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Items: 1 to 50 of 64

1.

Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling.

Pham N, van Heck RGA, van Dam JCJ, Schaap PJ, Saccenti E, Suarez-Diez M.

Metabolites. 2019 Feb 6;9(2). pii: E28. doi: 10.3390/metabo9020028.

2.

Simulation and Reconstruction of Metabolite-Metabolite Association Networks Using a Metabolic Dynamic Model and Correlation Based Algorithms.

Jahagirdar S, Suarez-Diez M, Saccenti E.

J Proteome Res. 2019 Feb 4. doi: 10.1021/acs.jproteome.8b00781. [Epub ahead of print]

PMID:
30663881
3.

Milk Metabolomics Data Reveal the Energy Balance of Individual Dairy Cows in Early Lactation.

Xu W, Vervoort J, Saccenti E, van Hoeij R, Kemp B, van Knegsel A.

Sci Rep. 2018 Oct 25;8(1):15828. doi: 10.1038/s41598-018-34190-4.

4.

Biological significance and prognostic/predictive impact of complex karyotype in chronic lymphocytic leukemia.

Cavallari M, Cavazzini F, Bardi A, Volta E, Melandri A, Tammiso E, Saccenti E, Lista E, Quaglia FM, Urso A, Laudisi M, Menotti E, Formigaro L, Dabusti M, Ciccone M, Tomasi P, Negrini M, Cuneo A, Rigolin GM.

Oncotarget. 2018 Sep 28;9(76):34398-34412. doi: 10.18632/oncotarget.26146. eCollection 2018 Sep 28. Review.

5.

Group-wise ANOVA simultaneous component analysis for designed omics experiments.

Saccenti E, Smilde AK, Camacho J.

Metabolomics. 2018;14(6):73. doi: 10.1007/s11306-018-1369-1. Epub 2018 May 21.

6.

Diabetes and necrotizing soft tissue infections-A prospective observational cohort study: Statistical analysis plan.

Rosén A, Arnell P, Madsen MB, Nedrebø BG, Norrby-Teglund A, Hyldegaard O, Dos Santos VM, Bergey F, Saccenti E; INFECT Study Group, Skrede S.

Acta Anaesthesiol Scand. 2018 Apr 19. doi: 10.1111/aas.13130. [Epub ahead of print]

PMID:
29671865
7.

In chronic lymphocytic leukaemia with complex karyotype, major structural abnormalities identify a subset of patients with inferior outcome and distinct biological characteristics.

Rigolin GM, Saccenti E, Guardalben E, Cavallari M, Formigaro L, Zagatti B, Visentin A, Mauro FR, Lista E, Bassi C, Lupini L, Quaglia FM, Urso A, Bardi MA, Bonaldi L, Volta E, Tammiso E, Ilari C, Cafforio L, Melandri A, Cavazzini F, Negrini M, Semenzato G, Trentin L, Foà R, Cuneo A.

Br J Haematol. 2018 Apr;181(2):229-233. doi: 10.1111/bjh.15174. Epub 2018 Apr 2.

PMID:
29611195
8.

From correlation to causation: analysis of metabolomics data using systems biology approaches.

Rosato A, Tenori L, Cascante M, De Atauri Carulla PR, Martins Dos Santos VAP, Saccenti E.

Metabolomics. 2018;14(4):37. doi: 10.1007/s11306-018-1335-y. Epub 2018 Feb 27. Review.

9.

Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine.

Emwas AH, Saccenti E, Gao X, McKay RT, Dos Santos VAPM, Roy R, Wishart DS.

Metabolomics. 2018;14(3):31. doi: 10.1007/s11306-018-1321-4. Epub 2018 Feb 12. Review.

10.

The Effect of DNA Extraction Methods on Observed Microbial Communities from Fibrous and Liquid Rumen Fractions of Dairy Cows.

Vaidya JD, van den Bogert B, Edwards JE, Boekhorst J, van Gastelen S, Saccenti E, Plugge CM, Smidt H.

Front Microbiol. 2018 Jan 31;9:92. doi: 10.3389/fmicb.2018.00092. eCollection 2018.

11.

SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles.

Koehorst JJ, van Dam JCJ, Saccenti E, Martins Dos Santos VAP, Suarez-Diez M, Schaap PJ.

Bioinformatics. 2018 Apr 15;34(8):1401-1403. doi: 10.1093/bioinformatics/btx767.

12.

Age and Sex Effects on Plasma Metabolite Association Networks in Healthy Subjects.

Vignoli A, Tenori L, Luchinat C, Saccenti E.

J Proteome Res. 2018 Jan 5;17(1):97-107. doi: 10.1021/acs.jproteome.7b00404. Epub 2017 Nov 21.

PMID:
29090929
13.

Necrotizing soft tissue infections - a multicentre, prospective observational study (INFECT): protocol and statistical analysis plan.

Madsen MB, Skrede S, Bruun T, Arnell P, Rosén A, Nekludov M, Karlsson Y, Bergey F, Saccenti E, Martins Dos Santos VAP, Perner A, Norrby-Teglund A, Hyldegaard O.

Acta Anaesthesiol Scand. 2018 Feb;62(2):272-279. doi: 10.1111/aas.13024. Epub 2017 Oct 29.

PMID:
29082520
14.

Comparative transcriptomics reveal developmental turning points during embryogenesis of a hemimetabolous insect, the damselfly Ischnura elegans.

Simon S, Sagasser S, Saccenti E, Brugler MR, Schranz ME, Hadrys H, Amato G, DeSalle R.

Sci Rep. 2017 Oct 19;7(1):13547. doi: 10.1038/s41598-017-13176-8.

15.

Refined karyotype-based prognostic stratification of chronic lymphocytic leukemia with a low- and very-low-risk genetic profile.

Giudice ID, Rigolin GM, Raponi S, Cafforio L, Ilari C, Wang J, Bordyuh M, Piciocchi A, Marinelli M, Nanni M, Tavolaro S, Filetti M, Bardi A, Tammiso E, Volta E, Negrini M, Saccenti E, Mauro FR, Rossi D, Gaidano G, Guarini A, Rabadan R, Cuneo A, Foà R.

Leukemia. 2018 Feb;32(2):543-546. doi: 10.1038/leu.2017.292. Epub 2017 Sep 19. No abstract available.

16.

Anti-leukemic activity of microRNA-26a in a chronic lymphocytic leukemia mouse model.

D'Abundo L, Callegari E, Bresin A, Chillemi A, Elamin BK, Guerriero P, Huang X, Saccenti E, Hussein EMAA, Casciano F, Secchiero P, Zauli G, Calin GA, Russo G, Lee LJ, Croce CM, Marcucci G, Sabbioni S, Malavasi F, Negrini M.

Oncogene. 2017 Nov 23;36(47):6617-6626. doi: 10.1038/onc.2017.269. Epub 2017 Aug 7.

17.

Use of Microarray Datasets to generate Caco-2-dedicated Networks and to identify Reporter Genes of Specific Pathway Activity.

Venkatasubramanian PB, Toydemir G, de Wit N, Saccenti E, Martins Dos Santos VAP, van Baarlen P, Wells JM, Suarez-Diez M, Mes JJ.

Sci Rep. 2017 Jul 28;7(1):6778. doi: 10.1038/s41598-017-06355-0.

18.

Protein domain architectures provide a fast, efficient and scalable alternative to sequence-based methods for comparative functional genomics.

Koehorst JJ, Saccenti E, Schaap PJ, Martins Dos Santos VAP, Suarez-Diez M.

Version 3. F1000Res. 2016 Aug 15 [revised 2017 Jan 1];5:1987. doi: 10.12688/f1000research.9416.3. eCollection 2016.

19.

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling.

Suarez-Diez M, Adam J, Adamski J, Chasapi SA, Luchinat C, Peters A, Prehn C, Santucci C, Spyridonidis A, Spyroulias GA, Tenori L, Wang-Sattler R, Saccenti E.

J Proteome Res. 2017 Jul 7;16(7):2547-2559. doi: 10.1021/acs.jproteome.7b00106. Epub 2017 May 26.

20.

In CLL, comorbidities and the complex karyotype are associated with an inferior outcome independently of CLL-IPI.

Rigolin GM, Cavallari M, Quaglia FM, Formigaro L, Lista E, Urso A, Guardalben E, Liberatore C, Faraci D, Saccenti E, Bassi C, Lupini L, Bardi MA, Volta E, Tammiso E, Melandri A, Negrini M, Cavazzini F, Cuneo A.

Blood. 2017 Jun 29;129(26):3495-3498. doi: 10.1182/blood-2017-03-772285. Epub 2017 Apr 26. No abstract available.

21.

An extensive molecular cytogenetic characterization in high-risk chronic lymphocytic leukemia identifies karyotype aberrations and TP53 disruption as predictors of outcome and chemorefractoriness.

Rigolin GM, Formigaro L, Cavallari M, Quaglia FM, Lista E, Urso A, Guardalben E, Martinelli S, Saccenti E, Bassi C, Lupini L, Bardi MA, Volta E, Tammiso E, Melandri A, Negrini M, Cavazzini F, Cuneo A.

Oncotarget. 2017 Apr 25;8(17):28008-28020. doi: 10.18632/oncotarget.15883.

22.

Diurnal Dynamics of Gaseous and Dissolved Metabolites and Microbiota Composition in the Bovine Rumen.

van Lingen HJ, Edwards JE, Vaidya JD, van Gastelen S, Saccenti E, van den Bogert B, Bannink A, Smidt H, Plugge CM, Dijkstra J.

Front Microbiol. 2017 Mar 17;8:425. doi: 10.3389/fmicb.2017.00425. eCollection 2017.

23.

Correlation Patterns in Experimental Data Are Affected by Normalization Procedures: Consequences for Data Analysis and Network Inference.

Saccenti E.

J Proteome Res. 2017 Feb 3;16(2):619-634. doi: 10.1021/acs.jproteome.6b00704. Epub 2016 Dec 15.

PMID:
27977202
24.

Comparison of 432 Pseudomonas strains through integration of genomic, functional, metabolic and expression data.

Koehorst JJ, van Dam JC, van Heck RG, Saccenti E, Dos Santos VA, Suarez-Diez M, Schaap PJ.

Sci Rep. 2016 Dec 6;6:38699. doi: 10.1038/srep38699.

25.

Identifying High-Risk Chronic Lymphocytic Leukemia: A Pathogenesis-Oriented Appraisal of Prognostic and Predictive Factors in Patients Treated with Chemotherapy with or without Immunotherapy.

Martinelli S, Cuneo A, Formigaro L, Cavallari M, Lista E, Quaglia FM, Ciccone M, Bardi A, Volta E, Tammiso E, Saccenti E, Sofritti O, Daghia G, Negrini M, Dabusti M, Tomasi P, Moretti S, Cavazzini F, Rigolin GM.

Mediterr J Hematol Infect Dis. 2016 Oct 15;8(1):e2016047. eCollection 2016. Review.

26.

Characterisation of peripheral blood mononuclear cell microRNA in early onset psoriatic arthritis.

Ciancio G, Ferracin M, Saccenti E, Bagnari V, Farina I, Furini F, Galuppi E, Zagatti B, Trotta F, Negrini M, Govoni M.

Clin Exp Rheumatol. 2017 Jan-Feb;35(1):113-121. Epub 2016 Oct 7.

PMID:
27749230
27.

Considering Horn's Parallel Analysis from a Random Matrix Theory Point of View.

Saccenti E, Timmerman ME.

Psychometrika. 2017 Mar;82(1):186-209. doi: 10.1007/s11336-016-9515-z. Epub 2016 Oct 13.

PMID:
27738958
28.

Erratum to: Extensive next-generation sequencing analysis in chronic lymphocytic leukemia at diagnosis: clinical and biological correlations.

Rigolin GM, Saccenti E, Bassi C, Lupini L, Quaglia FM, Cavallari M, Martinelli S, Formigaro L, Lista E, Bardi MA, Volta E, Tammiso E, Melandri A, Urso A, Cavazzini F, Negrini M, Cuneo A.

J Hematol Oncol. 2016 Sep 30;9(1):103. No abstract available.

29.

Extensive next-generation sequencing analysis in chronic lymphocytic leukemia at diagnosis: clinical and biological correlations.

Rigolin GM, Saccenti E, Bassi C, Lupini L, Quaglia FM, Cavallari M, Martinelli S, Formigaro L, Lista E, Bardi MA, Volta E, Tammiso E, Melandri A, Urso A, Cavazzini F, Negrini M, Cuneo A.

J Hematol Oncol. 2016 Sep 15;9(1):88. doi: 10.1186/s13045-016-0320-z. Erratum in: J Hematol Oncol. 2016 Sep 30;9(1):103.

30.

Geochemical and microbial community determinants of reductive dechlorination at a site biostimulated with glycerol.

Atashgahi S, Lu Y, Zheng Y, Saccenti E, Suarez-Diez M, Ramiro-Garcia J, Eisenmann H, Elsner M, J M Stams A, Springael D, Dejonghe W, Smidt H.

Environ Microbiol. 2017 Mar;19(3):968-981. doi: 10.1111/1462-2920.13531. Epub 2016 Oct 6.

PMID:
27631786
31.

Entropy-Based Network Representation of the Individual Metabolic Phenotype.

Saccenti E, Menichetti G, Ghini V, Remondini D, Tenori L, Luchinat C.

J Proteome Res. 2016 Sep 2;15(9):3298-307. doi: 10.1021/acs.jproteome.6b00454. Epub 2016 Aug 1.

PMID:
27436276
32.

Approaches to Sample Size Determination for Multivariate Data: Applications to PCA and PLS-DA of Omics Data.

Saccenti E, Timmerman ME.

J Proteome Res. 2016 Aug 5;15(8):2379-93. doi: 10.1021/acs.jproteome.5b01029. Epub 2016 Jul 7.

PMID:
27322847
33.

The implementation of a Community Health Centre-based primary care model in Italy. The experience of the Case della Salute in the Emilia-Romagna Region.

Odone A, Saccani E, Chiesa V, Brambilla A, Brianti E, Fabi M, Curcetti C, Donatini A, Balestrino A, Lombardi M, Rossi G, Saccenti E, Signorelli C.

Ann Ist Super Sanita. 2016;52(1):70-7. doi: 10.4415/ANN_16_01_13.

34.

Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study.

Stroeve JH, Saccenti E, Bouwman J, Dane A, Strassburg K, Vervoort J, Hankemeier T, Astrup A, Smilde AK, van Ommen B, Saris WH.

Obesity (Silver Spring). 2016 Feb;24(2):379-88. doi: 10.1002/oby.21361.

35.

Impact of a wastewater treatment plant on microbial community composition and function in a hyporheic zone of a eutrophic river.

Atashgahi S, Aydin R, Dimitrov MR, Sipkema D, Hamonts K, Lahti L, Maphosa F, Kruse T, Saccenti E, Springael D, Dejonghe W, Smidt H.

Sci Rep. 2015 Nov 26;5:17284. doi: 10.1038/srep17284.

36.

Effects of Sample Size and Dimensionality on the Performance of Four Algorithms for Inference of Association Networks in Metabonomics.

Suarez-Diez M, Saccenti E.

J Proteome Res. 2015 Dec 4;14(12):5119-30. doi: 10.1021/acs.jproteome.5b00344. Epub 2015 Oct 30.

PMID:
26496246
37.

Assessing the Metabolic Diversity of Streptococcus from a Protein Domain Point of View.

Saccenti E, Nieuwenhuijse D, Koehorst JJ, Martins dos Santos VA, Schaap PJ.

PLoS One. 2015 Sep 14;10(9):e0137908. doi: 10.1371/journal.pone.0137908. eCollection 2015.

38.

Chromosome aberrations detected by conventional karyotyping using novel mitogens in chronic lymphocytic leukemia: Clinical and biologic correlations.

Rigolin GM, del Giudice I, Formigaro L, Saccenti E, Martinelli S, Cavallari M, Lista E, Tammiso E, Volta E, Lupini L, Bassi C, Bardi A, Sofritti O, Daghia G, Cavazzini F, Marinelli M, Tavolaro S, Guarini A, Negrini M, Foà R, Cuneo A.

Genes Chromosomes Cancer. 2015 Dec;54(12):818-26. doi: 10.1002/gcc.22293. Epub 2015 Sep 10.

PMID:
26355802
39.

Microbial Community Response of an Organohalide Respiring Enrichment Culture to Permanganate Oxidation.

Sutton NB, Atashgahi S, Saccenti E, Grotenhuis T, Smidt H, Rijnaarts HH.

PLoS One. 2015 Aug 5;10(8):e0134615. doi: 10.1371/journal.pone.0134615. eCollection 2015.

40.

Diagnostic and prognostic microRNAs in the serum of breast cancer patients measured by droplet digital PCR.

Mangolini A, Ferracin M, Zanzi MV, Saccenti E, Ebnaof SO, Poma VV, Sanz JM, Passaro A, Pedriali M, Frassoldati A, Querzoli P, Sabbioni S, Carcoforo P, Hollingsworth A, Negrini M.

Biomark Res. 2015 Jun 6;3:12. doi: 10.1186/s40364-015-0037-0. eCollection 2015.

41.

Allostasis and Resilience of the Human Individual Metabolic Phenotype.

Ghini V, Saccenti E, Tenori L, Assfalg M, Luchinat C.

J Proteome Res. 2015 Jul 2;14(7):2951-62. doi: 10.1021/acs.jproteome.5b00275. Epub 2015 Jun 24.

PMID:
26055080
42.

Absolute quantification of cell-free microRNAs in cancer patients.

Ferracin M, Lupini L, Salamon I, Saccenti E, Zanzi MV, Rocchi A, Da Ros L, Zagatti B, Musa G, Bassi C, Mangolini A, Cavallesco G, Frassoldati A, Volpato S, Carcoforo P, Hollingsworth AB, Negrini M.

Oncotarget. 2015 Jun 10;6(16):14545-55.

43.

Strategies for individual phenotyping of linoleic and arachidonic acid metabolism using an oral glucose tolerance test.

Saccenti E, van Duynhoven J, Jacobs DM, Smilde AK, Hoefsloot HC.

PLoS One. 2015 Mar 18;10(3):e0119856. doi: 10.1371/journal.pone.0119856. eCollection 2015.

44.

Hypothesis: the sound of the individual metabolic phenotype? Acoustic detection of NMR experiments.

Cacciatore S, Saccenti E, Piccioli M.

OMICS. 2015 Mar;19(3):147-56. doi: 10.1089/omi.2014.0131.

PMID:
25748436
45.

Quantification of circulating miRNAs by droplet digital PCR: comparison of EvaGreen- and TaqMan-based chemistries.

Miotto E, Saccenti E, Lupini L, Callegari E, Negrini M, Ferracin M.

Cancer Epidemiol Biomarkers Prev. 2014 Dec;23(12):2638-42. doi: 10.1158/1055-9965.EPI-14-0503.

46.

Probabilistic networks of blood metabolites in healthy subjects as indicators of latent cardiovascular risk.

Saccenti E, Suarez-Diez M, Luchinat C, Santucci C, Tenori L.

J Proteome Res. 2015 Feb 6;14(2):1101-11. doi: 10.1021/pr501075r. Epub 2014 Dec 8.

PMID:
25428344
47.

Of monkeys and men: a metabolomic analysis of static and dynamic urinary metabolic phenotypes in two species.

Saccenti E, Tenori L, Verbruggen P, Timmerman ME, Bouwman J, van der Greef J, Luchinat C, Smilde AK.

PLoS One. 2014 Sep 15;9(9):e106077. doi: 10.1371/journal.pone.0106077. eCollection 2014.

48.

microRNAome expression in chronic lymphocytic leukemia: comparison with normal B-cell subsets and correlations with prognostic and clinical parameters.

Negrini M, Cutrona G, Bassi C, Fabris S, Zagatti B, Colombo M, Ferracin M, D'Abundo L, Saccenti E, Matis S, Lionetti M, Agnelli L, Gentile M, Recchia AG, Bossio S, Reverberi D, Rigolin G, Calin GA, Sabbioni S, Russo G, Tassone P, Morabito F, Ferrarini M, Neri A.

Clin Cancer Res. 2014 Aug 1;20(15):4141-53. doi: 10.1158/1078-0432.CCR-13-2497. Epub 2014 Jun 10.

49.

A metabolomic perspective on coeliac disease.

Calabrò A, Gralka E, Luchinat C, Saccenti E, Tenori L.

Autoimmune Dis. 2014;2014:756138. doi: 10.1155/2014/756138. Epub 2014 Feb 9. Review.

50.

Modern treatment in chronic lymphocytic leukemia: impact on survival and efficacy in high-risk subgroups.

Cuneo A, Cavazzini F, Ciccone M, Daghia G, Sofritti O, Saccenti E, Negrini M, Rigolin GM.

Cancer Med. 2014 Jun;3(3):555-64. doi: 10.1002/cam4.226. Epub 2014 Mar 19.

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