Biodegradable Mg alloys –A review

لقد توسع سوق السبائك المستخدمة للزرع الجراحي، وخاصة تلك المصممة لزراعة العظام، بسرعة خلال العقد الماضي. حيث استفاد القطاع الطبي بشكل كبير من التقدم الكبير الذي تم تحقيقه من خلال دراسة السبائك القابلة للتحلل الحيوي القائمة على المغنيسيوم. حيث يعد البحث في هذا المجال خطوة للأمام لعدد من الأسباب، منها الرغبة في تحسين نوعية حياة الناس (محرك اجتماعي واقتصادي). من خلال تقليل الاعتماد على الغرسات المعدنية الدائمة المصنوعة من (الفولاذ المقاوم للصدأ، والسبائك القائمة على الكوبالت، وسبائك التيتانيوم)، والتي لها مجموعة عيوب خاصة بها والتي يمكن أن يكون لها تأثير سلبي على الصحة النفسية والجسدية للمرضى. تتم مناقشة سبائك المغنيسيوم القابلة للتحلل في هذه الورقة، إلى جانب تاريخ تطورها، والميزات المهمة التي تجعلها مرغوبة لمثل هذه التطبيقات (زراعة العظام)، والميزات التي يجب تعديلها (معدل التآكل والخواص الميكانيكية) للوصول إلى المنتج الأمثل للتطبيق المقصود. ويركز على تقنيات/طرق واستراتيجيات التوصيف الكهروكيميائية لتعزيز سلوك التآكل والخصائص الميكانيكية لأنواع مختلفة من السبائك القابلة للتحلل، بالإضافة إلى الآلية والميزات المرتبطة بسلوك التآكل لسبائك المغنيسيوم. المعايير التي سيتم تصميمها، والمتطلبات التي يجب أن تلبيها لغرسات السبائك القابلة للتحلل الحيوي التي تعتمد على المغنيسيوم، والميزات المرتبطة بكفاءتها، بالإضافة إلى طرق التحسين وتأثير العناصر المضافة لصناعة تلك السبائك.  The market for implant alloys, particularly those designed for orthopedic implants has expanded rapidly during the last decade. The medical sector has benefited greatly from the significant advances achieved in the study of Mg-based biodegradable alloy during this time. Research is a step up in this area for a number of reasons, including the desire to improving people quality of life (a social as well as an economic driver). By decreasing the prevalence of permanent metallic implants (such as those made of stainless steel, cobalt-based alloys, and titanium alloys), which have their own set of drawbacks (including stress shielding and metal ion releases) that can have a negative impact on patients\u27 mental and physical health. Biodegradable Mg alloys are discussed in this paper, along with their history of development, important features that make them desirable for such applications (orthopedic implants), and features that must be modulated (corrosion rate and mechanical properties) to arrive at the optimal product for the intended application. It emphasizes the electrochemical characterization techniques/methods and strategies to enhance the corrosion behavior and mechanical characteristics of various kinds of biodegradable alloys, as well as the mechanism and features linked to the corrosion behavior of Mg alloys. The criteria to be design, the requirements that implants of biodegradable alloys Mg-based must fulfill, and the features connected to their efficiency provided, as well as the methods of optimization, the category, and the influence of the alloying components

SOS Application Under Android: Help Pro

Information and Communication Technologies are regularly used in medical services and associations around the world. An increasing number of devices running Android as Smartphones, smart TV and tablets, are comprehensively utilized for numerous purposes. For these devices, there are distinctive types of health and medical applications that provide an easy access to patients and their caretakers to save time [1]. In this work, we develop an Android application named Help Pro with an important goal: save lives by a single tap. In case of emergency, Help Pro users could have an ambulance available within 100 m of the user’s real-time location. Through the Dijkstra algorithm, this application employs Google Maps API (Application Programming Interface) to trace the nearest way in order to an available ambulance arrives on time. Consequently, not only time is efficiently saved but also precious lives.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Funding for open access charge: Universidad de Málaga / CBUA

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Evaluation of 80 cases of anterior cruciate ligament arthroscopic reconstruction done in Al-Wasity Teaching Hospital, Baghdad

Background: The objectives of anatomic anterior cruciate ligament (ACL) reconstruction is to reproduce the native anatomy of the ACL by restoring native insertion site, the tension pattern of the (ACL), the two functional bundles, and individualizing the surgery for each patient. This can be achieved using either the single-bundle or double-bundle technique depending on the patient condition. Objective: The goal of anatomic reconstruction in short term is to benefit clinical outcome and in long term is to reduce the prevalence of osteoarthritis. To review the results of patients who underwent arthroscopic reconstruction of the (ACL) using the semitendinosus and gracilis tendon double or triple stranded graft. Materials and Methods: Eighty cases (72 males, 8 females) who met the inclusion criteria of study that underwent arthroscopical reconstruction for (ACL) injury and followed up for 6 months in Al-Wasti Hospital between September 2012 and October 2014 were included in this study. Results: Excellent clinical outcome was reported with 92.5% of the patients, 2(2.5%) cases had delay flexion and extension, failure of surgery was reported with only one case and deep venous thrombosis, and infection were reported with two cases only. No significant difference regarding complications was reported according to the method of fixation or sex difference. Conclusion: Excellent functional outcome was obtained with all methods of fixation which confirm the reliability and safety of these techniques

Elastic band ligation of hemorrhoids using flexible gastroscope

Objective This study aims at investigating the outcome of rubber band ligation of piles in an outpatient setting. Methods After taking an informed consent, 46 patients, 89% of them were males, mean age 52.5 years, with uncomplicated grade II-III piles were involved in the study. Rubber band ligation of piles was made by Pentax iScan gastroscope. The patients were followed up regularly to record any complication. Results Most of the patients (91.3%) need one session of treatment, 3 patients need 2 sessions, no more than 2 bands were used. Additionally, a relatively low percentage of patients recorded certain complication of the operation, on follow up. Conclusion High success rate, cost effectiveness and the simplicity of rubber band ligation as an outpatient procedure promote its use as the frst line of treatment for frst, second and early third degree hemorrhoids

Immunological and Molecular Study of Interleukin17A and Uropathogenic E. coli among Patients in Holy Karbala, Iraq

The current study amid to investigate association of Interleukin-17A with uropathogenic Escherichia coli among patients with urinary tract infection in karbala province, Iraq. Bacterial infections are widespread in urinary tract infections with a global extention. Uropathogenic E.coli (UPEC) is the most common causes of these infections. Out of 110 patients were examined by urologists for urinary tract infection, 25 patients showed positive result for UPEC and other 25 showed positive result for other bacterial pathogens. UPEC were diagnosed depended on the cultural, microscopical, biochemical examinations and confirm the identification by using Vitek2 system. Polymerase chain reaction was used to detection of four genes (pap C, cnfA, fim H, and fyu A). Interleukin-17A concentration in urine was measured by using ELISA kit. out of 110 urine samples, 56 (44.90%) with significant bacteriuria, 44(40%) with non-significant bacteriuria and 10 (9.09 %) with negative culture. The presence of UPEC among significant bacteriuria was 25/56 (44.64 %). The distribution of pap C, cnfA, fim H, and fyu A genes among UPEC were 17(68%), 17(68%), 16(64%) and 15(60%) respectively. Through UTI patients, 50 gave positive (121.70) pg/ml results compared to 30 of control (13. 94) pg/ml. Among uropathogenic Escherichia coli patients, 25 gave positive (92.80) pg/ml results, while 25 of other bacterial pathogens gave positive (15.40) pg/ml results

A Conceptual and Systematics for Intelligent Power Management System-Based Cloud Computing: Prospects, and Challenges

This review describes a cloud-based intelligent power management system that uses analytics as a control signal and processes balance achievement pointer, and describes operator acknowledgments that must be shared quickly, accurately, and safely. The current study aims to introduce a conceptual and systematic structure with three main components: demand power (direct current (DC)-device), power mix between renewable energy (RE) and other power sources, and a cloud-based power optimization intelligent system. These methods and techniques monitor demand power (DC-device), load, and power mix between RE and other power sources. Cloud-based power optimization intelligent systems lead to an optimal power distribution solution that reduces power consumption or costs. Data has been collected from reliable sources such as Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar, and PubMed. The overall findings of these studies are visually explained in the proposed conceptual framework through the literature that are considered to be cloud computing based on storing and running the intelligent systems of power management and mixing

A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%)

A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%)