Anti-Hepatocyte Growth Factor polyclonal antibody

Background/Aim: FGF-2, HGF, MIF and PTN have been suggested as biomarkers for testicular germ cell cancer patients in earlier studies. Our study was designed to validate these potential novel tumor markers. Materials and Methods: Serum FGF-2, HGF, MIF and PTN levels were analysed using an ELISA technique in a screening cohort of 20 testicular germ cell cancer patients and 10 healthy men. MIF levels were measured in a validation cohort of 84 patients with testicular cancer, 24 with non-malignant testicular tumors and 64 healthy men. Results: Serum FGF-2, HGF and PTN levels did not differ in cancer patients and healthy males within the screening cohort, whereas MIF was significantly increased among cancer patients. Within the validation cohort, a modest but insignificant increase of serum MIF was observed in TGCT patients compared to healthy men. MIF levels were not correlated with adverse clinical-pathological parameters. Conclusion: FGF-2, HGF, MIF and PTN are not suitable as non-invasive biomarkers for testicular germ cell cancer patients.

It has been proposed that accurate motor control relies on Bayesian inference that integrates sensory input with prior contextual knowledge (Bays and Wolpert 2007; Kording and Wolpert, 2004; Wolpert et al., 1995). Recent evidence has suggested that modulations in beta power (similar to 12-30 Hz) measured over sensorimotor cortices using electroencephalography (EEG) may represent parameters of Bayesian inference. While the well characterised post-movement beta synchronisation has been shown to correlate with prediction error (H. Tan, Jenkinson, & Brown, 2014; Huiling Tan, Wade, & Brown, 2016), recent evidence suggests that beta power may also represent uncertainty measures (Tan et al., 2016; Tzagarakis et al., 2015). The current study aimed to measure the neurophysiological correlates of uncertainty mediating Bayesian updating during a visuomotor adaptation paradigm in healthy human participants. In particular, sensory uncertainty was directly modulated to measure its effect on sensorimotor beta power. Participant's behaviour was modelled using the Hierarchical Gaussian Filter (HGF) in order to extract the latent variables involved in learning actions required by the task and correlate these with the measured EEG. We found that sensorimotor beta power correlated with inverse uncertainty afforded to sensory prediction errors both prior to and following a movement. This suggests that sensorimotor beta oscillations may more readily represent relative uncertainty within the sensorimotor system rather than error. Neurophysiological models describing the generation of beta oscillations offer a potential mechanism by which this neural signature may encode latent uncertainty parameters. This is essential for understanding how the brain controls behaviour.