Endothelin-1 Serum Concentration is Lower in Children and Adolescents with High Myopia, a Preliminary Study.

The aim of this study is to evaluate the levels of enothelin-1 (ET-1) in children and adolescents with high myopia and its association with the axial length of the eye and the presence of myopic retinal degeneration. The cross-sectional study was carried out in 57 patients with high myopia and 29 control subjects. Serum concentrations of ET-1 were measured using enzyme-linked immunosorbent assay (ELISA) kit. A significantly lower concentration of ET-1 in highly myopic patients compared to controls was found (1.47 (0.91; 1.87) vs. 1.94 (1.1; 2.69) pg/mL, p = 0.005). In patients with high myopia, a weak negative correlation between ET-1 concentration and the longest axial length out of the two eyes was found (r = -0.255, p = 0.0558). Further analysis revealed statistically significant differences in ET-1 concentration between patients with the axial length of the eye > 26 and ≤ 26 mm (p < 0.041) and patients with the axial length of the eye > 26 mm and controls (p < 0.001). ET-1 expression is disturbed in highly myopic children and adolescents. Lower ET-1 concentration in patients with the axial length of the eye > 26 mm may co-occur with high myopia and should be considered a risk factor in the pathophysiology of high myopia progression.

Learning from M/EEG data with variable brain activation delays.

Magneto- and electroencephalography (M/EEG) measure the electromagnetic signals produced by brain activity. In order to address the issue of limited signal-to-noise ratio (SNR) with raw data, acquisitions consist of multiple repetitions of the same experiment. An important challenge arising from such data is the variability of brain activations over the repetitions. It hinders statistical analysis such as prediction performance in a supervised learning setup. One such confounding variability is the time offset of the peak of the activation, which varies across repetitions. We propose to address this misalignment issue by explicitly modeling time shifts of different brain responses in a classification setup. To this end, we use the latent support vector machine (LSVM) formulation, where the latent shifts are inferred while learning the classifier parameters. The inferred shifts are further used to improve the SNR of the M/EEG data, and to infer the chronometry and the sequence of activations across the brain regions that are involved in the experimental task. Results are validated on a long-term memory retrieval task, showing significant improvement using the proposed latent discriminative method.