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1. Body surface area, height, and body fat percentage as more sensitive risk factors of cancer and cardiovascular disease

2. Causal effect of sex hormone-binding globulin and testosterone on coronary heart disease: A multivariable and network Mendelian randomization analysis

3. [Evaluating the causal relationship between hip circumference and coronary heart disease: a Mendelian randomization study]

4. Smoking, heavy drinking, physical inactivity, and obesity among middle-aged and older adults in China: cross-sectional findings from the baseline survey of CHARLS 2011–2012

5. Roles for circulating polyunsaturated fatty acids in ischemic stroke and modifiable factors: a Mendelian randomization study

6. Whole blood viscosity is an independent early predictor for metabolic syndrome and its components in men: A prospective cohort study in Northern Chinese population

7. Additional file 3 of Roles for circulating polyunsaturated fatty acids in ischemic stroke and modifiable factors: a Mendelian randomization study

8. Additional file 1 of Smoking, heavy drinking, physical inactivity, and obesity among middle-aged and older adults in China: cross-sectional findings from the baseline survey of CHARLS 2011–2012

9. Egg intake in extremely undernourished Chinese women during reproductive age and subsequent nonfatal perimenopausal coronary events

10. Prevalence and risk factors associated with hardcore smoking among rural daily male smokers

11. Assessment of temperature effect on childhood hand, foot and mouth disease incidence (0–5 years) and associated effect modifiers: A 17 cities study in Shandong Province, China, 2007–2012

12. Research progress and surface/interfacial regulation methods for electrophotocatalytic hydrogen production from water splitting

13. Risk Factors Associated with Precancerous Lesions of Esophageal Squamous Cell Carcinoma: a Screening Study in a High Risk Chinese Population

14. G protein-coupled estrogen receptor is involved in modulating colonic motor function via nitric oxide release in C57BL/6 female mice

15. The Impact of Ambient Temperature on Childhood HFMD Incidence in Inland and Coastal Area: A Two-City Study in Shandong Province, China

16. Detecting the association between meteorological factors and hand, foot, and mouth disease using spatial panel data models

17. Epidemiological Characteristics and Spatial-Temporal Clusters of Mumps in Shandong Province, China, 2005–2014

19. Additional file 2: Figure S1. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

20. Additional file 11: Figure S10 of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

21. Additional file 3: Figure S2. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

22. Additional file 4: Figure S3. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

23. Additional file 10: Figure S9. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

24. Additional file 6: Figure S5. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

25. Additional file 4: Figure S3. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

26. Additional file 12: Figure S11. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

27. Additional file 8: Figure S7. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

28. Additional file 8: Figure S7. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

29. Additional file 3: Figure S2. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

30. Additional file 9: Figure S8. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

31. Additional file 2: Figure S1. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

32. Additional file 7: Figure S6 of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

33. Additional file 9: Figure S8. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

34. Additional file 7: Figure S6 of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

35. Additional file 6: Figure S5. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

36. Additional file 5: Figure S4. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

37. Additional file 5: Figure S4. of The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

38. Serum metabolomics for early diagnosis of esophageal squamous cell carcinoma by UHPLC-QTOF/MS

39. Association between white blood cell count and non-alcoholic fatty liver disease in urban Han Chinese: a prospective cohort study

40. The short-term association between meteorological factors and mumps in Jining, China

41. Additional file 1: Table S1. of A powerful score-based test statistic for detecting gene-gene co-association

45. Additional file 4: Figure S3. of A powerful score-based test statistic for detecting gene-gene co-association

46. Additional file 5: Figure S4. of A powerful score-based test statistic for detecting gene-gene co-association

48. Additional file 4: Figure S3. of A powerful score-based test statistic for detecting gene-gene co-association

49. Additional file 5: Figure S4. of A powerful score-based test statistic for detecting gene-gene co-association

50. [Influencing factors for needlestick injuries in student nurses]

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