticism and gastroesophageal reflux disease (GERD) using the Mendelian randomization (MR) method. Methods Exposure and outcome data were downloaded from the Integrative Epidemiology Unit (IEU) database in August 2023, including summary statistics from genome-wide association studies (GWAS) for neuroticism (n=374 323) and GERD (n=602 604). MR was conducted using the weighted median method, MR-Egger method, inverse variance weighted method, weighted mode method, and simple mode method. The causal relationship between the two was assessed using odds ratio (OR), and sensitivity analyses were performed to ensure the accuracy of the results. ResultsNeuroticism was associated with an increased risk of GERD [OR=1.229, 95%CI (1.186, 1.274), P<0.001]. Similarly, GERD was associated with an increased risk of neuroticism [OR=1.786, 95%CI (1.623, 1.965), P<0.001]. Conclusion There is a bidirectional causal relationship between neuroticism and gastroesophageal reflux disease.
Objective To analyze the potential causal relationship between sunscreen/ultraviolet protection and the risk of non-Hodgkin lymphoma using a two sample Mendelian randomization (MR) study method. Methods The summary data of genome-wide association study was used to select three types of non-Hodgkin lymphoma, namely diffuse large B-cell lymphoma (DLBCL), follicular lymphoma, T/NK cell lymphoma, and sunscreen/ultraviolet protection highly correlated genetic loci, namely single nucleotide polymorphism (SNP), as instrumental variables. The reverse variance weighting method was used as the main method for MR analysis, MR Egger and MR-PRESO were used to detect level pleiotropy, and leave-one-out method was used for sensitivity analysis to ensure the robustness of the results. Results A total of 132 SNPs were included in the analysis. The results of the inverse variance weighted analysis showed that sunscreen/ultraviolet protection increased the incidence of DLBCL [odds ratio=2.439, 95% confidence interval (1.109, 5.362), P=0.027]. The heterogeneity test results showed that there was no heterogeneity in the causal relationship between sunscreen/ultraviolet protection and DLBCL (P>0.05). The results of the horizontal pleiotropy test showed that SNP did not exhibit horizontal pleiotropy (P>0.05). The leave-one-out method showed that no SNP with a significant impact on the results was found. There was no causal relationship between sunscreen/ultraviolet protection and follicular lymphoma and T/NK cell lymphoma. Conclusion There is a positive causal relationship between sunscreen/ultraviolet protection and the incidence of DLBCL.
The β-secretase is one of prospective targets against Alzheimer's disease (AD). A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of Hydroethylamines (HEAs) as β-secretase inhibitors was established using Topomer CoMFA. The multiple correlation coefficient of fitting, cross validation and external validation were r2=0.928, qloo2=0.605 and rpred2=0.626, respectively. The 3D-QSAR model was used to search R groups from ZINC database as the source of structural fragments. As a result, a series of R groups with relatively high activity contribution was obtained to design a total of 15 new compounds, with higher activity than that of the template molecule. The molecular docking was employed to study the interaction mode between the new compounds as ligands and β-secretase as receptors, displaying that hydrogen bond and hydrophobicity played important roles in the binding affinity between the new compounds and β-secretase. The results showed that Topomer CoMFA and Topomer Search could be effectively used to screen and design new molecules of HEAs as β-secretase inhibitors, and the designed compounds could provide new candidates for drug design targeting AD.
Objective To explore the relationship between the gut microbiome (GM) and psoriasis using a two-sample two-way Mendelian randomization (MR) approach. Methods The forward analysis uses the gut microbiota as the exposure factor, and its genetic data are derived from the genome-wide association study dataset published by the MiBioGen consortium. Psoriasis was used as the outcome variable, and its genetic data were obtained from the UK Biobank. The reverse MR analysis, on the other hand, took psoriasis as the exposure and the specific gut microbiota taxonomic units identified in the forward analysis as the outcome variable. MR analysis was conducted using maximum likelihood, MR Egger regression, weighted median, inverse variance weighting (IVW), and weighted models to study the causal relationship between the gut microbiota and psoriasis. Then, sensitivity analyses including horizontal pleiotropy test, Cochran’s Q test, and leave-one-out analysis were used to evaluate the reliability of the results. Results A total of 51 single nucleotide polymorphisms from 5 fungi were included in the forward study. The forward IVW analysis results showed that, the class Mollicutes [odds ratio (OR)=1.003, 95% confidence interval (CI) (1.001, 1.006), P=0.004], genus Lachnospiraceae FCS020 group [OR=1.003, 95%CI (1.000, 1.006), P=0.041], and phylum Tenericutes [OR=1.003, 95%CI (1.001, 1.006), P=0.004] were causally associated with an increased risk of psoriasis. The family Victivallaceae [OR=0.998, 95%CI (0.997, 1.000), P=0.005] and order Pasteurellales [OR=0.998, 95%CI (0.996, 1.000), P=0.047] were also linked to a decreased risk of psoriasis. The results of the sensitivity analysis were robust. There was no evidence of a reverse causal relationship from psoriasis to the identified bacterial taxa found in the results of reverse MR analysis results. Conclusions The abundance of three species, class Mollicutes, genus Lachnospiraceae and phylum Tenericutes, may increase the risk of psoriasis. The abundance of two species, family Victivallaceae and order Pasteurellales may reduce the risk of psoriasis. These results provide new directions for the prevention and treatment of psoriasis in the future, but further research is needed to explore how the aforementioned microbiome affects the progression of psoriasis.
ObjectiveTo provide references in the forensic identification of injury and cerebrovascular malformation involved death cases, and to reduce the relevant medical dispute by exploring the forensic pathological features, identification of medical dispute as well as relationship between injury and disease. MethodsWe collected 33 cases of cerebrovascular malformation from January 2006 to December 2014 in West China Center of Forensic Medicine, including details of cases, clinical medical record and forensic pathology examination, and then the cases were retrospectively analyzed. ResultsIn the 33 cases, the average age of the individuals was 37.4 years old, and the male/female ratio was 23/10. Nineteen patients (57.6%) died within 1 hour. Seventeen patients with mixed pathological type of cerebrovascular malformation dominated (51.5%). Medical dispute happened in 7 cases (21.1%), 4 of which were identified to be led by medical fault and 3 with no medical fault. Relationship between injury and disease was analyzed in 11 cases (33.3%), in which injury was identified to take full responsibility in 1 case, inductive cause of death in 9 cases, and no relationship between injury and death in 1 case. ConclusionComprehensive and systematic investigation of forensic pathology plays an important role in the proper settlement of medical disputes as well as the identification of cause of death and relationship between injury and disease.
Objective To explore the factors which affect shared decision-making and develop strategies to get patients actively involved in clinical decision-making. Methods We conducted a survey on 566 patients of a Class A Hospital in Sichuan with group random sampling method. The data were collected by the use of anonymous selfadministered questionnaires. We used SPSS 10.0 to analyse the data. Results A total of 600 questionnaires were distributed at random, of which 565 were completed. There were 68% patients who had some knowledge of the disease, and 93% who were willing to participate in clinical decision-making. The patients’ biggest concerns were: treatment effect, cost and doctors’ skills. The biggest difficulties that patients worried about were: long-time waiting in out-patient departments and limited time to communicate with doctors. Conclusion As more and more patients would like to involve in shared decision-making, doctors need to provide patients with more choices and help them make a right decision in their treatment.
ObjectiveTo explore the potential causal relationship between 91 inflammatory factors and the risk of lung cancer (LC). MethodsBy extracting related data of inflammatory factors and LC and its subtypes from public databases of genome-wide association studies (GWAS), bidirectional, repeated, multivariable Mendelian randomization (MR) and subgroup MR methods were used for analysis. The inverse variance weighted method was mainly used for causal inference, and a series of sensitivity analyses were applied to verify the strength of the results. ResultsHigher levels of CD5, interleukin-18 (IL-18), and oncostatin-M (OSM) were causally associated with a lower risk of LC, while nerve growth factor-β (NGF-β) and S100 calcium-binding protein A12 (S100A12) were associated with an increased risk of LC. Subgroup MR analysis results showed that IL-18 had a causal relationship with a reduced risk of lung adenocarcinoma, while NGF-β and S100A12 had a causal relationship with an increased risk of lung adenocarcinoma; CD5 and OSM had a causal relationship with a reduced risk of lung squamous cell carcinoma; NGF-β had a causal relationship with an increased risk of small cell lung cancer. ConclusionFive inflammatory factors, including CD5, IL-18, OSM, NGF-β, and S100A12 have a causal correlation with the risk of LC, providing potential targets for early screening of LC patients and development of therapeutic drugs.
The medical literature contains a wealth of valuable medical knowledge. At present, the research on extraction of entity relationship in medical literature has made great progress, but with the exponential increase in the number of medical literature, the annotation of medical text has become a big problem. In order to solve the problem of manual annotation time such as consuming and heavy workload, a remote monitoring annotation method is proposed, but this method will introduce a lot of noise. In this paper, a novel neural network structure based on convolutional neural network is proposed, which can solve a large number of noise problems. The model can use the multi-window convolutional neural network to automatically extract sentence features. After the sentence vectors are obtained, the sentences that are effective to the real relationship are selected through the attention mechanism. In particular, an entity type (ET) embedding method is proposed for relationship classification by adding entity type characteristics. The attention mechanism at sentence level is proposed for relation extraction in allusion to the unavoidable labeling errors in training texts. We conducted an experiment using 968 medical references on diabetes, and the results showed that compared with the baseline model, the present model achieved good results in the medical literature, and F1-score reached 93.15%. Finally, the extracted 11 types of relationships were stored as triples, and these triples were used to create a medical map of complex relationships with 33 347 nodes and 43 686 relationship edges. Experimental results show that the algorithm used in this paper is superior to the optimal reference system for relationship extraction.
Dose-response relationship model has been widely used in epidemiology studies, as well as in evidence-based medicine area. In dose-response meta-analysis, the results are highly depended on the raw data. However, many primary studies did not provide sufficient data and led the difficulties in data analysis. The efficiency and response rate of collecting the raw data from original authors were always low, thus, evaluating and transforming the missing data is very important. In this paper, we summarized several types of missing data, and introduced how to estimate the missing data and transform the effect measure using the existed information.