The repositioning of bone segments during orthognathic surgeries often results in mandibular condyle positional changes and can also affect jaw muscles, soft tissues and the temporomandibular joint (TMJ). Condylar displacements are considered as one of the factors of bone remodeling and further skeletal relapse. The quantitative approach is commonly used in comparative analyses and evaluations of the relationships between examined factors. The aim of this study is the overview of the current literature including quantitative analysis in the research of mandibular condyle positional changes as a consequence of orthognathic surgeries. https://www.selleckchem.com/products/Nolvadex.html Thirty articles were included in the overview. Most of the articles present a comparative and evaluative analysis of treatment results concerning different surgical approaches, fixation methods or types of skeletal defects. The correlation between condylar displacements and bone remodeling, skeletal relapse and TMJ dysfunctions were considered. The most frequently repeated study variables were short-term changes, Class III malocclusion, yaw rotation, 3D cephalometry measurements. Quantitative data might be useful in the evaluation of patterns and range of condylar displacements for specific treatment conditions. Available literature concerning the analysed topic is characterized by great heterogeneity with regards to the purpose and methodologies of the studies. More systematic approaches and long-term considerations are needed in future research.Vascular access procedures are crucial for the management of various critically ill pediatric and adult patients. Venous access is commonly performed in the form routine as well as tunneled peripherally inserted central catheters (PICC). These venous accesses are commonly used in emergency, surgical as well as ICU settings, for various infusions, total parenteral nutrition, long term intravenous antibiotics, frequent blood draws, etc. PICC insertion is guided using ultrasound and fluoroscopic guidance, which decreases the risk of complications that are otherwise seen with central venous accesses like triple lumen catheters, etc. PICC insertion and care is very simple and can be performed by specially trained PICC nurses and that helps in decreasing the overall cost of healthcare. This review article is written with educational intent for the readers to discuss indications, contraindications, procedure techniques, imaging, care of routine as well as tunneled PICC.Left ventricular (LV) twist is calculated from the net difference of counterclockwise apical and clockwise basal rotation during systole. The current study was designed to evaluate correlations between autonomic function and LV rotational mechanics in healthy subjects. The present study comprised 18 healthy subjects (mean age 36±12 years, 12 men). Three-dimensional speckle tracking echocardiography (3DSTE) could be used for non-invasive evaluation of LV rotation and twist. Autonomic function was assessed by means of 5 standard cardiovascular reflex tests. During 3DSTE, basal LV rotation proved to be -3.24±2.02 degree, while apical LV rotation was 9.08±3.04 degree, therefore LV twist was 11.70±6.80 degree. Valsalva test showed significant correlations with LV basal (r=0.529, P=0.019) and apical rotations (r=-0.534, P=0.022), and LV twist (r=-0.467, P=0.044). Heart rate response to deep breathing significantly correlated with LV twist, as well (r=-0.452, P=0.052). The other tests had no any relationship with rotational characteristics. Correlations exist between parasympathetic autonomic function and 3DSTE-derived LV rotation and twist in healthy subjects. The segmentation of cardiac medical images is a crucial step for calculating clinical indices such as wall thickness, ventricular volume, and ejection fraction. In this study, we introduce a method named LsUnet that combines multi-channel, fully convolutional neural network, and annular shape level-set methods for efficiently segmenting cardiac cine magnetic resonance (MR) images. In this method, the multi-channel deep learning algorithm is applied to train the segmentation task to extract the left ventricle (LV) endocardial and epicardial contours. Next, the segmentation contours from the multi-channel deep learning method are incorporated into a level-set formulation, which is dedicated explicitly to detecting annular shapes to assure the segmentation's accuracy and robustness. The proposed automatic approach was evaluated on 95 volumes (total 1,076 slices, ~80% as for training datasets, ~20% 2D as for testing datasets). This combined multi-channel deep learning and annular shape level-set segmentation method achieved high accuracy with average Dice values reaching 92.15% and 95.42% for LV endocardium and epicardium delineation, respectively, in comparison to the reference standard (the manual segmentation). A novel method for fully automatic segmentation of the LV endocardium and epicardium from different MRI datasets is presented. The proposed workflow is accurate and robust compared to the reference and other state-of-the-art methods. A novel method for fully automatic segmentation of the LV endocardium and epicardium from different MRI datasets is presented. The proposed workflow is accurate and robust compared to the reference and other state-of-the-art methods. Meibography is a non-contact imaging technique used by ophthalmologists and eye care practitioners to acquire information on the characteristics of meibomian glands. One of its most important applications is to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD). As the artificial qualitative analysis of meibography images can lead to low repeatability and efficiency, automated and quantitative evaluation would greatly benefit the image analysis process. Moreover, since the morphology and function of meibomian glands varies at different stages of MGD, multiparametric analysis offering more comprehensive information could help in discovering subtle changes to glands during MGD progression. Therefore, an automated and multiparametric objective analysis of meibography images is urgently needed. An algorithm was developed to perform multiparametric analysis of meibography images with fully automatic and repeatable segmentation based on image contrast enhancement and noise reduction. The full architecture can be divided into three steps (I) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (II) segmentation and identification of glands within the ROI; and (III) quantitative multiparametric analysis including a newly defined gland diameter deformation index ( ), gland tortuosity index ( ), and gland signal index ( ).