This Matters Arising article critically examines the study “Genetic susceptibility association between viral infection and colorectal cancer risk: a two-sample Mendelian randomization analysis” by Li et al., highlighting both its contributions and methodological limitations. Their study employed two-sample Mendelian randomization (MR) to explore potential causal links between viral infections and colorectal cancer (CRC), identifying significant associations with infections such as herpes simplex virus and measles. However, several aspects of the methodology warrant scrutiny, including the relaxation of instrumental variable selection thresholds, the handling of potential pleiotropy, and the interpretation of biologically implausible findings. While leveraging advanced MR techniques such as MR-RAPS, cML, ConMix, and dIVW to address challenges like pleiotropy and weak instruments, the study encountered issues related to heterogeneity, insufficient exploration of biological plausibility, and a lack of detailed reporting on instrumental variable (IV) selection and preprocessing. This Matters Arising calls for more rigorous sensitivity analyses, improved transparency in IV selection criteria and harmonization of genome-wide association study (GWAS) datasets, particularly in addressing differences between self-reported and clinically diagnosed infections. Additionally, the Matters Arising article calls for a deeper exploration of biological mechanisms, such as the role of immune modulation and inflammation, to better interpret the observed associations. By addressing these limitations, future MR studies can enhance methodological rigor, improve reproducibility, and provide more robust insights into the causal pathways linking viral infections to CRC risk.

The role identification in social networks is important for analyzing and understanding social networks, predicting user behavior, and researching relationships and interactions between users. Most role identification works are based on link analysis following the ideas of PageRank and HITS, and some methods also combing link analysis and content analysis. However, previous role identification methods focus on the amount of topic released by the target user without considering the importance of the topic. In order to identify high-value users by topic weights in social networks, this paper first proposes a method of assigning topic weights based on the LDA model and the collective credit allocation method in science(CCA). Then we introduce content value density based on the Kullback-Leibler divergence of topic weight and topic distribution to rank the high-value users. In addition, the new method is more suitable for calculation on large-scale networks and time-evolution networks because the new method is an inductive method which works on the local graph. Finally, the experiments are carried out to analyze the effect of the topic weight allocation and content value density in a real data set. The results show that the new method is superior to the compared methods when identifying high-value users in certain circumstances.