I am a postdoctoral research associate at the National Phenome Centre in the section of Bioanalytical Chemistry. My research interests focus on all aspects of NMR spectroscopy and its application to bio-analytical and metabolomics studies along with basic handling of mass spectrometry metabolomics data. Currently, I am working and interested in the development of NMR-based bio- chem- informatics software based upon signal processing etc. for the deconvolution of complex mixtures such as biofluids, aiming at the rapid assignment and quantification of diagnostic and prognostic markers for disease risk and personalized medicine.
For more details about my work on NMR spectroscopy/computational developments for biophysics, metabolomics and bioanalytical chemistry research, see the publication, software and patent list below.
- Correia, G.D.S.*, Takis, P. G.*, Sands, C.J., Kowalka, A.M., Tan, T., Turtle, L., Ho, A., Semple, M.G., Openshaw, P.J.M., Baillie, J.K., Takáts, Z., Lewis. M.R. (2022) ‘1H NMR Signals from Urine Excreted Protein Are a Source of Bias in Probabilistic Quotient Normalization’, Analytical Chemistry (https://doi.org/10.1021/acs.analchem.2c00466)
- Takis, P. G.*, Jimenez, B., Al-Saffar, N.M.S., Harvey, N., Misra, S., Chekmeneva, E., Lewis. M.R (2021) ‘A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D 1H NMR’, Analytical Chemistry (https://doi.org/10.1021/acs.analchem.1c00113)
- Takis, P. G.*, Jimenez, B., Sands, C.J., Chekmeneva, E., Lewis. M.R. (2020) ‘SMolESY: an efficient and quantitative alternative to on-instrument macromolecular 1H-NMR signals suppression’, Chemical Science (https://doi.org/10.1039/D0SC01421D)†
- Takis, P. G., Ghini, V., Tenori, L., Turano, P., Luchinat, C. (2019) ‘Uniqueness of the NMR approach to metabolomics’, Trends in Analytical Chemistry (TrAC), 120, 115300 (https://doi.org/10.1016/j.trac.2018.10.036)
- Takis, P. G., Schäfer, H., Spraul, M., Luchinat, C. (2017) ‘Deconvoluting interrelationships between chemical shifts and concentrations in complex mixtures: a powerful biofluid analysis tool’, Nature Communications, 8, 1662 (https://doi.org/10.1038/s41467-017-01587-0)
†Highlighted in Chemistry World:
et al., 2022, NMR-based metabolomics in differential diagnosis of Chronic Kidney Disease (CKD) subtypes, Metabolites, Vol:12, ISSN:2218-1989, Pages:1-14
et al., 2022, 1H NMR Signals from urine excreted protein are a source of bias in probabilistic quotient normalization, Analytical Chemistry, Vol:94, ISSN:0003-2700, Pages:6919-6923
et al., 2022, Implementation of corticosteroids in treatment of COVID-19 in the ISARIC WHO Clinical Characterisation Protocol UK: prospective, cohort study., The Lancet Digital Health, Vol:4, ISSN:2589-7500, Pages:e220-e234
et al., 2022, SARS-CoV-2 Omicron-B.1.1.529 leads to widespread escape from neutralizing antibody responses, Cell, Vol:185, ISSN:0092-8674, Pages:467-484.e15
et al., 2021, Impact of cardiometabolic multimorbidity and ethnicity on cardiovascular/renal complications in patients with COVID-19, Heart, ISSN:1355-6037