Effect of Nebulized Morphine on Dyspnea of Mustard Gas-Exposed Patients: A Double-Blind Randomized Clinical Trial Study

Background. Dyspnea is one of the main complaints in a group of COPD patients due to exposure to sulfur mustard (SM) and is refractory to conventional therapies. We designed this study to evaluate effectiveness of nebulized morphine in such patients. Materials and Methods. In a double-blind clinical trial study, 40 patients with documented history of exposure to SM were allocated to two groups: group 1 who received 1 mg morphine sulfate diluted by 4 cc normal saline 0.5% using nebulizer once daily for 5 days and group 2 serving as control who received normal saline as placebo. They were visited by pulmonologist 7 times per day to check symptoms and signs and adverse events. Different parameters including patient-scored peak expiratory flow using pick flow meter, visual analogue scale (VAS) for dyspnea, global quality of life and cough, and number of respiratory rate, night time awaking for dyspnea and cough have been assessed. Results. The scores of VAS for dyspnea, cough and quality of life and also respiratory rate, heart rate, and night time awaking due to dyspnea and night time awaking due to cough improved significantly after morphine nebulization without any major adverse events. Also pick expiratory flow has been improved significantly after nebulization in each day. Conclusion. Our results showed the clinical benefit of nebulized morphine on respiratory complaints of patients due to exposure to SM without significant side effects

Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN

We applied physics-informed neural networks to solve the constitutive relations for nonlinear, path-dependent material behavior. As a result, the trained network not only satisfies all thermodynamic constraints but also instantly provides information about the current material state (i.e., free energy, stress, and the evolution of internal variables) under any given loading scenario without requiring initial data. One advantage of this work is that it bypasses the repetitive Newton iterations needed to solve nonlinear equations in complex material models. Additionally, strategies are provided to reduce the required order of derivation for obtaining the tangent operator. The trained model can be directly used in any finite element package (or other numerical methods) as a user-defined material model. However, challenges remain in the proper definition of collocation points and in integrating several non-equality constraints that become active or non-active simultaneously. We tested this methodology on rate-independent processes such as the classical von Mises plasticity model with a nonlinear hardening law, as well as local damage models for interface cracking behavior with a nonlinear softening law. Finally, we discuss the potential and remaining challenges for future developments of this new approach

Rare Presentation of Rhino-Orbital-Cerebral Zygomycosis: Bilateral Facial Nerve Palsy

Rhino-orbital-cerebral zygomycosis afflicts primarily diabetics and immunocompromised individual, but can also occur in normal hosts rarely. We here presented an interesting case of facial nerve palsy and multiple cold abscesses of neck due to rhino-orbital-cerebral zygomycosis in an otherwise healthy man. Although some reports of facial nerve paralysis in conjunction with rhino-orbital-cerebral zygomycosis exist, no case of bilateral complete facial paralysis has been reported in the literature to date

An Evolutionary Approach towards Ph.D. Educational System in Medical Sciences in Iran: a Systematic Review of Educational Models in the World’s Leading Universities

Background & Objective : In this study, we aimed to evaluate the status of World’s leading universities in Ph.D. students’ education and to compare it with Iranian universities applying the approach of educational status evaluation in a research based way. Methods: Using a systematic review, all documents present in the webs and related links of universities of first 10 countries in Shanghai’s Academic Ranking of World Universities were systematically reviewed information related to Ph.D. education was systematically collected and analyzed. Results : Reviewing 28 leading universities revealed that the educational model in educating Ph.D. students in 22 of them was research based and the rest are using course based model. Passing taught courses, as an index in course based model, is considered to be among minimum course requirements in most of the universities which are using research based model. In cases where passing such courses is voluntarily, a consultant professor plays a significant role in guiding the students in selecting and attending theoretical courses. Conclusion : Based on the above explanation and similar to other successful countries, it seems that it is time to create various models for training postgraduate students to meet industrial needs. With revisions in educational curriculums, purposeful attempts should be made to solve possible problems and train people who meet country’s developmental needs in regards with the twenty year prospect. Keywords: Postgraduate education, Shanghai’s Academic Ranking, Research based curriculum, Scientific development, Ph.D.

Incidence of Dentinal Crack after Root Canal Preparation by ProTaper Universal, Neolix and SafeSider Systems

Introduction: This study aimed to compare the incidence of dentinal crack formation by instrumentation with ProTaper Universal system (rotary, multi-file system), SafeSider (reciprocation movement, multi-file system) and Neolix (rotary, single-file system). Methods and Materials: In this in vitro study, 60 freshly extracted mandibular first molars were randomly divided into three experimental groups (n=15) and a control group containing unprepared teeth (n=15). Instrumentation in different groups was accomplished using either ProTaper, Neolix or SafeSider systems up to 25/0.08. The teeth were then sectioned at 3, 6 and 9 mm from the apex, and observed under a stereomicroscope for presence of dentinal cracks. Data were analyzed with Chi square test, Fisher’s exact test and Bonferroni correction. Results: Micro cracks were seen in all experimental groups (13.3% in ProTaper, 26.7% in SafeSider and 40% in Neolix). There was a significant difference between Neolix and the control groups in microcrack formation (P=0.042). Micro cracks mainly occurred in the coronal section (9 mm). No microcrack occurred in the control group. Conclusion: Neolix rotary single-file system caused more dentinal cracks compared to the unprepared roots. All the instrumentation systems increased the number of micro cracks compared to unprepared teeth.Keywords: Dentinal Cracks; Micro Crack; Root Canal Preparation; Root Crack; Root Dentine; Single-file Syste

Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains

Physics-informed neural networks (PINNs) are a new tool for solving boundary value problems by defining loss functions of neural networks based on governing equations, boundary conditions, and initial conditions. Recent investigations have shown that when designing loss functions for many engineering problems, using first-order derivatives and combining equations from both strong and weak forms can lead to much better accuracy, especially when there are heterogeneity and variable jumps in the domain. This new approach is called the mixed formulation for PINNs, which takes ideas from the mixed finite element method. In this method, the PDE is reformulated as a system of equations where the primary unknowns are the fluxes or gradients of the solution, and the secondary unknowns are the solution itself. In this work, we propose applying the mixed formulation to solve multi-physical problems, specifically a stationary thermo-mechanically coupled system of equations. Additionally, we discuss both sequential and fully coupled unsupervised training and compare their accuracy and computational cost. To improve the accuracy of the network, we incorporate hard boundary constraints to ensure valid predictions. We then investigate how different optimizers and architectures affect accuracy and efficiency. Finally, we introduce a simple approach for parametric learning that is similar to transfer learning. This approach combines data and physics to address the limitations of PINNs regarding computational cost and improves the network's ability to predict the response of the system for unseen cases. The outcomes of this work will be useful for many other engineering applications where deep learning is employed on multiple coupled systems of equations for fast and reliable computations

Myasthenia Gravis Development and Crisis Subsequent to Multiple Sclerosis

During the last decade, sporadic combination of multiple sclerosis (MS) and myasthenia gravis (MG) has been reported repeatedly. Although these are anecdotal, they are important enough to raise concerns about co-occurrence of MG and MS. Here, we present a case of an MS patient who developed an MG crisis. She had received interferon for relapsing remitting MS. Interestingly, she developed an MG crisis 4 years after the diagnosis of MS. MS and MG have relatively the same distribution for age, corresponding to the younger peak of the bimodal age distribution in MG. They also share some HLA typing characteristics. Furthermore, some evidences support the role of systemic immune dysregulation due to a genetic susceptibility that is common to these two diseases. The association may be underdiagnosed because of the possible overlap of symptoms especially bulbar manifestations in which either MG or MS can mimic each other, leading to underestimating incidence of the combination. The evidence warrants physicians, especially neurologists, to always consider the possibility of the other disease when encountering any patients either with MS or MG. Anecdotal and sporadic reports of combination of multiple sclerosis (MS) and myasthenia gravis (MG) have been raised concerns about co-occurrence of them

Difficulties of Diagnosing Alzheimer's Disease: The Application of Clinical Decision Support Systems

Introduction: Alzheimer's disease is one of the most common causes of dementia, which gradually causes cognitive impairment. Diagnosis of Alzheimer's disease is a complicated process performed through several tests and examinations. Design and development of Clinical Decision Support System (CDSS) could be an appropriate approach for eliminating the existing difficulties of diagnosing Alzheimer's disease. Materials and Methods: This study reviews the current problems in the diagnosis of Alzheimer's disease with an approach to the application of CDSS. The study reviewed the articles published from 1990 to 2016. The articles were identified by searching electronic databases such as PubMed, Google Scholar, Science Direct. Considering the relevance of articles with the objectives of the study, 29 papers were selected. According to the performed investigations, various reasons cause difficulty in Alzheimer's diagnosis. Results: The complexity of diagnostic process and  the similarity of Alzheimer's disease with other causes of dementia are the most important of them. The results of studies about the application of CDSSs on Alzheimer's disease diagnosis indicated that the implementation of these systems could help to eliminate the existing difficulties in the diagnosis of Alzheimer's disease. Conclusion: Developing CDSSs based on diagnostic guidelines could be regarded as one of the possible approaches towards early and accurate diagnosis of Alzheimer's disease. Applying of computer-interpretable guideline (CIG) models such as GLIF, PROforma, Asbru, and EON can help to design CDSS with the capability of minimizing the burden of diagnostic problems with Alzheimer's disease

A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method

Physics-informed neural networks (PINNs) are capable of finding the solution for a given boundary value problem. We employ several ideas from the finite element method (FEM) to enhance the performance of existing PINNs in engineering problems. The main contribution of the current work is to promote using the spatial gradient of the primary variable as an output from separated neural networks. Later on, the strong form which has a higher order of derivatives is applied to the spatial gradients of the primary variable as the physical constraint. In addition, the so-called energy form of the problem is applied to the primary variable as an additional constraint for training. The proposed approach only required up to first-order derivatives to construct the physical loss functions. We discuss why this point is beneficial through various comparisons between different models. The mixed formulation-based PINNs and FE methods share some similarities. While the former minimizes the PDE and its energy form at given collocation points utilizing a complex nonlinear interpolation through a neural network, the latter does the same at element nodes with the help of shape functions. We focus on heterogeneous solids to show the capability of deep learning for predicting the solution in a complex environment under different boundary conditions. The performance of the proposed PINN model is checked against the solution from FEM on two prototype problems: elasticity and the Poisson equation (steady-state diffusion problem). We concluded that by properly designing the network architecture in PINN, the deep learning model has the potential to solve the unknowns in a heterogeneous domain without any available initial data from other sources. Finally, discussions are provided on the combination of PINN and FEM for a fast and accurate design of composite materials in future developments

Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions

We present a comparative evaluation of various techniques for action recognition while keeping as many variables as possible controlled. We employ two categories of Riemannian manifolds: symmetric positive definite matrices and linear subspaces. For both categories we use their corresponding nearest neighbour classifiers, kernels, and recent kernelised sparse representations. We compare against traditional action recognition techniques based on Gaussian mixture models and Fisher vectors (FVs). We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation). Despite recent advancements for handling manifolds, manifold based techniques obtain the lowest performance and their kernel representations are more unstable in the presence of challenging conditions. The FV approach obtains the highest accuracy under ideal conditions. Moreover, FV best deals with moderate scale and translation changes