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.
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 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 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.
As a valid method in systematic review, dose-response meta-analysis is widely used in investigating the relationship between independent variable and dependent variable, and which usually based on observational studies. With large sample size, observational studies can provide a reasonable amount of statistical power for meta-analysis. However, due to the design defects of observational studies, they tend to introduce many kinds of biases, which may influence the final results that make them deviation from the truth. Given the dead zone of methodology, there is no any bias adjusting method in dose-response meta-analysis. In this article, we will introduce some bias adjusting methods from other observational-study-based meta-analysis and make them suit for dose-response meta-analysis, and then compare the advantages and disadvantages of these methods.
ObjectiveTo assess the causal relationship between cervical vertebra related disorders and essential hypertension using a bidirectional two-sample Mendelian randomization study approach. MethodsThe research data comes from the genome-wide association study dataset. Four types of cervical vertebra related disorders: cervicalgia, cervical disc disorders, cervical root disorders, injury of nerves and spinal cord at neck level, as well as data on essential hypertension, were selected for the study. Relevant single nucleotide polymorphisms were selected as instrumental variables to assess the causal relationship between cervical vertebra related disorders and essential hypertension mainly by inverse variance weighted model ratio. Cochran's Q test was used to detect heterogeneity, MR-Egger intercept term and MR-PRESSO was used to detect multiplicity, and leave-one-out method was used for sensitivity analysis. ResultsCervicalgia had a positive causal relationship with the essential hypertension (OR=1.01, 95%CI 1.00 to1.02, P=0.019). Essential hypertension had a positive causal relationship with the cervical disc disorders (OR=4.08, 95%CI 1.57 to10.61, P=0.004). There was no significant causal relationship between cervical root disorders, injury of nerves and spinal cord at neck level and essential hypertension. Reliability assessment indicates that the study results were reliable. ConclusionCervicalgia is a risk factor for essential hypertension; Essential hypertension is a risk factor for cervical disc lesions; There is no correlation between cervical root disorders, injury of nerves and spinal cord at neck level and essential hypertension.
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.
Objective To evaluate the potential causal relationship between asthma and the risk of gastroesophageal reflux disease (GERD) using a two-sample Mendelian randomization study. Methods A large sample of genome-wide association study was used to summarize the data, and the genetic loci [single nucleotide polymorphisms (SNPs)] closely related to asthma were selected as instrumental variables, and Mendelian randomization analysis was conducted by inverse variance weighting, weighted median and MR-Egger method, respectively. At the same time, the multi-effect of MR-Egger was detected and the sensitivity analysis was carried out by Leave-one-out method to ensure the robustness of the results. Results A total of 77 SNPs closely related to asthma were selected as instrumental variables. The results of inverse variance weighted analysis showed a significant positive correlation between asthma and the occurrence of gastroesophageal reflux disease [odds ratio (OR)=1.044, 95% confidence interval (CI) (1.006, 1.083), P=0.024]. Weighted median results showed similar causality [OR=1.075, 95%CI (1.021, 1.133), P=0.006]. The MR-Egger regression results showed that there was a positive correlation between asthma and GERD, but there was no statistical significance [OR=1.080, 95%CI (0.983, 1.187), P=0.115]. The heterogeneity test results showed that there was no heterogeneity in the causal relationship between asthma and GERD (P>0.05). The results of the horizontal pleiotropy test showed that there was no horizontal pleiotropy in SNPs (P>0.05). The results of the retention test showed that no SNPs with significant impact on the results were detected. Conclusion There is a positive causal relationship between asthma and GERD.
ObjectiveThyroid nodules are an exceptionally common thyroid disorder. Past studies suggested a possible link between thyroid diseases and breast neoplasms. However, few studies have delved into the causal relationship between thyroid nodules and breast neoplasms. This study conducted a Mendelian randomization (MR) analysis to further investigate the causal relationship between them. MethodsThis study was conducted using data sourced from genome-wide association study (GWAS) summary datasets. The study focused on thyroid nodules, benign breast tumors, and malignant breast cancers as the research objects, and relevant single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs). The inverse-variance weighted (IVW) was primarily used to assess the causal relationship between thyroid nodules and breast neoplasms. Cochran’s Q test was employed to detect heterogeneity, while MR-Egger intercept and MR-PRESSO were used to test for pleiotropy. Sensitivity analysis was conducted using the leave-one-out method. ResultsThere was a significant causal relationship between thyroid nodules and malignant neoplasm of breast (OR=0.88, 95%CI 0.83 to 0.95, P<0.01), with no evidence of reverse causality between them (OR=1.01, 95%CI 0.99 to 1.03, P=0.16). No causal relationship was found between thyroid nodules and benign neoplasm of breast, as indicated by both forward MR analysis (OR=0.97, 95%CI 0.89 to 1.06, P=0.51) and reverse MR analysis (OR=0.97, 95%CI 0.92 to 1.04, P=0.40). Sensitivity analyses suggested that the study findings were accurate and reliable. ConclusionThe present study identifies thyroid nodules as a potential protective factor for malignant neoplasm of breast.
Dose-response meta-analysis, an important tool in investigating the relationship between a certain exposure and risk of disease, has been increasingly applied. Traditionally, the dose-response meta-analysis was only modelled as linearity. However, since the proposal of more powerful function models, which contains both linear, quadratic, cubic or more higher order term within the regression model, the non-linearity model of dose-response relationship is also available. The packages suit for R are available now. In this article, we introduced how to conduct a dose-response meta-analysis using dosresmeta and mvmeta packages in R.