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  • Surprisingly Zhao et al did

    2018-11-07

    Surprisingly, Zhao et al. () did not replicate findings from large-scale and meta-analytic MDD studies of reduced cortical thickness in the ACC and insular cortex (). Rather, the findings were in the opposite direction. This is also surprising given the recent report of reduced gray matter volume in these regions being a common neural substrate across a range of psychiatric disorders including MDD and SAD (). The inconsistencies may probably be explained by many different factors including differences in study populations (e.g. non-comorbid and medication-naïve in Zhao et al.). We must also be open for the possibility of spurious findings given the relatively small sample size in Zhao et al., although the authors do safeguard against this as best as possible with a stringent statistical threshold. The findings from Zhao et al. () support the extension of neural models of SAD from fear circuitry and amygdala-centered models to models including additional beta lactamase inhibitor regions and networks such as cortico-striato-thalamic-cortical circuitry and the salience and attention networks. Indeed, there is rather limited evidence of changes in amygdala volume in the SAD literature. It should also be noted that the findings from previous studies investigating gray matter alterations in SAD are mixed and no clear picture has emerged (). We recently applied pattern recognition on voxel-wise regional gray matter volume () and found that accurate separation of patients with SAD from healthy controls could only be achieved from the pattern of gray matter volume from the whole brain, not specific regions. Based on these results and the highly variable findings from previous studies using univariate methods, we proposed that gray matter alterations in SAD are best described as diffuse and widespread. However, it should be noted that studies on gray matter alterations in SAD, including our own, have often been small, with only few studies including more than 40 patients, and that the results may further be confounded by methodological heterogeneity, previous medication, and comorbidity. In this respect, the paper by Zhao et al. () represents a welcome contribution to the field by investigating two different aspects of brain structure in the same individuals and only including non-comorbid and treatment-naïve patients. The impact of neuroimaging on clinical psychiatric practice has been very limited, and this will most probably be true also for the study by Zhao et al. However, this might be about to change, as in recent years, machine learning pattern recognition techniques applied to neuroimaging data has produced some interesting findings, including that brain scans may be useful for discriminating between psychiatric disorders () and predict who will respond to treatment (). If these findings stand the test of replication and validation in independent samples, clinicians might soon order MRIs to be used in diagnosis and treatment selection. The distinct pattern of gray matter alterations in MDD and SAD found by Zhao et al. () indicates that these disorders would be separable by automatic methods, with the caveat that the present results are based on group-level comparisons and not on the individual patient level.
    Introduction With the recent advances in mass spectrometry (MS) based-proteomics, the application of top-down MS-based proteomic strategies now allows the analysis of complex protein mixtures in their intact state without the need for enzymatic digestion (Tran et al., 2011). In a study by Ye et al., top-down MS-based proteomics coupled to Matrix Assisted Laser Desorption Ionization (MALDI) MS imaging (MALDI-MSI) of a rat brain post-treated with the NMDA receptor antagonist MK801 revealed 34 proteins with their specific post-translational modifications (PTMs) (Ye et al., 2014). Recently, we performed MALDI-MSI coupled to top-down tissue microproteomics on 3 rat brain regions and demonstrated the possibility to identify specific proteoforms linked to the physiology of the tissue region; several unique markers were identified showing different proteoforms of brain-specific proteins (data not shown). In this work, we investigated the pathological heterogeneity in ovarian serous cancer tumor microenvironment utilizing a top-down microproteomics approach. Specifically, we investigated proteome microenvironment alterations aiming to delineate and characterize specific protein profiles in benign, tumor and necrotic/fibrotic tumor regions by taking into account their PTMs and assessing their cleaved forms.