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I specialise in disease diagnosis and classification, with a particular emphasis on cancer.
I am working on the translation of my cancer diagnostics to a commercially viable clinical tool.
I am a Fellow of the Higher Education Academy (FHEA).
Within Trinity I primarily give tutorials to first-year undergraduate Biochemistry students with topics covering the fundamentals of molecular and cellular biochemistry. Emphasis is given to DNA transcription and translation as well as the understanding of techniques used in genetic analysis and cloning.
My role in the Department of Oncology incorporates lectures on MRI applications and supervision of graduate students for the MSc Radiobiology course.
My main research focus is on the use of metabolomics for the diagnosis and classification of diseases. Metabolomics is a powerful technique which combines the analysis of biofluid samples, such as urine and blood plasma, by an analytical technique such as nuclear magnetic resonance (NMR) with multivariate statistical modelling techniques. Combined, metabolomics can classify samples into groups which I use for classification of disease states and disease diagnosis.
In this way, I have shown that it is possible to separate relapse-remitting and secondary progressive MS patients using a blood test in a way that is much more rapid (days) than current clinical diagnosis methods (typically up to a year). More recently, I have been expanding these methods into the diagnosis and classification of brain tumours and other cancers in models and patients.
NMR spectra for blood serum from relapsing-remitting multiple sclerosis (RRMS) and secondary progressive MS (SPMS) patients with insets showing areas of difference between them
My secondary research interest is in understanding the flow of blood into and around tumours. Tumours need nutrients and oxygen for growth and often have a very abnormal vasculature, grown in a disorganised and ad hoc fashion. I use perfusion and diffusion-based MRI techniques in models of brain metastasis to try and understand how abnormal vasculature gives rise to known tumour pathology, as well as trying to predict tumour locations using non-invasive imaging.
Multimodality MRI (green) much more accurately defines the extent of a confirmed brain tumour (blue) than an expert human observer delineating the same tumour (red)
I recently won a STEM for Britain award. You can find out more about my work here.
Larkin J.R., Dickens A.M., Claridge T.D.W., Bristow C., Andreou K., Anthony D.C., et al., ‘Early Diagnosis of Brain Metastases Using a Biofluids-Metabolomics Approach in Mice’, Theranostics 6(12) (2016), 2161–2169
Larkin J.R., Simard M.A., Bernardi A. de, Johanssen V.A., Perez-Balderas F., Sibson N.R., ‘Improving Delineation of True Tumor Volume With Multimodal MRI in a Rat Model of Brain Metastasis’, International Journal of Radiation Oncology, Biology, Physics 106(5) (Apr 2020), 1028–1038
Larkin J.R., Simard M.A., Khrapitchev A.A., Meakin J.A., Okell T.W., Craig M., et al., ‘Quantitative blood flow measurement in rat brain with multiphase arterial spin labelling magnetic resonance imaging’, J. Cereb. Blood Flow Metab. (2018)
Dickens A.M., Larkin J.R., Griffin J.L., Cavey A., Matthews L., Turner M.R., et al., ‘A type 2 biomarker separates relapsing-remitting from secondary progressive multiple sclerosis’, Neurology 83(17) (Oct 2014), 1492–1499
The future of disease diagnosis and classification is being driven by advanced biochemical technologies being developed today.