BackgroundAtopic dermatitis (AD) is characterized by pruritic and eczematous skin lesions, which often cause depressive symptoms, anxiety, stress, sleep disturbances, social withdrawal, and stigmatization.MethodsIn total, 23,442 subjects (434 AD patients and 23,008 control subjects) aged 19 years or older and without a history of major medical illness or depressive disorders were selected from The Fifth Korea National Health and Nutrition Examination Survey 2007-2012. Following the initial selection, 2,170 age- and sex-matched control subjects were selected using 1:5 propensity score matching. Multiple logistic regression analysis was performed to identify the presence of depressive symptoms of at least 2 weeks in duration.ResultsThe demographic, socioeconomic, and clinical characteristics of AD patients and control subjects were presented and compared, and some variables differed significantly between groups. Presence of depressive symptoms was set as dependent variable, and multiple logistic regression analysis was performed as follows: (1) unadjusted; (2) with alcohol use, exercise status, smoking status, and body mass index (BMI) adjusted for; and (3) with alcohol use, exercise status, smoking status, marital status, occupation, BMI, total caloric intake, history of hypertension, and history of diabetes mellitus adjusted for. Depressive symptoms were significantly higher (odds ratios, 1.46, 1.40, and 1.36; 95% confidence intervals, 1.09-1.95, 1.0.4-1.88, and 1.01-1.85, respectively) in AD patients relative to those of matched controls.ConclusionAD and clinical depression interact closely, and causal relationships between the two conditions have frequently been observed. Physicians should consider mental health interventions cautiously. It is particularly important that primary care physicians provide comprehensive, continuous long-term care.

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Human lumbar spine is composed of multiple tissue components that serve to provide structural stability and proper nutrition. Conventional magnetic resonance (MR) imaging techniques have been useful for evaluation of IVD, but inadequate at imaging the discovertebral junction and ligamentous tissues due primarily to their short T2 nature. Ultrashort time to echo (UTE) MR techniques acquire sufficient MR signal from these short T2 tissues, thereby allowing direct and quantitative evaluation. This article discusses the anatomy of the lumbar spine, MR techniques available for morphologic and quantitative MR evaluation of long and short T2 tissues of the lumbar spine, considerations for T2 relaxation modeling and fitting, and existing and new techniques for spine image post-processing, focusing on segmentation. This article will be of interest to radiologic and orthopaedic researchers performing lumbar spine imaging.

We propose a new deep learning network capable of successfully segmenting intervertebral discs and their complex boundaries from magnetic resonance (MR) spine images. The existing U-network (U-net) is known to perform well in various segmentation tasks in medical images; however, its performance with respect to details of segmentation such as boundaries is limited by the structural limitations of a max-pooling layer that plays a key role in feature extraction process in the U-net. We designed a modified convolutional and pooling layer scheme and applied a cascaded learning method to overcome these structural limitations of the max-pooling layer of a conventional U-net. The proposed network achieved 3% higher Dice similarity coefficient (DSC) than conventional U-net for intervertebral disc segmentation (89.44% vs. 86.44%, respectively; p < 0.001). For intervertebral disc boundary segmentation, the proposed network achieved 10.46% higher DSC than conventional U-net (54.62% vs. 44.16%, respectively; p < 0.001).

We propose a deep learning-based spike sorting method for extracellular recordings. For analysis of extracellular single unit activity, the process of detecting and classifying action potentials called “spike sorting” has become essential. This is achieved through distinguishing the morphological differences of the spikes from each neuron, which arises from the differences of the surrounding environment and characteristics of the neurons. However, cases of high structural similarity and noise make the task difficult. And for manual spike sorting, it requires professional knowledge along with extensive time cost and suffers from human bias. We propose a deep learning-based spike sorting method on extracellular recordings from a single electrode that is efficient, robust to noise, and accurate. In circumstances where labelled data does not exist, we created pseudo-labels through principal component analysis and K-means clustering to be used for multi-layer perceptron training and built high performing spike classification model. When tested, our model outperformed conventional methods by 2.1% on simulation data of various noise levels, by 6.0% on simulation data of various clusters count, and by 1.7% on in-vivo data. As a result, we showed that the deep learning-based classification can classify spikes from extracellular recordings, even showing high classification accuracy on spikes that are difficult even for manual classification.

We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user’s role while achieving good segmentation accuracy.

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