The Role of IgLON Cell Adhesion Molecules in Neurodegenerative Diseases

In the brain, cell adhesion molecules (CAMs) are critical for neurite outgrowth, axonal fasciculation, neuronal survival and migration, and synapse formation and maintenance. Among CAMs, the IgLON family comprises five members: Opioid Binding Protein/Cell Adhesion Molecule Like (OPCML or OBCAM), Limbic System Associated Membrane Protein (LSAMP), neurotrimin (NTM), Neuronal Growth Regulator 1 (NEGR1), and IgLON5. IgLONs exhibit three N-terminal C2 immunoglobulin domains; several glycosylation sites; and a glycosylphosphatidylinositol anchoring to the membrane. Interactions as homo- or heterodimers in cis and in trans, as well as binding to other molecules, appear critical for their functions. Shedding by metalloproteases generates soluble factors interacting with cellular receptors and activating signal transduction. The aim of this review was to analyse the available data implicating a role for IgLONs in neuropsychiatric disorders. Starting from the identification of a pathological role for antibodies against IgLON5 in an autoimmune neurodegenerative disease with a poorly understood mechanism of action, accumulating evidence links IgLONs to neuropsychiatric disorders, albeit with still undefined mechanisms which will require future thorough investigations

Prediction Fatigue Life of Aluminum Alloy 7075 T73 Using Neural Networks and Neuro-Fuzzy Models

In present paper the fatigue life of aluminum alloy 7075 T73 under constant amplitude loading is predicted using ANN and ANFIS models. Many neural networks models are used for this purpose and also different neuro-fuzzy models are built for predict fatigue life.Theclassical power law formula ismost common used to find fatigue behaviors of materials. In present study, two techniques are used to find coefficients of the formula linear and nonlinear regression. Forcomparison the fatigue life curves of soft computing methods are plotted together with two conventionalmethods. The neural network and neuro-fuzzy models give good results compared with two conventional methods. Also it is shown thatneural network model which is trained using Levenberg-Marquardt algorithm is best neural network modelscompared with other NNS models.Also, it is foundANFIS models with input trapezoidal membership function is best performance from other membership function types to predict fatigue life. It can be stated that neuro-fuzzy models are better models than neural network and conventional methods to predict fatigue life of the maintained alloy

Enhanced Recovery After Surgery (ERAS) and the Role of Advanced Hemodynamic Monitoring

This study aimed at exploring the Enhanced Recovery After Surgery (ERAS) as a multi-modality, evidence-based approach to improving the quality of patient care after major surgery and to investigate the effectiveness of the implementation of the ERAS on the outcome measures. Therefore, the problem of this study lies in exploring the Enhanced Recovery After Surgery (ERAS) upon the role of advanced hemodynamic monitoring through examining a sample of (220) patients in two Jordanian hospitals (Jordan Hospital and the Specialty Hospital) undergoing major surgery. The study concluded that the patients had witnessed progressive outcome measures in the Improved Post-operative Morbidity Score (POMS), and the Reduced Length of Stay in Hospital, and the Reduced episodes of harm and surgical complications