The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach.

We examined, on 28 healthy adult subjects, the possible correlations of an exhaustive exercise, and the consequent high blood lactate levels, on immediate (explicit) and delayed (implicit) motor execution of sequential finger movements (cognitive task). Moreover, we determined with transcranial magnetic stimulation whether changes in motor performance are associated with variations in excitability of primary motor area (M1) and supplementary motor area (SMA). We observed that, after an acute exhaustive exercise, the large increase of blood lactate is associated with a significant worsening of both explicit and implicit sequential visuomotor task paradigms, without gender differences. We also found that, at the end of the exhaustive exercise, there is a change of excitability in both M1 and SMA. In particular, the excitability of M1 was increased whereas that of SMA decreased and, also in this case, without gender differences. These results support the idea that an increase of blood lactate after an exhaustive exercise appears to have a protective effect at level of primary cortical areas (as M1), although at the expense of efficiency of adjacent cortical regions (as SMA).

The aim of this research was to assess the impact of a well-characterized extract fromCitrus bergamiajuice on adipogenesis and/or lipolysis using mesenchymal stem cells from human adipose tissue as a cell model. To evaluate the effects on adipogenesis, some cell cultures were treated with adipogenic medium plus 10 or 100 μg/mL of extract. To determine the properties on lipolysis, additional mesenchymal stem cells were cultured with adipogenic medium for 14 days and after this time added withCitrus bergamiafor further 14 days. To verify adipogenic differentiation, oil red O staining at 7, 14, 21, and 28 days was performed. Moreover, the expression of peroxisome proliferator-activated receptor gamma (PPAR-γ), adipocytes fatty acid-binding protein (A-FABP), adipose triglyceride lipase (ATGL), hormone-sensitive lipase (HSL), monoglyceride lipase (MGL), 5′-adenosine monophosphate-activated protein kinase (AMPK)α1/2, and pAMPKα1/2 was evaluated by Western blot analysis and the release of glycerol by colorimetric assay.Citrus bergamiaextract suppressed the accumulation of intracellular lipids in mesenchymal stem cells during adipogenic differentiation and promoted lipolysis by repressing the expression of adipogenic genes and activating lipolytic genes.Citrus bergamiaextract could be a useful natural product for improving adipose mobilization in obesity-related disorders.