Editorial: Social and Administrative Policy in Healthcare and Pharmacy Practice

Most public health policies and activities in developing and developed countries are government-funded, so new information should be open to the public (1). There is a need to focus on both strengths and weaknesses of medication use policy, medication marketing, and evaluation of theoretical models. Furthermore, these could impact practice and/or patient behavior in responses to the social, health, and environmental challenges providing both theoretical and empirical findings. Potential issues include but are not limited to medication products/programs/services, medication adherence, disease management, medication use policy, and medication marketing (2–4). Social and health issues related to delivering health care services, medical governance, medication management, and pharmaceutical management related to multilevel, multi-stakeholder, and multi-sectoral approaches to healthy and affected communities should be explored. It is noteworthy to criticize ethical issues related to medication products/programs/services, medication use policy, and medication marketing. The ideas relevant to the social policy and health policy-related concepts received contributions from health policymakers, academics, practitioners, and collaborators in other sectors whose work impacts social and administrative policy. They were the appropriate sources to discuss how policy and practice change over time, how it compares across the globe, and how it is realized at all levels, from international to local. Whilst focused on relevance to practice, it was understood that examining the theories and philosophies that underpin social and administrative policy was essential. It captured a diversity of opinions across a broad range of fields, from the traditional (medication adherence; disease management; medication use policy; medication marketing, etc.) to the new (big data, new technologies). This Research Topic provided a venue for health professionals in social and administrative policy disciplines with a specific interest in policy and practice to share their research findings and other Research Topics related to public health

Polyvinylbenzyl Tris-Aminodicarboxylate Microspheres for the Optical Sensing of Cu2+ Ions

In this work, a tris(2-aminoethyl)aminodicaboxylate functionality was substituted for the chloride of polyvinyl- benzyl chloride (PVBC) which was lightly cross-linked (2%) with divinyl benzene. The resulting derivatized po- lymer microspheres were embedded in a hydrogel matrix of poly vinyl alcohol cross-linked with glutaraldehyde to produce a sensing membrane. The latter responded selectively to Cu2+ solutions of different concentration ranges (1 × 10−4 M to 1 × 10−6 M). The response is based on the interaction between the metal cations and the negatively charged deprotonated dicarboxylate functional group, which led to neutralization of the charges. As a result, an increase in the turbidity of the sensing membrane occurred which is attributed to a change in the re- fractive index of the derivatized polymer microspheres relative to that of the hydrogel. The change in the turbid- ity of the sensing membrane was measured as absorbance using a conventional spectrophotometer. It was found that Cu2+ ions bind to the aminodicarboxylated-polymer with a formation constant, Kf, of 1 × 105 M−1. SEM, Eds and IR analyses were performed on the aminodicarboxylated microspheres and their Cu2+ complex.We are grateful to Professor W. R. Seitz and his research group, at the University of New Hampshire (USA) for their fruitful comments and for supplying us with the polyvinylbenzyl chloride microspheres. We are also grateful to the staff at the department of chemistry of An-Najah National University (PA), for their technical assistance

Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction

سرطان الثدي يعتبر واحد من الامراض القاتلة الشائعة بين النساء في جميع أنحاء العالم. والتشخيص المبكر لسرطان الثدي الكشف المبكر من أهم استراتيجيات الوقاية الثانوية. نظرًا لاستخدام التصوير الطبي على نطاق واسع في تشخيص العديد من الأمراض المزمنة ومراقبتها، فقد تم اقتراح العديد من خوارزميات معالجة الصور على مر السنين لزيادة مجال التصوير الطبي بحيث تصبح عملية التشخيص أكثر دقة وكفاءة. تقدم هذه الدراسة خوارزمية جديدة لاستخراج الخواص العميقة من نوعين من صور الرنين المغناطيسي T2W-TSE و STIR MRI كمدخلات للشبكات العصبية العميقة المقترحة والتي تُستخدم لاستخراج الخواص للتمييز بين فحوصات التصوير بالرنين المغناطيسي للثدي المرضية والصحية. في هذه الخوارزمية، تتم معالجة فحوصات التصوير بالرنين المغناطيسي للثدي مسبقًا قبل خطوة استخراج الخواص لتقليل تأثيرات الاختلافات بين شرائح التصوير بالرنين المغناطيسي، وفصل الثدي الايمن عن الايسر، بالإضافة الى عزل خلفية الصور. وقد كانت أقصى دقة تم تحقيقها لتصنيف مجموعة بيانات تضم 326 شريحة تصوير بالرنين المغناطيسي للثدي 98.77٪. يبدو أن النموذج يتسم بالكفاءة والأداء ويمكن بالتالي اعتباره مرشحًا للتطبيق في بيئة سريرية.Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature extraction step to enhance and preserve the fine details of the breast MRI scans boundaries by using fractional integral entropy FIE algorithm, to reduce the effects of the intensity variations between MRI slices, and finally to separate the right and left breast regions by exploiting the symmetry information. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, all extracted features significantly improves the performance of the LSTM network to precisely discriminate between pathological and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 326 T2W-TSE images and 326 STIR images is 98.77%. The experimental results demonstrate that FIE enhancement method improve the performance of CNN in classifying breast MRI scans. The proposed model appears to be efficient and might represent a useful diagnostic tool in the evaluation of MRI breast scans

Mode I delamination simulation using LS-DYNA

There are several failure models that can be used to predict delamination and these include stress based failure models, fracture mechanics and damage mechanics. The damage mechanics combines the advantages of stress based and fracture mechanics into a single failure model. In LS-DYNA the damage mechanics based failure models are available through material models MAT_138 investigate mode I delamination. Results are presented on the accuracy of both failure models

Development modeling methods of analysis and synthesis of fingerprint deformations images

The current study is to develop modeling methods, Analysis and synthesis of fingerprints deformations images and their application in problems of automatic fingerprint identification. In the introduction justified urgency of the problem, is given a brief description of thematic publications. In this study will review of modern technologies of biometric technologies and methods of biometric identification, the review of fingerprint identification systems, investigate for distorting factors. The influence of deformations is singled out, the causes of deformation of fingerprints are analyzed. The review of modern ways of the account and modeling of deformations in problems of automatic fingerprint identification is given. The scientific novelty of the work is the development of information technologies for the analysis and synthesis of deformations of fingerprint images. The practical value of the work in the application of the developed methods, algorithms and information technologies in fingerprints identification systems. In addition, it has been found that our paper "devoted to research methods and synthesis of the fingerprint deformations" is a more appropriate choice than other papers

A bayesian via laplace approximation on log-gamma model with censored data

Log-gamma distribution is the extension of gamma distribution which is more flexible, versatile and provides a great fit to some skewed and censored data. Problem/Objective: In this paper we introduce a solution to closed forms of its survival function of the model which shows the suitability and flexibility towards modelling real life data. Methods/Analysis: Alternatively, Bayesian estimation by MCMC simulation using the Random-walk Metropolis algorithm was applied, using AIC and BIC comparison makes it the smallest and great choice for fitting the survival models and simulations by Markov Chain Monte Carlo Methods. Findings/Conclusion: It shows that this procedure and methods are better option in modelling Bayesian regression and survival/reliability analysis integrations in applied statistics, which based on the comparison criterion log-gamma model have the least values. However, the results of the censored data have been clarified with the simulation results