Publications

Medical Informatics

1. Park J, Lucas AM, Zhang X, Chaudhary K, ..., Do R, Ritchie MD, Rader DJ (2021). Exome-wide evaluation of rare coding variants using electronic health records identifies new gene–phenotype associations. Nat Med. 2021 Jan;27(1):66-72. doi: 10.1038/s41591-020-1133-8. Epub 2021 Jan 11.

2. Forrest IS, Chaudhary K, T Vy HM, Petrazzini BO, Bafna S, Jordan DM, Rocheleau G, Loos RJF, Nadkarni GN, Cho JH, Do R (2022). Population-Based Penetrance of Deleterious Clinical Variants. JAMA. 2022 Jan 25;327(4):350-359. doi: 10.1001/jama.2021.23686.

3. Forrest IS, Chaudhary K, Paranjpe I, T Vy HM, Marquez-Luna C, Rocheleau G, Saha A, Chan L, Van Vleck T, Loos RJF, Cho J, Pasquale LR, Nadkarni GN, Do R (2021). Genome-wide polygenic risk score for retinopathy of type 2 diabetes. Hum Mol Genet. 2021 May 29;30(10):952-960. doi: 10.1093/hmg/ddab067.

4. Yadav AK, Banerjee SK, Das B, Chaudhary K (2022). Editorial: System biology and omics approaches for understanding complex disease biology. Front Genet. 2022 Apr 12;13:896818. doi: 10.3389/fgene.2022.896818. eCollection 2022

5. Singh P, Ujjainiya R, Prakash S, ..., Chaudhary K, Agrawal A, Sengupta S, Dash D (2022). A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys. Comput Biol Med. 2022 Jul;146:105419. doi: 10.1016/j.compbiomed.2022.105419.

I) Cardiovascular

6. Damrauer SM*, Chaudhary K*, Cho JH*, Liang LW*, ...., Nadkarni G, Rader DJ, Do R. Association of the V122I Hereditary Transthyretin Amyloidosis Genetic Variant With Heart Failure Among Individuals of African or Hispanic/Latino Ancestry. JAMA. 2019;322(22):1-12 (*Joint First authors)

7. Chaudhary K, Petrazzini BO, Narula J, Nadkarni GN, Do R (2020). Prediction of Incident Heart Failure in TTR Val122Ile Carriers One Year Ahead of Diagnosis in a Multiethnic Biobank. Am J Cardiol. 2021 Mar 1;142:151-153. doi: 10.1016/j.amjcard.2020.12.015. Epub 2020 Dec 18.

8. Aragam KG, Dobbyn A, Judy R, Chaffin M, Chaudhary K, Hindy G, Cagan A, Finneran P, Weng L, Loos RJF, Nadkarni G, Cho JH, Kember RL, Baras A, Reid J, Overton J, Philippakis A, Ellinor PT, Weiss ST, Rader DJ, Lubitz SA, Smoller JW, Karlson EW, Khera AV, Kathiresan S, Do R, Damrauer SM, Natarajan P (2020). Limitations of Contemporary Guidelines for Managing Patients at High Genetic Risk of Coronary Artery Disease. J Am Coll Cardiol. 2020 Jun, 75 (22) 2769-2780.

9. Fahed AC, Aragam KG, Hindy G, Chen YI, Chaudhary K, ..., Kathiresan S, Khera AV (2020). Transethnic transferability of a genome-wide polygenic score for coronary artery disease. Circ Genom Precis Med. 2021 Feb;14(1):e003092. doi: 10.1161/CIRCGEN.120.003092. Epub 2020 Dec 7.

10. Forrest IS, Chan L, Chaudhary K, Saha A, Hsun HH, Cho J, Rocheleau G, Marquez-Luna C, Cepin CL, Narula J, Nadkarni GN, Do R (2022). Genome-First Recall of Healthy Individuals by Polygenic Risk Score Reveals Differences in Coronary Artery Calcium. Am. Heart J. doi: 10.1016/j.ahj.2022.04.006 EPUB

11. Petrazzini BO, Chaudhary K, Márquez-Luna C, Forrest IS, Rocheleau G, Cho J, Narula J, Nadkarni G, Do R (2022). Coronary risk estimation based on clinical data in electronic health records. J Am Coll Cardiol. 2022 Mar 29;79(12):1155-1166. doi: 10.1016/j.jacc.2022.01.021.

12. Paranjpe I, Tsao NL, De Freitas JK, Judy R, Chaudhary K, ..., Do R, Damrauer SM, Nadkarni GN (2021). Derivation and Validation of Genome‐Wide Polygenic Score for Ischemic Heart Failure. J Am Heart Assoc. 2021 Nov 16;10(22):e021916. doi: 10.1161/JAHA.121.021916.

13. Forrest IS, Chaudhary K, T Vy HM, Bafna S, Kim S, Won HH, Loos RJF, Cho J, Pasquale LR, Nadkarni GN, Rocheleau G, Do R (2021). Genetic pleiotropy of ERCC6 loss-of-function and deleterious missense variants links retinal dystrophy, arrhythmia, and immunodeficiency in diverse ancestries. Hum Mutat. 2021 Aug;42(8):969-977. doi: 10.1002/humu.24220.

14. Assimes T, Tcheandjieu C, Zhu X, ..., Chaudhary K, Graff M, Huang J (2021). Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat Med. 2022 Aug;28(8):1679-1692. doi: 10.1038/s41591-022-01891-3.

15. Petrazzini B, Vaid A, ..., Chaudhary K, Do R (2022). Short-term prediction of coronary artery disease using serum metabolomic patterns. Am Heart J Plus. 2022 Dec;24:100232. doi: 10.1016/j.ahjo.2022.100232.

II) Nephrology

16. Chan L*, Kumardeep Chaudhary K*, ..., Coca SG, Nadkarni GN (2020). AKI in Hospitalized Patients with COVID-19. J Am Soc Nephrol. 2021 Jan;32(1):151-160 (*Joint First authors)

17. Vaid A, Chan L, Chaudhary K, ..., Coca SG, Glicksberg BS, Nadkarni GN (2021). Predictive approaches for acute dialysis requirement and death in COVID-19. Clin J Am Soc Nephrol. 2021 Aug;16(8):1158-1168. doi: 10.2215/CJN.17311120.

18. Paranjpe I, Jayaraman P, ..., Chaudhary K, ..., Glicksberg BS, Charney AW, Nadkarni GN (2021). Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection. medRxiv Preprint.

19. T Vy HM, Dellepiane S, Chaudhary K, Blair A, Glicksberg BS, Coca SG, Chan L, He JC, Do R, Nadkarni GN (2021). Genome-wide polygenic risk score method for diabetic kidney disease in patients with type 2 diabetes. medRxiv Preprint

20. Paranjpe I, Chaudhary K, ..., Glicksberg BS, Nadkarni GN (2021). Association of SARS-CoV-2 viral load at admission with in-hospital acute kidney injury: A retrospective cohort study. PLoS One. 2021 Feb 24;16(2):e0247366. doi: 10.1371/journal.pone.0247366. eCollection 2021.

21. Gu X, Yang H, ..., Chaudhary K, ..., Rader DJ, Susztak K (2021). Kidney disease genetic risk variants alter lysosomal beta-mannosidase (MANBA) expression and disease severity. Sci Transl Med. 2021 Jan 13;13(576):eaaz1458. doi: 10.1126/scitranslmed.aaz1458.

22. Pattharanitima P, Vaid A, Jaladanki SK, ..., Chaudhary K, ..., Chan L, Nadkarni GN (2021). Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury. Blood Purif. 2021;50(4-5):621-627. doi: 10.1159/000513700.

23. Chaudhary K, Vaid A, Duffy Á, ..., Chan L, Nadkarni GN (2020). Utilization of deep learning for subphenotype identification in sepsis-associated acute kidney injury. Clin J Am Soc Nephrol. 2020 Nov 6;15(11):1557-1565. doi: 10.2215/CJN.09330819.

24. Paranjpe I, Tsao N, Judy R, Paranjpe M, Chaudhary K, ..., Do R, Damrauer SM, Nadkarni GN (2020). Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis. Kidney Int. 2020 Nov;98(5):1323-1330. doi: 10.1016/j.kint.2020.04.055. Epub 2020 Jun 12.

25. Chan L, Beers K, Yau AA, Chauhan K, Duffy A, Chaudhary K, Debnath N, Saha A, Pattharanitima P, Cho J, Kotanko P, Federman A, Coca SG, Vleck TV, Nadkarni GN (2020). Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients. Kidney Int 97 (2), 383-392, 2020

26. Paranjpe I, Chaudhary K, Paranjpe M, O’Hagan R, Manna S, Jaladanki S, Kapoor A, Horowitz C, DeFelice N, Cooper R, Glicksberg B, Bottinger EP, Just AC, Nadkarni GN (2020). Association of APOL1 Risk Genotype and Air Pollution for Kidney Disease. CJASN February 2020, CJN.11921019; DOI: https://doi.org/10.2215/CJN.11921019

27. Pattharanitima P, Chauhan K, Shamy OE, Chaudhary K, Sharma S, Coca SG, Nadkarni GN, Uribarri J, Chan L (2020). The association of standard Kt/V and surface area‐normalized standard Kt/V with clinical outcomes in hemodialysis patients. Hemodial Int. 2020 Oct;24(4):495-505. doi: 10.1111/hdi.12865.

28. Nadkarni GN, Chaudhary K, Coca SG (2019). Machine Learning in Glomerular Diseases: Promise for Precision Medicine. Am J Kidney Dis. 2019 Sep;74(3):290-292. doi: 10.1053/j.ajkd.2019.04.011.

29. Chauhan K, Pattharanitima P, Patel N, Duffy A, Saha A, Chaudhary K, Debnath N, Vleck TV, Chan L, Nadkarni GN, Coca SG (2019). Rate of Correction of Hypernatremia and Health Outcomes in Critically Ill Patients. CJASN May 2019, 14 (5) 656-663; DOI: https://doi.org/10.2215/CJN.10640918

III) Pathogens

30. COVID-19 Host Genetics Initiative (2021). Mapping the human genetic architecture of COVID-19. Nature. 2021 Dec;600(7889):472-477. doi: 10.1038/s41586-021-03767-x. (HGI Consortium author)

31. Singh P, Ujjainiya R, ...., Chaudhary K, Agrawal A, Sengupta S, Dash D (2022). A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys. Comput Biol Med. 2022 Apr 25;146:105419. doi: 10.1016/j.compbiomed.2022.105419.

32. Ujjainiya R, Tyagi A, Sardana V, ..., Chaudhary K, ..., Agrawal A, Dash D, Jha S, Sengupta S (2022). High failure rate of ChAdOx1-nCoV19 immunization against asymptomatic infection in healthcare workers during a Delta variant surge. Nat Commun. 2022 Apr 1;13(1):1726. doi: 10.1038/s41467-022-29404-3.

33. COVID-19 Host Genetics Initiative (2022). A first update on mapping the human genetic architecture of COVID-19. Nature. 2022 Aug;608(7921):E1-E10. doi: 10.1038/s41586-022-04826-7 (HGI Consortium author)

34. Dhanda SK, Chaudhary K, Gupta S, Brahmachari SK and Raghava GPS (2016). A web-based resource for designing therapeutics against Ebola Virus. Sci Rep. 2016; 6: 24782.

35. Nagpal G., Chaudhary K., Dhanda S.K., Raghava G.P.S. (2017) Computational Prediction of the Immunomodulatory Potential of RNA Sequences. In: Bindewald E., Shapiro B. (eds) RNA Nanostructures. Methods in Molecular Biology, vol 1632. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7138-1_5 (Book Chapter)

36. Fourati S, ...., The Respiratory Viral DREAM Challenge Consortium, ..., Sieberts SK (2018). A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nat Comm. 2018; https://doi.org/10.1038/s41467-018-06735-8 (Consortium Author)

37. Gaur AS, Bhardwaj A, ….., Chaudhary K, ….. and Sastry GN (2017) Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis (MPDSTB). J Chem Sci 129:515. doi: 10.1007/s12039-017-1268-4 

38. Pickett BE, Connor R, ..., Chaudhary K, ..., Torian U, Zalunin V, Busby B (2022). Lessons learned in virulence factor identification and data management from a hackathon on microbial virulence. F1000Research, 11(160), 160.

Cancer Informatics

39. Poirion OB, Jing Z, Chaudhary K, Huang S, Garmire LX (2021). DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome Med. 2021 Jul 14;13(1):112. doi: 10.1186/s13073-021-00930-x.

40. Kumar R*, Chaudhary K*, Gupta S*, Singh H, Kumar S, Gautam A, Kapoor P, Raghava GPS (2013). CancerDR: cancer drug resistance database. Sci Rep. 2013; 3:1445. (*Joint First Authors)

41. Kumar R*, Chaudhary K*, Singla D, Gautam A, Raghava GPS (2014) Designing of promiscuous inhibitors against pancreatic cancer cell lines. Sci Rep. 2014 Apr 14; 4:4668. (*Joint First Authors)

42. Chaudhary K, Poirion OB, Lu L, Huang S, Ching T and Garmire LX (2019). Multi-modal meta-analysis of 1494 hepatocellular carcinoma samples reveals significant impact of consensus driver genes on phenotypes. Clin Can Res 2019; doi: 10.1158/1078-0432.CCR-18-0088

43. Bhalla S*, Chaudhary K*, Kumar R, Sehgal M, Kaur H, Sharma S, Raghava GPS (2017). Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer.  Sci Rep. 2017;7:44997. 

44. Nagpal G, Sharma M, Kumar S, Chaudhary K, Gupta S, Gautam A, Raghava GPS (2014) PCMdb: pancreatic cancer methylation database. Sci Rep. 2014 Feb 26; 4:4197.

45. Mason MJ, Schinke C, ..., Multiple Myeloma DREAM Consortium, ... , Guinney J (2020). Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease.  Leukemia (2020). https://doi.org/10.1038/s41375-020-0742-z (Consortium Author)

46. Abul-Husn NS, Soper ER, ..., CBIPM Genomics Team, Regeneron Genetics Center, … , Kenny EE (2020). Exome sequencing reveals a high prevalence of BRCA1 and BRCA2 founder variants in a diverse population-based biobank. Genome Med 12, 2 (2020). https://doi.org/10.1186/s13073-019-0691-1 (CBIPM Genomics Team member)

47. Yang M, Petralia F, ... , NCI-CPTAC-DREAM Consortium Group, … , Fenyo FD, Saez-Rodriguez J (2020). Community assessment of the predictability of cancer protein and phosphoprotein levels from genomics and transcriptomics. Cell Systems http://dx.doi.org/10.2139/ssrn.3554086 (NCI-CPTAC-DREAM Consortium Group member)

48. Gautam A., Chaudhary K., Kumar R., Gupta S., Singh H., Raghava G.P.S. (2016) Managing Drug Resistance in Cancer: Role of Cancer Informatics. In: Rueff J., Rodrigues A. (eds) Cancer Drug Resistance. Methods in Molecular Biology, vol 1395. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3347-1_17 (Book Chapter)

49. Singh H, Kumar R, Singh S, Chaudhary K, Gautam A and Raghava GPS (2016). Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines. BMC Cancer 2016, 16:77.

50. Gupta S, Chaudhary K, Kumar R, Gautam A, Nanda JS, Dhanda SK, Brahmachari SK and Raghava GPS (2016). Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine. Sci Rep. 2016, 6: 23857.

51. Alakwaa FM, Benny P, Chaudhary K, Garmire LX (2020). A data driven approach to identify ZM-447439 as a potential repurposed drug to overcome tamoxifen-resistance in breast cancer. Research Square Preprint

52. Alkawaa FM, Chaudhary K and Garmire LX (2017). Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data. J. Proteome Res. DOI: 10.1021/acs.jproteome.7b00595

53. Chaudhary K, Poirion OB, Lu L and Garmire LX (2018). Deep Learning based multi-omics integration robustly predicts survival in liver cancer. Clin Can Res 2018; doi: 10.1158/1078-0432.CCR-17-0853

54. Poirion OB*, Chaudhary K*, Garmire LX (2018). Deep Learning data integration for better risk stratification models of bladder cancer. AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:197-206. eCollection 2018.

Immunoinformatics

55. Nagpal G*, Gupta S*, Chaudhary K*, Dhanda SK*, Prakash S, and Raghava GPS (2015). VaccineDA: Prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants. Sci Rep., doi:10.1038/srep12478 (*Joint First authors)

56. Chaudhary K, Nagpal G, Dhanda, SK, Raghava GPS (2016). Prediction of Immunomodulatory potential of an RNA sequence for designing non-toxic siRNAs and RNA-based vaccine adjuvants. Sci Rep. 2016; 6: 20678.

57. Nagpal G*, Chaudhary K*, Agrawal P, Raghava GPS (2018) Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants. J Transl Med. 2018; 16:181 (*Joint First authors)

58. Gupta S, Chaudhary K, Dhanda SK, Kumar R, Kumar S, Sehgal M, Nagpal G and Raghava GPS (2016).  A platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer. PLoS One. 11(11): e0166372

Peptide Informatics

59. Bayrak CS, Stein D, Jain A, Chaudhary K, Nadkarni GN, Van Vleck TT, Puel A, Boisson-Dupuis S, Okada S, Stenson PD, Cooper DN, Schlessinger A, Itan Y (2021). Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants. Am J Hum Genet. 2021 Dec 2;108(12):2301-2318. doi: 10.1016/j.ajhg.2021.10.007. Epub 2021 Nov 10.

60. Huang S, Chaudhary K, Garmire LX (2017). More is better: recent progress in multi-omics data integration methods. Front Genet. 2017 Jun 16;8:84. doi: 10.3389/fgene.2017.00084. eCollection 2017.

61. Agrawal P, Bhalla S, Chaudhary K, Kumar R, Sharma M and Raghava GPS (2018) In silico approach for prediction of antifungal peptides.  Front. Microbiol., 9:323. doi: 10.3389/fmicb.2018.00323

62. Chaudhary K, Kumar R, Singh S, Tuknait A, Gautam A, Mathur D, Anand P, Varshney GC, Raghava GPS (2016). A Web Server and Mobile App for Computing Hemolytic Potency of Peptides. Sci Rep. 2016; 6: 22843.

63. Singh S, Chaudhary K, Dhanda SK, Bhalla S, Usmani SS, Gautam A, Tuknait A, Agrawal P, Mathur D and Raghava GPS (2016). SATPdb: a database of structurally annotated therapeutic peptides. Nucl. Acids Res. 44 (D1): D1119-D1126.

64. Agrawal, P, Bhalla S, Usmani SS, Singh S, Chaudhary K, Raghava GPS and Gautam A (2016). CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides. Nucl. Acids Res. 44 (D1): D1098-D1103.

65. Kumar R*, Chaudhary K*, Chauhan JS*, Nagpal G*, Kumar R*, Sharma M, Raghava GPS (2015). An in silico platform for predicting, screening and designing of antihypertensive peptides. Sci Rep. doi:10.1038/srep12512. (*Joint First authors)

66. Singh S, Singh H, Tuknait A, Chaudhary K, Singh B, Kumaran S, Raghava GPS (2015). PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues. Biology Direct, 10:73.

67. Gautam A, Chaudhary K, Kumar R, Raghava GPS (2015). Computer-aided virtual screening and designing of cell-penetrating peptides. In: Langel Ü. (eds) Cell-Penetrating Peptides. Methods in Molecular Biology, vol 1324. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2806-4_4 (Book Chapter)

68. Gupta S., Kapoor P., Chaudhary K., Gautam A., Kumar R., Raghava G.P.S. (2015) Peptide Toxicity Prediction. In: Zhou P., Huang J. (eds) Computational Peptidology. Methods in Molecular Biology, vol 1268. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2285-7_7 (Book Chapter)

69. Gautam A*, Chaudhary K*, Singh S*, Joshi A, Anand P, Tuknait A, Mathur D, Varshney GC, Raghava GPS (2014) Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides. Nucleic Acids Res. 2014 Jan; 42 (Database issue): D444-9. (*Joint First Authors)

70. Gautam A, Sharma M, Vir P, Chaudhary K, Kapoor P, Kumar R, Nath SK, Raghava GPS (2014). Identification and characterization of novel protein derived arginine-rich cell penetrating peptides. Eur J Pharm Biopharm. pii: S0939-6411(14)00347-6.

71. Mehta D, Anand P, Kumar V, Joshi A, Mathur D, Singh S, Tuknait A, Chaudhary K, Gautam SK, Gautam A, Varshney GC, Raghava GPS (2014) ParaPep: a web resource for experimentally validated antiparasitic peptide sequences and their structures. Database (Oxford). 12; 2014. pii: bau051.

72. Gautam A, Kapoor P, Chaudhary K, Kumar R, Raghava GPS, Source Drug Discovery Consortium (2014). Tumor homing peptides as molecular probes for cancer therapeutics, diagnostics and theranostics. Curr Med Chem. 2014;21(21):2367-91. doi: 10.2174/0929867321666140217122100.

73. Kumar R, Chaudhary K, Sharma M, Nagpal G, Chauhan JS, Singh S, Gautam A, and Raghava GPS (2014). AHTPDB: A Comprehensive Platform for Analysis and Presentation of Antihypertensive Peptides. Nucleic Acids Res. 43 (D1): D956-D962.

74. Gautam A*, Chaudhary K*, Kumar R*, Sharma A*, Kapoor P, Tyagi A; Open source drug discovery consortium, Raghava GPS (2013). In silico approaches for designing highly effective cell penetrating peptides. J Transl Med.11:74. (*Joint First Authors)

75. Gupta S*, Kapoor P*, Chaudhary K*, Gautam A*, Kumar R, OSDD Consortium, Raghava GPS (2013). In Silico Approach for Predicting Toxicity of Peptides and Proteins. Plos One 8(9) e73957 (*Joint First authors)

76. Tyagi A, Kapoor P, Kumar R, Chaudhary K, Gautam A and Raghava GPS (2013) In silico models for designing and discovering novel anticancer peptides. Sci Rep. 18; 3:2984.

77. Sharma A, Kapoor P, Gautam A, Chaudhary K, Kumar R, Chauhan JS, Tyagi A, Raghava GPS (2013). Computational approach for designing tumor homing peptides. Sci Rep. 2013; 3:1607.

78. Gautam A, Singh H, Tyagi A, Chaudhary K, Kumar R, Kapoor P, Raghava GPS (2012) CPPsite: A Curated Database of Cell Penetrating Peptides. Database (Oxford). bas015.

79. Kapoor P, Singh H, Gautam A, Chaudhary K, Kumar R, and Raghava GPS (2012) TumorHope: A database of tumor homing peptides. PLoS One. 2012;7(4):e35187