Yangzhi Peng

The associations between female infertility and epithelial ovarian cancer (EOC) or endometrial cancer (EC) have been reported in observational studies, but its causal relationship remains unknown. We intended to assess the causal effect of female infertility on EOCs and ECs using a two-sample Mendelian Randomization (MR) approach. Large pooled genome-wide association study (GWAS) datasets for female infertility (6481 cases and 68 969 controls), EOC (25 509 cases and 40 941 controls), and EC (12 906 cases and 108 979 controls) were derived from public GWAS databases and published studies. The Inverse Variance Weighted method, Weighted Median method, MR-Egger regression, and MR-Pleiotropy Residual Sum and Outlier test were adopted for MR analyses. Our results suggested that genetically predicted infertility was positively associated with the risk of EOC (OR = 1.117, 95% CI = 1.003–1.245, P = .045), but did not find a causal relationship between infertility and EC (OR = 1.081, 95% CI = 0.954–1.224, P = .223). As to the reverse direction, our study did not obtain evidence from genetics that EOCs (OR = 0.974, 95% CI = 0.825–1.150, P = .755) and ECs (OR = 1.039, 95% CI = 0.917–1.177, P = .548) were associated with an increased risk of infertility. This large MR analysis supported a causal association between female infertility and increased risk of EOCs, but did not find a causal relationship between infertility and ECs. What is already known on this topic: Many studies have revealed that some people are more susceptible to undergoing cancers than others. Understanding the risk drivers that confer cancer risk will provide vital information to help public health officials make decisions that will benefit these at-risk populations. Observational studies have shown that female infertility is a high risk factor for ovarian cancer and endometrial cancer (EC), but the causal relationship has not been established. What this study adds: Our two-sample Mendelian Randomization study supported a causal association between female infertility and increased risk of epithelial ovarian cancers (EOCs), but did not find a causal relationship between infertility and ECs. No clear evidence was found for a potential causal effect of EOCs and ECs on female infertility. How this study might affect research, practice, or policy Our findings suggested that patients with infertility were at higher risk of developing EOC compared to the general population. Increased knowledge and regular surveillance of EOC in infertile women may help to identify early EOC.

Background. The practice of traditional Chinese medicine (TCM) began several thousand years ago, and the knowledge of practitioners is recorded in paper and electronic versions of case notes, manuscripts, and books in multiple languages. Developing a method of information extraction (IE) from these sources to generate a cohesive data set would be a great contribution to the medical field. The goal of this study was to perform a systematic review of the status of IE from TCM sources over the last 10years. Methods. We conducted a search of four literature databases for articles published from 2010 to 2021 that focused on the use of natural language processing (NLP) methods to extract information from unstructured TCM text data. Two reviewers and one adjudicator contributed to article search, article selection, data extraction, and synthesis processes. Results. We retrieved 1234 records, 49 of which met our inclusion criteria. We used the articles to (i) assess the key tasks of IE in the TCM domain, (ii) summarize the challenges to extracting information from TCM text data, and (iii) identify effective frameworks, models, and key findings of TCM IE through classification. Conclusions. Our analysis showed that IE from TCM text data has improved over the past decade. However, the extraction of TCM text still faces some challenges involving the lack of gold standard corpora, nonstandardized expressions, and multiple types of relations. In the future, IE work should be promoted by extracting more existing entities and relations, constructing gold standard data sets, and exploring IE methods based on a small amount of labeled data. Furthermore, fine-grained and interpretable IE technologies are necessary for further exploration.