Objective Cytochrome P450 1B1 (CYP1B1) genetic variants are relevant in the pathogenesis of breast cancer. Exploring the relationships between CYP1B1 functional variants and breast cancer could improve our understanding of breast cancer molecular pathophysiology. Methods This is a two-stage hospital-based case–control study of a Chinese Han population. Genotyping was performed to identify candidate gene variants. 3DSNP, ANNOVAR, and RegulomeDB were used to determine functional single nucleotide polymorphisms (SNPs). The relationship between candidate variants and breast cancer risk was evaluated through unconditional logistic regression analysis. The PancanQTL platform was used to perform cis and trans expression quantitative trait loci (eQTL) analysis of positive SNPs. The GSCA platform was then used to compare the gene expression levels of potential target genes between breast cancer tissue and normal tissue adjacent to the cancer. Results rs10175368-T acted as a protective factor against breast cancer based on an additive model [odds ratio (OR) = 0.722, 95% confidence interval (CI) = 0.613–0.850; P < 0.001], and was identified as a protective factor in the postmenopausal population (OR = 0.601; 95% CI, 0.474–0.764; P < 0.001). eQTL analysis and analysis of differential expression in carcinoma and paracancerous tissues revealed that the expression level of CYP1B1-AS1 was associated with rs10175368 and that CYP1B1-AS1 had significantly higher expression levels in breast cancer tissues than in paracancerous tissues. Conclusion We show, for the first time in a Chinese Han population, that the functional variant rs10175368 plays a protective role against breast cancer, especially in the postmenopausal population.

Background The COVID-19 pandemic led many educational institutions to shift to online courses, making blended education a significant trend in teaching. We examined the effectiveness of blended learning in an evidence-based medicine course.Methods We compared the examination scores of a blended learning group, an online only group, and a traditional offline group and conducted a questionnaire survey on students’ preferences for different learning modes and the reasons for their preferences. A total of 2100 undergraduate students in clinical medicine were included in this cross-sectional study. Examination results were collected, and questionnaires were administered to the study participants. We compared the mean theoretical scores and exam pass rates of the three teaching groups using ANOVA and c2test for multiple comparisons.Results The blended group’s theoretical scores and pass rate were significantly higher than those of the offline and online groups. Furthermore, 71.6% preferred the blended teaching mode. Most students believed that blended teaching was the most effective mode—offline education: 7.86%; online education: 26.14%; blended education: 66%. Subsequently, in a questionnaire administered to a blended group of students, their foremost reason for liking online instruction was ‘flexible in time and space’ (99%), followed by ‘can be viewed repeatedly, facilitating a better understanding of knowledge points’ (98%). Their foremost reason for liking offline teaching was ‘helps to create a good learning atmosphere’ (97%), followed by ‘teachers can control students’ learning status in real time’ (89%).Conclusions This study explored the effectiveness of learning in evidence-based medicine courses by comparing the learning outcomes and personal perceptions of three different teaching modes. This is the first cross-sectional study in which three different teaching models are compared and discussed in an evidence-based medicine course. We also elaborate on the specific instructional protocols for each model. This study shows that using a blended education approach in evidence-based medicine courses can improve students’ learning motivation, autonomy, and satisfaction. It also enhances instructional efficiency, thereby improving students’ understanding of the course content.