Neopterin

Per Christian Eriksen

Beate

Øivind

Per Magne Ueland has been Professor at the University of Bergen 1987-2018. He is one of the founders of Bevital AS and the scientific advisor in Bevital since 2023. His interests includes biomarkers related to nutrition, inflammation, ageing and life-style related chronic diseases. Per is committed to the development of precise, high-throughput mass spectrometry methods, tailored for metabolic profiling of biobank specimens from large cohorts.

Ove

Lena

Marit holds a degree in chemical engineering from Bergen Ingeniørhøyskole, which is now part of the Western Norway University of Applied Sciences. She works with quantitative analysis and method development on LC-MS/MS at the laboratory of Bevital AS.

Ove completed a bachelor’s degree in Biomedical Laboratory Sciences at the Western Norway University of Applied Sciences in Bergen. With extensive experience in method development and expertise in GC-MS/MS, he specializes in optimizing analytical techniques for research-focused studies. At Bevital, Ove is dedicated to advancing laboratory methods and workflows, contributing to innovative research through precise and reliable analytical solutions.

Lene holds a bachelor’s degree in Biomedical Laboratory Science from the Western Norway University of Applied Sciences, where she is also completing her master’s degree in Medical Laboratory Technology. At Bevital, she works with GC-MS/MS analyses, focusing on accurate and reliable testing of biological samples. With her strong laboratory background, Lene is committed to delivering high-quality results that support medical research.

Klaus holds a PhD in physics from the University of Münster in Germany. He has over three decades of experience in Time-of-Flight mass spectrometry. He leverages his extensive expertise to provide customers with cutting-edge MALDI-MS analysis and the newest Olink Proteomics services.

Adrian holds a PhD in diabetes research, along with bachelor’s and master’s degrees in biomedical science and public health, respectively. With over 20 years of experience in laboratory science, he leads high-precision metabolite analyses and method development at Bevital. His expertise centers on quantifying biomarkers, metabolite classes, and metabolic pathways related to nutrition, cardiovascular and neurodegenerative diseases, and cancer. Adrian is committed to advancing research quality and actively collaborates nationally and internationally, leveraging targeted metabolomics to support innovative, multidisciplinary research.

Statistical power is the probability that a statistical test will correctly reject a false null hypothesis (H0​) when a specific alternative hypothesis (H1​) is true. H0​ is the null hypothesis, which states there is no effect or no difference. H1​ is the alternative hypothesis, which states there is a real effect or difference. Alpha (α) is the probability of a Type I error (a false positive), which is the risk of incorrectly rejecting the H0​ when it is actually true. You set this value before the experiment, commonly at 0.05. Beta (β) is the probability of a Type II error (a false negative), which is the risk of failing to reject the H0​ when it is actually false.

Power is calculated as 1−β. Increasing power means you are decreasing the probability of making a Type II error.

Several factors can be adjusted to increase the power of a statistical test:

• Effect Size: This is the magnitude of the difference you are trying to detect. A larger effect size is easier to detect, thus increasing power. 

• Sample Size: The number of observations in a study. A larger sample size provides more information about the population, reducing the margin of error and increasing the power to detect a true effect.

• Variation: Refers to the spread or standard deviation of the data within the population. Less variation makes it easier to distinguish a real effect from random noise, thereby increasing power.

• Alpha ($\alpha$): Increasing the alpha level (e.g., from 0.05 to 0.10) also increases power, but at the cost of a higher risk of a Type I error. This trade-off is often undesirable.