PurposeElectroencephalogram (EEG) alpha asymmetry (AA) in depressive disorders has been of interest over the last few decades, but it continues to remain unclear whether EEG AA can discriminate between healthy and depressive individuals.Materials and methodsA systematic literature search for papers addressing EEG AA using the keywords alpha asymmetry, depression, and EEG was performed in PubMed. All studies were checked for sample size, gender, handedness, reference, recording protocol, EEG band range, impedance, type of analysis, drugs, and comorbidity.ResultsA total of 61 articles were found, of which 44 met our inclusion criteria. They have been consecutively analyzed in respect of methodology and results. Approximately 25% (11/44) of the studies did not mention or ignored handedness, 41% (18/44) of the studies used data with only self-reported handedness, and only 34.1% (15/44) of all studies tested handedness. Only 35% (15/44) of the studies reported pharmacological treatment, and only 35% (15/44) of the studies controlled for medication. A total of 52% (23/44) of the studies reported comorbidity, and only 30% (13/44) of the studies controlled for comorbidity. Only 29.6% (13/44) of the studies reported education. In all, 30.5% (13/44) of the studies analyzed group differences and correlations, while 15.9 (7/44) of the studies used only correlational analyses. A total of 52.3% (23/44) of the studies analyzed only group differences. Alpha range was fixed (8–13 Hz) in 59.1% (26/44) of all studies. Reference to common average was used in seven of 44 studies (15.9%). In all, nine of 44 (20.5%) studies used the midline central position as reference, 22 of 44 (50%) studies used the ear or the mastoid as reference, and four of 44 (9.1%) studies used the nose as reference.ConclusionDiscriminative power of EEG AA for depressed and healthy controls remains unclear. A systematic analysis of 44 studies revealed that differences in methodology and disregarding proper sampling are problematic. Ignoring handedness, gender, age, medication, and comorbidity could explain inconsistent findings. Hence, we formulated a guideline for requirements for future studies on EEG AA in order to allow for better comparisons.

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The collateral damage caused by COVID-19 pandemic-associated public health and governmental measures on patient care has been increasingly assessed in various oncological and non-oncological clinical settings. In order to investigate potential adverse effects in the field of melanoma the present study analyzed the characteristics of primary melanoma diagnoses at an Austrian dermato-pathological referral center before, during, and after the first coronavirus-related lockdown in March 2020. As suspected, we found significant temporary reductions in the number of newly diagnosed melanomas in 2020 compared to previous years, in particular, during the first lockdown period.

The alpha asymmetry in EEG seemed to disappear with age. Correlations between PAA and anxiety and depression were found. The results are in line with the right (hemisphere) hemi-aging hypothesis.

Aims Despite their low individual metastatic potential, thin melanomas (≤1 mm Breslow thickness) contribute significantly to melanoma mortality overall. Therefore, identification of prognostic biomarkers is particularly important in this subgroup of melanoma. Prompted by preclinical results, we investigated cyclin D1 protein and Ki‐67 expression in in‐situ, metastatic and non‐metastatic thin melanomas. Methods and results Immunohistochemistry was performed on 112 melanoma specimens, comprising 22 in situ, 48 non‐metastatic and 42 metastatic thin melanomas. Overall, epidermal and dermal cyclin D1 and Ki‐67 expression were semiquantitatively evaluated by three independent investigators and compared between groups. Epidermal Ki‐67 expression did not differ statistically in in‐situ and invasive melanoma (P = 0.7). Epidermal cyclin D1 expression was significantly higher in thin invasive than in in‐situ melanoma (P = 0.003). No difference was found in cyclin D1 expression between metastatic and non‐metastatic invasive tumours. Metastatic and non‐metastatic thin melanomas did not show significant differences in epidermal expression of Ki‐67 and cyclin D1 (P = 0.148 and P = 0.611, respectively). In contrast, strong dermal expression of Ki‐67 was more frequent in metastatic than non‐metastatic samples (28.6 versus 8.3%, respectively, P = 0.001). The prognostic value of dermal Ki‐67 expression was confirmed by multivariate analysis (P = 0.047). Conclusion We found an increased expression of cyclin D1 in invasive thin melanomas compared to in‐situ melanomas, which supports a potential role of this protein in early invasion in melanoma, as suggested by preclinical findings. Moreover, our results confirm that high dermal Ki‐67 expression is associated with an increased risk of development of metastasis in thin melanoma and could possibly serve as a prognostic biomarker in clinical practice, especially if combined with additional methods.

With the increasing use of real-time monitoring procedures in clinical practice, psychological time series become available to researchers and practitioners. An important interest concerns the identification of pattern transitions which are characteristic features of psychotherapeutic change. Change Point Analysis (CPA) is an established method to identify the point where the mean and/or variance of a time series change, but changes of other and more complex features cannot be detected by this method. In this study, an extension of the CPA, the Pattern Transition Detection Algorithm (PTDA), is optimized and validated for psychological time series with complex pattern transitions. The algorithm uses the convergent information of the CPA and other methods like Recurrence Plots, Time Frequency Distributions, and Dynamic Complexity. These second level approaches capture different aspects of the primary time series. The data set for testing the PTDA (300 time series) is created by an instantaneous control parameter shift of a simulation model of psychotherapeutic change during the simulation runs. By comparing the dispersion of random change points with the real change points, the PTDA determines if the transition point is significant. The PTDA reduces the rate of false negative and false positive results of the CPA below 5% and generalizes its application to different types of pattern transitions. RQA quantifiers also can be used for the identification of nonstationary transitions in time series which was illustrated by using Determinism and Entropy. The PTDA can be easily used with Matlab and is freely available at Matlab File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/80380-pattern-transition-detection-algorithm-ptda).

Human scabies is usually transmitted during skin-to-skin contact with persons infested with the ectoparasitic mite Sarcoptes scabiei var. hominis. Scabies is a rising major public health issue worldwide and has recently been designated as a neglected tropical disease by the World Health Organization (WHO). 1 Current guidelines propose a range

EEG beta 2 power recorded during a backward counting condition with eyes closed can be used as surrogate marker for verbal memory impairment in geriatric patients. Antidepressant treatment was correlated with EEG data in resting state but not in counting condition. Further studies are necessary to verify the results of this pilot study.