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    SmartScience Dx

    1. Cordola Hsu AR, Fan W, Harrington D, et al. (2022) Acute coronary syndromes in diabetes: Biomarkers of endothelial injury improve risk stratification and help identify predictors of risk. Diabetes & metabolic syndrome, 16(4),102476.
    2. Premyodhin N, Fan W, Younus M, et al. (2022) Novel biomarker panel measuring endothelial injury identifies patients at risk of coronary artery syndrome and discordance with low-density lipoprotein cholesterol. Coronary artery disease, 31(1),e51-e58.
    3. Younus M, Fan W, Harrington DS, and Wong ND. (2019) Usefulness of a Coronary Artery Disease Predictive Algorithm to Predict Global Risk for Cardiovascular Disease and Acute Coronary Syndrome. The American journal of cardiology, 123(5),769-775.

    SmartVascular Dx

    1. Cordola Hsu AR, Fan W, Harrington D, et al. (2022) Acute coronary syndromes in diabetes: Biomarkers of endothelial injury improve risk stratification and help identify predictors of risk. Diabetes & metabolic syndrome, 16(4),102476.
    2.   Premyodhin N, Fan W, Younus M, et al. (2022) Novel biomarker panel measuring endothelial injury identifies patients at risk of coronary artery syndrome and discordance with low-density lipoprotein cholesterol. Coronary artery disease, 31(1),e51-e58.
    3. Gundry SE. (2021) Correction to: Abstract 10712: mRNA COVID Vaccines Dramatically Increase Endothelial Inflammatory Markers and ACS Risk as Measured by the PULS Cardiac test: a Warning. Circulation CIR0000000000001053. Advance online publication.
    4. Younus M, Fan W, Harrington DS, and Wong ND. (2019) Usefulness of a Coronary Artery Disease Predictive Algorithm to Predict Global Risk for Cardiovascular Disease and Acute Coronary Syndrome. The American journal of cardiology, 123(5),769-775.
    5. Broder M. (2019) Advances in Cholesterol Management: Is this a Better Predictive Algorithm for ACS? MEDPAGE TODAY. New Haven, CT.
    6. Ormseth CH, Sheth KN, Saver JL. (2017) The American Heart Association’s Get With the Guidelines (GWTG) – Stroke development and impact on stroke care. Stroke and vascular neurology, 2(2),94-105.
    7. Harrington DS, Zhao Y, Simonini A, et al. (2017) Novel Biomarker Algorithmic Panel Measuring Permutations of Immune Response to Cardiac Endothelial Injury and Global Risk Factors Identifies Patients at Risk of Acute Coronary Syndrome. Journal of american college of cardiology, 69(11):1024. (Poster)
    8. Singh M, Kroman A, Singh J, et al. (2015) Special Operations Soldier with Cardiac Family History: Use of CCTA and Protein Biomarker Testing to Detect Risk of Heart Attack from Noncalcified Plaque. Journal of special operations medicine: a peer-reviewed journal of SOF medical professionals, 15(1),7-10.
    9. Simonini A, Harrington DS. (2015) Early Detection of Unstable Cardiac Lesions in Asymptomatic Individuals at Risk of Acute Coronary Syndrome. Cardiology, 131,148-148.
    10. Hytopoulos E, Lee ML, Beggs M, et al. (2014) Cost effectiveness analysis of a next generation risk assessment score for cardiovascular disease. Journal of medical economics, 17(2),132-141.
    11. Tariq H, Amin S, Singh M, et al. (2014) Predicting heart attack in a patient post-radiation therapy using plaque CCTA analysis and serum biomarker test. Case report. OncoReview, 4(2),A54-61.
    12. Ghushchyan V, Nair KV, and Page RL 2nd. (2014) Indirect and direct costs of acute coronary syndromes with comorbid atrial fibrillation, heart failure, or both. Vascular health and risk management, 11,25-34. 
    13. Nolan N, Tee L, Vijayakumar S, et al. (2013) Analytical performance validation of a coronary heart disease risk assessment multi-analyte proteomic test. Expert opinion on medical diagnostics, 7(2),127-136.
    14. Solomon MD, Tirupsur A, Hytopoulos E, et al. (2013) Clinical utility of a novel coronary heart disease risk-assessment test to further classify intermediate-risk patients. Clinical cardiology, 36(10),621-627.
    15. Nolan N, Pasion J, Korzus G, Tee L, et al. (2013) Analytical Validation and Method Comparison of a Multi-Analyte Serum Protein Based Test for Assessing Coronary Heart Disease Risk. (Abstract)
    16. Cross DS, McCarty CA, Hytopoulos E, et al. (2012) Coronary risk assessment among intermediate risk patients using a clinical and biomarker based algorithm developed and validated in two population cohorts. Current medical research and opinion, 28(11),1819–1830.
    17. Hamang A, Eide GE, Rokne B, et al. (2012) Predictors of heart-focused anxiety in patients undergoing genetic Investigation and Counseling of Long QT Syndrome or Hypertrophic Cardiomyopathy: A One Year Follow-up. Journal of genetic counseling, 21(1),72-84.
    18. Roger VL, Go AS, Lloyd-Jones DM, et al. (2011) Heart disease and stroke statistics–2011 update: a report from the American Heart Association. Circulation 123(4):e18-e209.
    19. Cook, NR and Paynter MP. (2011) Performance of reclassification statistics in comparing risk prediction models. Biometrical journal. Biometrische Zeitschrift, 53(2),237-258. 
    20. Kones R. (2011) Primary prevention of coronary heart disease: integration of new data, evolving views, revised goals, and role of rosuvastatin in management. A comprehensive survey. Drug design, development and therapy, 5,325-380.
    21. International Multiple Sclerosis Genetics Consortium, Wellcome Trust Case Control Consortium 2, Sawcer, S., et al. (2011). Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature, 476(7359),214–219.
    22. Khan JJ, Albarran JW, Lopez V, et al. (2010) Gender differences on chest pain perception associated with acute myocardial infarction in Chinese patients: a questionnaire survey. Journal of clinical nursing, 19(19-20),2720-2729.
    23. Kim HC, Greenland P, Rossouw JE, et al. (2010) Multimarker prediction of coronary heart disease risk: the Women’s Health Initiative. Journal of the American College of Cardiology, 55(19),2080-2091.
    24. Ripatti S, Tikkanen E, Orho-Melander M, et al. (2010) A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet (London, England), 376(9750),1393-1400.
    25. Berger JS, Jordan CO, Lloyd-Jones D, et al. (2010) Screening for cardiovascular risk in asymptomatic patients. Journal of the American College of Cardiology, 55(12),1169-1177.
    26. Dracup K. (2009) The challenge of reducing prehospital delay in patients with acute coronary syndrome. Circulation, Cardiovascular quality and outcomes, 2(3),144-145.
    27. Melander O, Newton-Cheh C, Almgren P, et al. (2009) Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. JAMA, 302(1),49-57.
    28. Shah T, Casas JP, Cooper JA, et al. (2009) Critical appraisal of CRP measurement for the prediction of coronary heart disease events: new data and systematic review of 31 prospective cohorts. International journal of epidemiology, 38(1),217-231.
    29. Rossouw JE, Cushman M, Greenland P, et al. (2008) Inflammatory, lipid, thrombotic, and genetic markers of coronary heart disease risk in the women’s health initiative trials of hormone therapy. Archives of internal medicine, 168(20),2245-2253.
    30. Ioannidis JP. (2008) Why most discovered true associations are inflated. Epidemiology (Cambridge, Mass) 19(5):640-648.
    31. Zethelius B, Berglund L, Sundström J, et al. (2008) Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. The New England journal of medicine, 358(20),2107-2116.
    32. Lobo JM, Jimenez-Valverde A, and Real R. (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17,145-151.
    33. Pencina MJ, D’Agostino Sr RB, D’Agostino RB, Jr, et al. (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine, 27(2),157-212.
    34. Ridker PM, Paynter NP, Rifai N, et al. (2008) C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Score for men. Circulation, 118(22),2243-2251.
    35. Rosamond W, Flegal K, Friday, G, et al. (2007) Heart disease and stroke statistics–2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation, 115(5),e69-e171.
    36. Ridker PM, Buring JE, Rifai N, et al. (2007) Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA, 297(6),611-619.
    37. Cook NR. (2007) Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation, 115(7),928-935. 
    38. Wang TJ, Gona P, Larson MG, et al. (2006) Multiple biomarkers for the prediction of first major cardiovascular events and death. The New England journal of medicine, 355(25),2631-2639.
    39. Doll R, Peto R, Boreham J, et al. (2004) Mortality in relation to smoking: 50 years’ observations on male British doctors. BMJ (Clinical research ed), 328(7455),1519.
    40. Granot M, Goldstein-Ferber S, and Azzam ZS. (2004) Gender differences in the perception of chest pain. Journal of pain and symptom management, 27(2),149-155.
    41. Yusuf S, Hawken S, Ounpuu S, et al. (2004) Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet, 364(9438),937-952.
    42. Chobanian AV, Bakris GL, Black HR, et al. (2003) The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA, 289(19),2560-2572.
    43. Naghavi M, Libby P, Falk E, et al. (2003) From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part I. Circulation, 108(14),1664-1672.
    44. McSweeney, JC, Cody M, O’Sullivan P, et al. (2003) Women’s early warning symptoms of acute myocardial infarction. Circulation, 108(21),2619-2623.
    45. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). (2002) Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation, 106(25),3143-3421.
    46. Greenland P, Smith SC Jr, & Grundy SM. (2001) Improving coronary heart disease risk assessment in asymptomatic people: role of traditional risk factors and noninvasive cardiovascular tests. Circulation, 104(15),1863-1867.
    47. Montgomery AA, Fahey T, MacKintosh C, et al. (2000) Estimation of cardiovascular risk in hypertensive patients in primary care. The British journal of general practice: the journal of the Royal College of General Practitioners, 50(451),127-128.
    48. de Lorgeril M, Salen P, Martin JL, et al. (1999) Mediterranean diet, traditional risk factors, and the rate of cardiovascular complications after myocardial infarction: final report of the Lyon Diet Heart Study. Circulation, 99(6),779-785.
    49. Goldberg RJ, O’Donnell C, Yarzebski J, et al. (1998) Sex differences in symptom presentation associated with acute myocardial infarction: a population-based perspective. American heart journal, 136(2),189-195.
    50. Wilson PW, D’Agostino RB, Levy D, et al. (1998) Prediction of coronary heart disease using risk factor categories. Circulation, 97(18),1837-1847.
    51. Parish S, Collins R, Peto R, et al. (1995) Cigarette smoking, tar yields, and non-fatal myocardial infarction: 14,000 cases and 32,000 controls in the United Kingdom. The International Studies of Infarct Survival (ISIS) Collaborators. BMJ (Clinical research ed), 311(7003),471-477.
    52. Kennel WB, McGee D, & Gordon T. (1976) A general cardiovascular risk profile: the Framingham Study. The American journal of cardiology, 38(1),46-51.

       

    SmartLung Dx

    1. Huo J, Xu Y, Sheu T, et al. (2019) Complication Rates and Downstream Medical Costs Associated With Invasive Diagnostic Procedures for Lung Abnormalities in the Community Setting. JAMA internal medicine, 179(3),324-332.
    2. Woodard GA, Wang SX, Kratz JR, et al. (2018) Adjuvant Chemotherapy Guided by Molecular Profiling and Improved Outcomes in Early Stage, Non-Small-Cell Lung Cancer. Clinical lung cancer, 19(1),58-64.
    3. Lokhandwala T, Bittoni MA, Dann RA, et al. (2017) Costs of Diagnostic Assessment for Lung Cancer: A Medicare Claims Analysis. Clinical lung cancer, 18(1),e27-e34.
    4. Gould MK, Tang T, Liu I, et al. (2015) Recent Trends in the Identification of Incidental Pulmonary Nodules. American journal of respiratory and critical care medicine, 192(10),1208-1214.
    5. Meza R, Meernik C, Jeon J, et al. (2015) Lung cancer incidence trends by gender, race and histology in the United States, 1973-2010. PLoS one, 10(3),e0121323.
    6. Kratz JR, He J, Van Den Eeden SK, et al. (2012) A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. Lancet (London, England), 379(9818):823-832.
    7. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. The New England journal of medicine, 365(5),395-409.
    8. Gould MK, Ananth L, Barnett PG, et al. (2007) A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest,131(2),383-388.
    9. Evans C, Evans M, & Pollock AV. (1974) The incidence and causes of postoperative jaundice. A prospective study. British journal of anaesthesia, 46(7),520-525.

    SmartKidney Dx

    1. von Roemeling CA, Caulfield TR, Marlow L, et al. (2017) Accelerated bottom-up drug design platform enables the discovery of novel stearoyl-CoA desaturase 1 inhibitors for cancer therapy. Oncotarget,9(1),3-20.
    2. Neely BA, Wilkins CE, Marlow LA, et al. (2016) Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma. PLoS one, 11(4),e0154074.
    3. von Roemeling CA, Radisky DC, Marlow LA, et al. (2014) Neuronal pentraxin 2 supports clear cell renal cell carcinoma by activating the AMPA-selective glutamate receptor-4. Cancer research, 74(17),4796-4810.
    4. Leung J & Kim WY. (2013) Stearoyl co-A desaturase 1 as a ccRCC therapeutic target: death by stress. Clinical cancer research: an official journal of the American Association for Cancer Research, 19(12),3111-3113.
    5. von Roemeling CA, Marlow LA, Kennedy WP, et al. (2013) Preclinical evaluation of the mTOR inhibitor, temsirolimus, in combination with the epothilone B analog, ixabepilone in renal cell carcinoma. American journal of cancer research, 3(4),390-401.
    6. von Roemeling CA, Marlow LA, Wei JJ, et al. (2013) Stearoyl-CoA desaturase 1 is a novel molecular therapeutic target for clear cell renal cell carcinoma. Clinical cancer research: an official journal of the American Association for Cancer Research, 19(9),2368-2380.
    7. Cooper SJ, Tun HW, Roper SM, et al. (2012) Current Status of Biomarker Discovery in Human clear Cell Renal Cell Carcinoma. Journal of Molecular Biomarkers & Diagnosis.
    8. Cooper SJ, von Roemeling CA, Kang KH, et al. (2012) Reexpression of tumor suppressor, sFRP1, leads to antitumor synergy of combined HDAC and methyltransferase inhibitors in chemoresistant cancers. Molecular cancer therapeutics, 11(10),2105-2115.
    9. Tun HW, Marlow LA, von Roemeling CA, et al. (2010) Pathway signature and cellular differentiation in clear cell renal cell carcinoma. PloS one, 5(5),e10696.
    10. Gumz ML, Zou H, Kreinest PA, et al. (2007) Secreted frizzled-related protein 1 loss contributes to tumor phenotype of clear cell renal cell carcinoma. Clinical cancer research: an official journal of the American Association for Cancer Research, 13(16),4740-4749. 

    Expanded Lipid Profile (ELP)

    • Burgess S, Ference BA, Staley JR, et al. (2018) Association of LPA Variants With Risk of Coronary Disease and the Implications for Lipoprotein(a)-Lowering Therapies: A Mendelian Randomization Analysis. JAMA Cardiol 3(7):619-627
    • Navarese EP, Robinson JG, Kowalewski M, et al. (2018) Association Between Baseline LDL-C Level and Total and Cardiovascular Mortality After LDL-C Lowering: A Systematic Review and Meta-analysis. JAMA 319(15):1566-1579
    • Ravnskov U, Diamond DM, Hama R, et al. (2016) Lack of an association or an inverse association between low-density-lipoprotein cholesterol and mortality in the elderly: a systematic review. BMJ Open 6(6):e010401
    • Silverman MG, Ference BA, Im K, et al. (2016) Association Between Lowering LDL-C and Cardiovascular Risk Reduction Among Different Therapeutic Interventions: A Systematic Review and Meta-analysis. JAMA 316(12):1289-97
    • Khetarpal SA and Rader DJ. (2015) Triglyceride-rich lipoproteins and coronary artery disease risk: new insights from human genetics. Arterioscler Thromb Vasc Biol 35(2):e3-9
    • Mozaffarian D and Ludwig DS. (2015) Dietary Cholesterol and Blood Cholesterol Concentrations-Reply. JAMA 314(19):2084-5
    • Srisawasdi P, Chaloeysup S, Teerajetgul Y, et al. (2011) Estimation of plasma small dense LDL cholesterol from classic lipid measures. Am J Clin Pathol 136(1):20-9
    • Sniderman A, Williams K, de Graaf J. (2010) Non-HDL C equals apolipoprotein B: except when it does not!. Curr Opin Lipidol 21(6):518-24
    • Harper CR and Jacobson TA. (2010) Using apolipoprotein B to manage dyslipidemic patients: time for a change? Mayo Clin Proc 85(5):440-5
    • Liu J, Sempos CT, Donahue RP, et al. (2006) Non-high-density lipoprotein and very-low-density lipoprotein cholesterol and their risk predictive values in coronary heart disease. Am J Cardiol 98(10):1363-8
    • Walldius G, Jungner I. (2006) The apoB/apoA-I ratio: a strong, new risk factor for cardiovascular disease and a target for lipid-lowering therapy–a review of the evidence. J Intern Med 259(5):493-519