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1. Epidemiological and clinical characterization of community, healthcare-associated and nosocomial colonization and infection due to carbapenemase-producing Klebsiella pneumoniae and Escherichia coli in Spain

4. Pseudomonas aeruginosa antibiotic susceptibility profiles, genomic epidemiology and resistance mechanisms: a nation-wide five-year time lapse analysis

6. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

11. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

13. Recomendaciones del Comité Español del Antibiograma (COESANT) para la realización de los Informes de Sensibilidad Antibiótica Acumulada

14. Supplementary Methods from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

15. Supplementary Table 2 from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

16. Supplementary Figure 4 from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

17. Data from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

18. Supplementary Table 1 from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

19. Supplementary Figure 1 from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

20. Supplementary Figure 2 from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

21. Supplementary Figure 3 from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

22. Supplementary Figure 5 from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

23. Author Correction: COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients

24. COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients

25. Author Correction: Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

27. Genome-wide gene-environment interaction analyses to understand the relationship between red meat and processed meat intake and colorectal cancer risk.

30. Genome-Wide Gene-Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk.

31. Recommendations of the Spanish Antibiogram Committee (COESANT) for selecting antimicrobial agents and concentrations for in vitro susceptibility studies using automated systems

32. Heterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer

34. Novel insights into genetic susceptibility for colorectal cancer from transcriptome-wide association and functional investigation.

35. CARB-ES-19 Multicenter Study of Carbapenemase-Producing Klebsiella pneumoniae and Escherichia coli From All Spanish Provinces Reveals Interregional Spread of High-Risk Clones Such as ST307/OXA-48 and ST512/KPC-3.

36. Heterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer.

37. Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer

42. Identification of Trypanosoma cruzi Discrete Typing Units (DTUs) in Latin-American migrants in Barcelona (Spain)

43. In vitro and in vivo efficacy of combinations of colistin and different endolysins against clinical strains of multi-drug resistant pathogens

45. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

49. Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes

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