Stylianos (Stelios) Serghiou joined the PhD program in Epidemiology and Clinical Research in 2016. After spending two years as a second lieutenant for the National Guard of Cyprus, he attended the University of Edinburgh, where he obtained a BSc with Honors in Neuroscience in 2012 and received his medical degree (MBChB) with Honors in 2015. He was then awarded a post and worked for a year as an Academic Foundation Doctor for NHS Lothian and as an Honorary Clinical Fellow of the University of Edinburgh. Over his time in Edinburgh, he obtained multiple awards, including scholarships by the Royal College of Physicians of UK and Ireland, Royal College of Ophthalmologists and being an AMGEN scholar. He is moving to Stanford to work on his interests in improving the quality of medical research and exploring the application of novel computational techniques to analyzing big data in medicine. Alongside medicine, he is especially interested in music and had served as the leader of the National Youth Orchestra of Cyprus and a first violin of the University of Edinburgh Symphony Orchestra.
Honors & Awards
Patric-Trevor Award, Royal College of Ophthalmology (2014)
Intercalated BSc Scholarship, Royal College of Physicians of UK and Ireland (2011)
AMGEN Scholar, University of Cambridge, AMGEN (2009)
Education & Certifications
BSc (Hons) Neuroscience, University of Edinburgh, Neuroscience (2012)
MBChB (Honors), University of Edinburgh, Medicine (2015)
Long noncoding RNAs as novel predictors of survival in human cancer: a systematic review and meta-analysis
Expression of various long noncoding RNAs (lncRNAs) may affect cancer prognosis. Here, we aim to gather and examine all evidence on the potential role of lncRNAs as novel predictors of survival in human cancer.We systematically searched through PubMed, to identify all published studies reporting on the association between any individual lncRNA or group of lncRNAs with prognosis in human cancer (death or other clinical outcomes). Where appropriate, we then performed quantitative synthesis of those results using meta-analytic methods to identify the true effect size of lncRNAs on cancer prognosis. The reliability of those results was then examined using measures of heterogeneity and testing for selective reporting biases.Three hundred ninety-two studies were screened to eventually identify 111 eligible studies on 127 datasets. In total, these represented 16,754 independent participants pertaining to 53 individual and 6 grouped lncRNAs within a total of 19 cancer sites. Overall, 83 % of the studies we identified addressed overall survival and 32 % of the studies addressed recurrence-free survival. For overall survival, 96 % (88/92) of studies identified a statistically significant association of lncRNA expression to prognosis. Meta-analysis of 6 out of 7 lncRNAs for which three or more studies were available, identified statistically significant associations with overall survival. The lncRNA HOTAIR was by far the most broadly studied lncRNA (n = 29; of 111 studies) and featured a summary hazard ratio (HR) of 2.22 (95 % confidence interval (CI), 1.86-2.65) with modest heterogeneity (I(2) = 49 %; 95 % CI, 14-79 %). Prominent excess significance was demonstrated across all meta-analyses (p-value = 0.0003), raising the possibility of substantial selective reporting biases.Multiple lncRNAs have been shown to be strongly associated with prognosis in diverse cancers, but substantial bias cannot be excluded in this field and larger studies are needed to understand whether these prognostic information may eventually be useful.
View details for DOI 10.1186/s12943-016-0535-1
View details for Web of Science ID 000379401400001
View details for PubMedID 27352941
Field-wide meta-analyses of observational associations can map selective availability of risk factors and the impact of model specifications
JOURNAL OF CLINICAL EPIDEMIOLOGY
2016; 71: 58-67
Instead of evaluating one risk factor at a time, we illustrate the utility of "field-wide meta-analyses" in considering all available data on all putative risk factors of a disease simultaneously.We identified studies on putative risk factors of pterygium (surfer's eye) in PubMed, EMBASE, and Web of Science. We mapped which factors were considered, reported, and adjusted for in each study. For each putative risk factor, four meta-analyses were done using univariate only, multivariate only, preferentially univariate, or preferentially multivariate estimates.A total of 2052 records were screened to identify 60 eligible studies reporting on 65 putative risk factors. Only 4 of 60 studies reported both multivariate and univariate regression analyses. None of the 32 studies using multivariate analysis adjusted for the same set of risk factors. Effect sizes from different types of regression analyses led to significantly different summary effect sizes (P-value < 0.001). Observed heterogeneity was very high for both multivariate (median I(2), 76.1%) and univariate (median I(2), 85.8%) estimates. No single study investigated all 11 risk factors that were statistically significant in at least one of our meta-analyses.Field-wide meta-analyses can map availability of risk factors and trends in modeling, adjustments and reporting, as well as the impact of differences in model specification.
View details for DOI 10.1016/j.jclinepi.2015.09.004
View details for Web of Science ID 000370908900009
View details for PubMedID 26415577
Comparative assessment of phototherapy protocols for reduction of oxidative stress in partially transected spinal cord slices undergoing secondary degeneration.
2016; 17 (1): 21
Red/near-infrared light therapy (R/NIR-LT) has been developed as a treatment for a range of conditions, including injury to the central nervous system (CNS). However, clinical trials have reported variable or sub-optimal outcomes, possibly because there are few optimized treatment protocols for the different target tissues. Moreover, the low absolute, and wavelength dependent, transmission of light by tissues overlying the target site make accurate dosing problematic.In order to optimize light therapy treatment parameters, we adapted a mouse spinal cord organotypic culture model to the rat, and characterized myelination and oxidative stress following a partial transection injury. The ex vivo model allows a more accurate assessment of the relative effect of different illumination wavelengths (adjusted for equal quantal intensity) on the target tissue. Using this model, we assessed oxidative stress following treatment with four different wavelengths of light: 450 nm (blue); 510 nm (green); 660 nm (red) or 860 nm (infrared) at three different intensities: 1.93 × 10(16) (low); 3.85 × 10(16) (intermediate) and 7.70 × 10(16) (high) photons/cm(2)/s. We demonstrate that the most effective of the tested wavelengths to reduce immunoreactivity of the oxidative stress indicator 3-nitrotyrosine (3NT) was 660 nm. 860 nm also provided beneficial effects at all tested intensities, significantly reducing oxidative stress levels relative to control (p ≤ 0.05).Our results indicate that R/NIR-LT is an effective antioxidant therapy, and indicate that effective wavelengths and ranges of intensities of treatment can be adapted for a variety of CNS injuries and conditions, depending upon the transmission properties of the tissue to be treated.
View details for DOI 10.1186/s12868-016-0259-6
View details for PubMedID 27194427
Risk of Bias in Reports of In Vivo Research: A Focus for Improvement
2015; 13 (10)
The reliability of experimental findings depends on the rigour of experimental design. Here we show limited reporting of measures to reduce the risk of bias in a random sample of life sciences publications, significantly lower reporting of randomisation in work published in journals of high impact, and very limited reporting of measures to reduce the risk of bias in publications from leading United Kingdom institutions. Ascertainment of differences between institutions might serve both as a measure of research quality and as a tool for institutional efforts to improve research quality.
View details for DOI 10.1371/journal.pbio.1002273
View details for Web of Science ID 000364457500008
View details for PubMedID 26460723