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2. Contributors

3. In silico validation of RNA-Seq results can identify gene fusions with oncogenic potential in glioblastoma

4. Data mining analyses for precision medicine in acromegaly: a proof of concept

6. The C250T Mutation of TERTp Might Grant a Better Prognosis to Glioblastoma by Exerting Less Biological Effect on Telomeres and Chromosomes Than the C228T Mutation

7. Epigenetic loss of RNA-methyltransferase NSUN5 in glioma targets ribosomes to drive a stress adaptive translational program

8. Revisiting the usefulness of the short acute octreotide test to predict treatment outcomes in acromegaly

10. Predictive signature of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer integrating mRNA expression, taxonomic subtypes, and clinicopathological features

11. Usefulness of the short version of the acute octreotide test for prediction of first-generation somatostatin receptor ligands response in acromegaly: a validation study with the ACROFAST cohort

12. Supplementary Data files S1 a_f from Glioblastoma TCGA Mesenchymal and IGS 23 Tumors are Identifiable by IHC and have an Immune-phenotype Indicating a Potential Benefit from Immunotherapy

13. Supplementary Fig. S4 from Glioblastoma TCGA Mesenchymal and IGS 23 Tumors are Identifiable by IHC and have an Immune-phenotype Indicating a Potential Benefit from Immunotherapy

14. Supplementary Table S1 from Glioblastoma TCGA Mesenchymal and IGS 23 Tumors are Identifiable by IHC and have an Immune-phenotype Indicating a Potential Benefit from Immunotherapy

15. Table S2 from Glioblastoma TCGA Mesenchymal and IGS 23 Tumors are Identifiable by IHC and have an Immune-phenotype Indicating a Potential Benefit from Immunotherapy

16. Molecular characterization of epithelial-mesenchymal transition and medical treatment related-genes in non-functioning pituitary neuroendocrine tumors

17. Gal-1 Expression Analysis in the GLIOCAT Multicenter Study: Role as a Prognostic Factor and an Immune-Suppressive Biomarker

18. Supplementary Information to Gal-1 expression analysis in the GLIOCAT multicenter study: role as a prognostic factor and an immune-suppressive biomarker

19. Gal-1 Expression Analysis in the GLIOCAT Multicenter Study: Role as a Prognostic Factor and an Immune-Suppressive Biomarker

24. Bevacizumab and temozolomide versus temozolomide alone as neoadjuvant treatment in unresected glioblastoma: the GENOM 009 randomized phase II trial

25. Is IDO1 an adequate target for treatment in glioblastoma?

27. Implications of Heterogeneity of Epithelial-Mesenchymal States in Acromegaly Therapeutic Pharmacologic Response

29. Implications of Heterogeneity of Epithelial-Mesenchymal States in Acromegaly Therapeutic Pharmacologic Response

30. Prognostic Impact of CD36 Immunohistochemical Expression in Patients with Muscle-Invasive Bladder Cancer Treated with Cystectomy and Adjuvant Chemotherapy

33. MP60-20 DETECTION OF LYMPH NODE METASTASES USING POOLING METHOD BY ONE-STEP NUCLEIC ACID AMPLIFICATION (OSNA) ASSAY IN PROSTATE CANCER PATIENTS: PRELIMINARY RESULTS FROM A PROSPECTIVE-MULTICENTRE STUDY

36. Gene fusions in glioblastoma: Results of Gliocat project.

37. Long-term results of the GEINO 1401 TRIAL: Randomizing patients to stop or to continue temozolomide until 12 cycles.

40. Glioblastoma TCGA mesenchymal and IGS 23 tumors are identifiable by immunohistochemistry and have an immune-phenotype indicating potential benefit from immunotherapy

41. Immunohistochemistry-based taxonomical classification of Bladder cancer predicts response to neoadjuvant chemotherapy

43. RNA sequencing and Immunohistochemistry Reveal ZFN7 as a Stronger Marker of Survival than Molecular Subtypes in G-CIMP–negative Glioblastoma

44. Glioblastoma TCGA Mesenchymal and IGS 23 Tumors are Identifiable by IHC and have an Immune-phenotype Indicating a Potential Benefit from Immunotherapy

46. Characteristics of Adrenocortical Carcinoma Associated With Lynch Syndrome

47. Complete Loss of EPCAM Immunoexpression Identifies EPCAM Deletion Carriers in MSH2-Negative Colorectal Neoplasia

50. Data mining analyses for precision medicine in acromegaly

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