Mosematthews0329

criptive and can be used to generate potentially useful insights on the predictions.The neurobiological mechanisms that mediate psychiatric comorbidities associated with metabolic disorders such as obesity, metabolic syndrome and diabetes remain obscure. High fructose corn syrup (HFCS) is widely used in beverages and is often included in food products with moderate or high fat content that have been linked to many serious health issues including diabetes and obesity. However, the impact of such foods on the brain has not been fully characterized. Here, we evaluated the effects of long-term consumption of a HFCS-Moderate Fat diet (HFCS-MFD) on behavior, neuronal signal transduction, gut microbiota, and serum metabolomic profile in mice to better understand how its consumption and resulting obesity and metabolic alterations relate to behavioral dysfunction. Mice fed HFCS-MFD for 16 weeks displayed enhanced anxiogenesis, increased behavioral despair, and impaired social interactions. Furthermore, the HFCS-MFD induced gut microbiota dysbiosis and lowered serum levels of serotonin and its tryptophan-based precursors. Importantly, the HFCS-MFD altered neuronal signaling in the ventral striatum including reduced inhibitory phosphorylation of glycogen synthase kinase 3β (GSK3β), increased expression of ΔFosB, increased Cdk5-dependent phosphorylation of DARPP-32, and reduced PKA-dependent phosphorylation of the GluR1 subunit of the AMPA receptor. These findings suggest that HFCS-MFD-induced changes in the gut microbiota and neuroactive metabolites may contribute to maladaptive alterations in ventral striatal function that underlie neurobehavioral impairment. learn more While future studies are essential to further evaluate the interplay between these factors in obesity and metabolic syndrome-associated behavioral comorbidities, these data underscore the important role of peripheral-CNS interactions in diet-induced behavioral and brain function. This study also highlights the clinical need to address neurobehavioral comorbidities associated with obesity and metabolic syndrome.Alzheimer disease (AD) is the most prevalent neurodegenerative disease in the elderly, characterized by accumulation in the brain of misfolded proteins, inflammation, and oxidative damage leading to neuronal cell death. By considering the viewpoint that AD onset and worsening may be influenced by environmental factors causing infection, oxidative stress, and inflammatory reaction, we investigated the changes of the salivary proteome in a population of patients with respect to that in healthy controls (HCs). Indeed, the possible use of saliva as a diagnostic tool has been explored in several oral and systemic diseases. Moreover, the oral cavity continuously established adaptative and protective processes toward exogenous stimuli. In the present study, qualitative/quantitative variations of 56 salivary proteoforms, including post-translationally modified derivatives, have been analyzed by RP-HPLC-ESI-IT-MS and MS/MS analyses, and immunological methods were applied to validate MS results. The salivary protein profile of AD patients was characterized by significantly higher levels of some multifaceted proteins and peptides that were either specific to the oral cavity or also expressed in other body districts (i) peptides involved in the homeostasis of the oral cavity; (ii) proteins acting as ROS/RNS scavengers and with a neuroprotective role, such as S100A8, S100A9, and their glutathionylated and nitrosylated proteoforms; cystatin B and glutathionylated and dimeric derivatives; (iii) proteins with antimicrobial activity, such as α-defensins, cystatins A and B, histatin 1, statherin, and thymosin β4, this last with a neuroprotective role at the level of microglia. These results suggested that, in response to injured conditions, Alzheimer patients established defensive mechanisms detectable at the oral level. Data are available via ProteomeXchange with identifier PXD021538.Little progress has been made in the long-term management of malignant brain tumors, leaving patients with glioblastoma, unfortunately, with a fatal prognosis. Glioblastoma remains the most aggressive primary brain cancer in adults. Similar to other cancers, glioblastoma undergoes a cellular metabolic reprogramming to form an oxidative tumor microenvironment, thereby fostering proliferation, angiogenesis and tumor cell survival. Latest investigations revealed that micronutrients, such as selenium, may have positive effects in glioblastoma treatment, providing promising chances regarding the current limitations in surgical treatment and radiochemotherapy outcomes. Selenium is an essential micronutrient with anti-oxidative and anti-cancer properties. There is additional evidence of Se deficiency in patients suffering from brain malignancies, which increases its importance as a therapeutic option for glioblastoma therapy. It is well known that selenium, through selenoproteins, modulates metabolic pathways and regulates redox homeostasis. Therefore, selenium impacts on the interaction in the tumor microenvironment between tumor cells, tumor-associated cells and immune cells. In this review we take a closer look at the current knowledge about the potential of selenium on glioblastoma, by focusing on brain edema, glioma-related angiogenesis, and cells in tumor microenvironment such as glioma-associated microglia/macrophages.It has been shown that conclusions about the human mental state can be drawn from eye gaze behavior by several previous studies. For this reason, eye tracking recordings are suitable as input data for attentional state classifiers. In current state-of-the-art studies, the extracted eye tracking feature set usually consists of descriptive statistics about specific eye movement characteristics (i.e., fixations, saccades, blinks, vergence, and pupil dilation). We suggest an Imaging Time Series approach for eye tracking data followed by classification using a convolutional neural net to improve the classification accuracy. We compared multiple algorithms that used the one-dimensional statistical summary feature set as input with two different implementations of the newly suggested method for three different data sets that target different aspects of attention. The results show that our two-dimensional image features with the convolutional neural net outperform the classical classifiers for most analyses, especially regarding generalization over participants and tasks.