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Dr Kaushala Naiwala Pathirannehelage
Kaushala Jayawardana is a Biostatistician in the Clinical Epidemiology and Biostatistics Unit (CEBU), within the Murdoch Children's Research Institute (MCRI). She is also a member of the Melbourne Children's Trials Centre (MCTC). She completed her PhD in Statistics at the University of Sydney in 2016, after obtaining a BSc (Honours) in Statistics at the University of Colombo, Sri Lanka in 2009. Since the completion of her PhD, she worked as a Bioinformatician at The Baker Heart and Diabetes Institute, where she worked on multiple clinical studies that involved clinical and lipidomics data.
Kaushala Jayawardana is currently the responsible statistician in a multi-site randomised trial, which is a platform trial that uses adaptive randomisation. In this role, she is responsible for all of the statistical aspects of the trial including overseeing the data collection and construction of the database. She is also conducting methodological research around this novel trial design.
- The University of Sydney - The Australian Postgraduate Award (APA) and International Postgraduate Research Scholarship (IPRS) (2011-2015).
- Winton Foundation Postgraduate Top-up Scholarship in Mathematical Biology(2013).
- Travel award - BioInfoSummer, Melbourne from the Australian Mathematical Sciences Institute (2011).
- Gold Medal - Best Final Year Project in Statistics (2009).
- and Mrs. Samaranayake Memorial Gold Medal for Statistics (2009).
- Studentship Awarded on academic excellence in the first year examinations (PhysicalScience Stream), University of Colombo, Sri Lanka (2005-2006).
- FORMaT (Finding the Optimal Regimen for Mycobacterium abscessus Treatment)
- Australian Trials Methodology (AusTriM) Research Network
- Tham, YK, Jayawardana, KS, Alshehry, ZH, Giles, C, Huynh, K, Smith, AAT, Ooi, JYY, Zoungas, S, Hillis, GS, Chalmers, J, Meikle, PJ and McMullen, JR. (2021). Novel Lipid Species for Detecting and Predicting Atrial Fibrillation in Patients with Type 2 Diabetes. Diabetes, 70(1): 255-261.
- Huynh, K, Lim, WLF, Giles, C, Jayawardana, KS, Salim, A, Mellett, NA, Smith, AAT, Olshansky, G, Drew, BG, Chatterjee, P, Martins, I, Laws, SM, Bush, AI, Rowe, CC, Villemagne, VL, Ames, D, Masters, CL, Arnold, M, Nho, K, Saykin, AJ, Baillie, R, Han, X, Kaddurah-Daouk, R, Martins, RN & Meikle, PJ. (2020). Concordant peripheral lipidome signatures in two large clinical studies of Alzheimer’s disease. Nat Commun 11, 5698.
- Jayawardana KS, Mundra, PA, Giles, C, Barlow, CK, Nestel, PJ, Barnes, EH, Kirby, A, Thompson, P, Sullivan, DR, Alshehry, ZH, Mellett, NA, Huynh, K, McConville, MJ, Zoungas, S, Hillis, GS, Chalmers, J, Woodward, M, Marschner, IC, Wong, G, Kingwell, BA, Simes, J, Tonkin, AM, Meikle, PJ (2019). Changes in plasma lipids predict pravastatin efficacy in secondary prevention. JCI Insight, 4, 13.
- Parker, BL, Calkin, AC, Seldin, MM, Keating, MF, Tarling, EJ, Yang, P, Moody, SC, Liu, Y, Zerenturk, EJ, Needham, EJ, Miller, ML, Cli ord, BL, Morand, P, Watt, MJ, Meex, RCR, Peng, KY, Lee, R, Jayawardana, K,Pan, C, Mellett, NA, Weir, JM, Lazarus, R, Lusis, AJ, Meikle, PJ, James, DE, de Aguiar Vallim, TQ, Drew, BG (2019). An integrative systems genetic analysis of mammalian lipid metabolism. Nature, 567, 7747:187-193.
- Meikle, PJ, Formosa, MF, Mellett, NA, Jayawardana, KS, Giles, C, Bertovic, DA, Jennings, GL, Childs, W, Reddy, M, Carey, AL, Baradi, A, Nanayakkara, S, Wilson, AM, Du y, SJ, Kingwell, BA (2019). HDL Phospholipids, but Not Cholesterol Distinguish Acute Coronary Syndrome From Stable Coronary Artery Disease. J Am Heart Assoc, 8, 11:e011792.
- Huynh, K, Barlow, CK, Jayawardana KS, Weir, JM, Mellett, NA, Cinel, M, Magliano, DJ, Shaw, JE, Drew, BG, Meikle, PJ (2019). High-Throughput Plasma Lipidomics: Detailed Mapping of the Associations with Cardiometabolic Risk Factors. Cell Chem Biol., 26, 1:71-84.
- Overgaard, AJ, Weir, JM, Jayawardana, K, Mortensen, HB, Pociot, F, Meikle, PJ (2018). Plasma lipid species at type 1 diabetes onset predict residual beta-cell function after 6 months. Metabolomics, 14, 12:158.
- Mundra, PA, Barlow, CK, Nestel, PJ, Barnes, EH, Kirby, A, Thompson, P, Sullivan, DR, Alshehry, ZH, Mellett, NA, Huynh, K, Jayawardana, KS, Giles, C, Mc-Conville, MJ, Zoungas, S, Hillis, GS, Chalmers, J, Woodward, M, Wong, G, Kingwell, BA, Simes, J, Tonkin, AM, Meikle, PJ (2018). Large-scale plasma lipidomic profi ling identifi es lipids that predict cardiovascular events in secondary prevention. JCI Insight, 3, 17.
- McCloskey, K, De Livera, AM, Collier, F, Ponsonby, AL, Carlin, JB, Vuillermin, P, Mellett, NA, Jayawardana, K, Weir, JM, Blangero, J, Curran, JE, Burgner, D, Meikle, PJ (2018). Gestational Age and the Cord Blood Lipidomic Profi le in Late Preterm and Term Infants. Neonatology, 114, 3:215-222.
- Peng, KY, Watt, MJ, Rensen, S, Greve, JW, Huynh, K,Jayawardana, KS, Meikle, PJ, Meex, RCR (2018). Mitochondrial dysfunction-related lipid changes occur in nonalcoholic fatty liver disease progression. J. Lipid Res., 59, 10:1977-1986.
- Nestel, PJ, Khan, AA, Straznicky, NE, Mellett, NA, Jayawardana, K, Mundra, PA, Lambert, GW, Meikle, PJ (2017). Markers of sympathetic nervous system activity associate with complex plasma lipids in metabolic syndrome subjects. Atherosclerosis, 256:21-28.
- Jayawardana, K, Schramm, SJ, Tembe, V, Mueller, S, Thompson, JF, Scolyer,RA, Mann, GJ, Yang, J (2016). Identi fication, Review, and Systematic Cross-Validation of microRNA Prognostic Signatures in Metastatic Melanoma. J. Invest. Dermatol., 136, 1:245-54.
- Jayawardana, K, Schramm, SJ, Haydu, L, Thompson, JF, Scolyer, RA, Mann, GJ, Mueller, S, Yang, JY (2015). Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information. Int. J. Cancer, 136, 4:863-74.
- Jayawardana, K, Mueller, S, Schramm, SJ, Mann, GJ, Yang, JY(2013). Vertical data integration for melanoma prognosis. In Proc. 59th World Statistics Congress of the International Statistical Institute, 3599-3604.
- Chung, SH, Shen, W, Jayawardana, K., Wang, P, Yang, YH, Shackel, N, Gillies, MC (2013) Differential gene expression profiling after conditional muller-cell ablation in a novel transgenic model. Investigative Ophthalmology and Visual Science, 54, 3:2142-2152.
- Jayawardana, NPKS, Sooriyarachchi, MR (2009). Prognostic Models with Competing Risks: Methods and Application to Prostate Cancer Data. Sri Lankan Journal of Applied Statistics, 10, 43-64.