Automatic identification of consistently defined body regions in medical images is vital in many applications. In this paper, we describe a method to automatically demarcate the superior and inferior boundaries for neck, thorax, abdomen, and pelvis body regions in computed tomography (CT) images.
 
  For any three-dimensional (3D) CT image I, following precise anatomic definitions, we denote the superior and inferior axial boundary slices of the neck, thorax, abdomen, and pelvis body regions by NS(I), NI(I), TS(I), TI(I), AS(I), AI(I), PS(I), and PI(I), respectively. Of these, by definition, AI(I)=PS(I), and so the problem reduces to demarcating seven body region boundaries. Our method consists of a two-step approach. In the first step, a convolutional neural network (CNN) is trained to classify each axial slice in I into one of nine categories the seven body region boundaries, plus legs (defined as all axial slices inferior to PI(I)), and the none-of-the-above category. This CNN uses a multichannel approach oves the dependency on experts for accurately demarcating body regions in a study.To provide a comprehensive and systematic analysis of demographic characteristics, clinical symptoms, laboratory findings, and imaging features of coronavirus disease 2019 (COVID-19) in pediatric patients. A meta-analysis was carried out to identify studies on COVID-19 from 25 December 2019 to 30 April 2020. A total of 48 studies with 5829 pediatric patients were included. Children of all ages were at risk for COVID-19.  https://www.selleckchem.com/products/OSI-906.html  The main illness classification ranged as 20% (95% confidence interval [CI] 14%-26%; I2  = 91.4%) asymptomatic, 33% (95% CI 23%-43%; I2  = 95.6%) mild and 51% (95% CI 42%-61%; I2  = 93.4%) moderate. The typical clinical manifestations were fever 51% (95% CI 45%-57%; I2  = 78.9%) and cough 41% (95% CI 35%-47%, I2  = 81.0%). The common laboratory findings were normal white blood cell 69% (95% CI 64%-75%; I2  = 58.5%), lymphopenia 16% (95% CI 11%-21%; I2  = 76.9%) and elevated creatine-kinase MB 37% (95% CI 25%-48%; I2  = 59.0%). The frequent imaging features were normal images 41% (95% CI 30%-52%; I2  = 93.4%) and ground-glass opacity 36% (95% CI 25%-47%; I2  = 92.9%). Among children under 1 year old, critical cases account for 14% (95% CI 13%-34%; I2  = 37.3%) that should be of concern. In addition, vomiting occurred in 33% (95% CI 18%-67%; I2  = 0.0%) cases that may also need attention. Pediatric patients with COVID-19 may experience milder illness with atypical clinical manifestations and rare lymphopenia. High incidence of critical illness and vomiting symptoms reward attention in children under 1 year old.
  Dental plaque biofilm is considered to be the underlying cause of peri-implant diseases. Moreover, it has been corroborated recently the association between the presence of these diseases and deficiently designed implant-supported prostheses. In this regard, professional-administered oral hygiene measures have been suggested to play a dominant role in prevention.
 
  A cross-sectional study was conducted in dental implant patients according to accessibility for self-performed oral hygiene using a 0.5mm interproximal brush. Periodontal and peri-implant status were assessed based on clinical and radiographic variables to determine the prevalence of peri-implant diseases. In addition, the participants completed a questionnaire on the efficiency and accessibility for self-performed proximal hygiene. Associations of descriptive data were analyzed using the chi-squared test and Mann-Whitney U-test. Correlations of the variables with the primary outcome (accessibility) were assessed by means of generalized estimatiohe full-mouth bleeding score (P = 0.018). On the other hand, the presence of peri-implant disease was related to self-reported assessment of oral hygiene measures (P = 0.015) and to patient perception of gingival/mucosal bleeding when performing oral hygiene (P = 0.026). In turn, the diagnosis of peri-implant disease was significantly associated to the quantity and quality of information provided at the time of implant therapy (P = 0.004), including the influence of confounders upon disease occurrence (P = 0.038) CONCLUSIONS To a certain extent, accessibility for self-performed proximal hygiene is associated to the peri-implant condition. On the other hand, the information received by the patient from the dental professional is essential for self-monitoring of the peri-implant conditions and for alerting to the possible presence of disorders.Understanding neural physiopathology requires advances in nanotechnology-based interfaces, engineered to monitor the functional state of mammalian nervous cells. Such interfaces typically contain nanometer-size features for stimulation and recording as in cell-non-invasive extracellular microelectrode arrays. In such devices, it turns crucial to understand specific interactions of neural cells with physicochemical features of electrodes, which could be designed to optimize performance. Herein, versatile flexible nanostructured electrodes covered by arrays of metallic nanowires are fabricated and used to investigate the role of chemical composition and nanotopography on rat brain cells in vitro. By using Au and Ni as exemplary materials, nanostructure and chemical composition are demonstrated to play major roles in the interaction of neural cells with electrodes. Nanostructured devices are interfaced to rat embryonic cortical cells and postnatal hippocampal neurons forming synaptic circuits. It is shown that Au-based electrodes behave similarly to controls. Contrarily, Ni-based nanostructured electrodes increase cell survival, boost neuronal differentiation, and reduce glial cells with respect to flat counterparts. Nonetheless, Au-based electrodes perform superiorly compared to Ni-based ones. Under electrical stimulation, Au-based nanostructured substrates evoke intracellular calcium dynamics compatible with neural networks activation. These studies highlight the opportunity for these electrodes to excite a silent neural network by direct neuronal membranes depolarization.