Diagnosing Pilgrimage Common Diseases by Interactive Multimedia Courseware

في هذه الدراسة، نحاول تقديم خدمة الرعاية الصحية للحجاج. تصف هذه الدراسة كيف يمكن استخدام مناهج الوسائط المتعددة في جعل الحجاج على علم بالأمراض الشائعة الموجودة في المملكة العربية السعودية أثناء موسم الحج. كما سيتم استخدام البرامج التعليمية للوسائط المتعددة في توفير بعض المعلومات حول أعراض هذه الأمراض، وكيف يمكن علاج كل منها. يحتوي البرنامج التعليمي للوسائط المتعددة على تمثيل افتراضي للمستشفى، وبعض مقاطع الفيديو للحالات الفعلية للمرضى، وأنشطة التعلم الأصيلة التي تهدف إلى تعزيز الكفاءات الصحية أثناء الحج. تم فحص المناهج الدراسية لدراسة الطريقة التي يتم بها تطبيق عناصر المناهج الدراسية في التعلم في الوقت الحقيقي. أكثر من ذلك، في هذا البحث، يتم تقديم مناقشة حول أخطر الأمراض التي قد تحدث خلال موسم الحج. إن استخدام دورة الوسائط المتعددة قادر على توفير المعلومات بشكل فعال وفعال للحجاج حول هذه الأمراض. تؤدي هذه التقنية هذه المهمة باستخدام المعرفة المتراكمة من التجارب السابقة، لا سيما في مجال تشخيص الأمراض والطب والعلاج. تم إنشاء المناهج الدراسية باستخدام أداة تأليف تُعرف باسم مدرب ToolBook لتزويد الحجاج بخدمة عالية الجودة.In this study, we attempt to provide healthcare service to the pilgrims. This study describes how a multimedia courseware can be used in making the pilgrims aware of the common diseases that are present in Saudi Arabia during the pilgrimage. The multimedia courseware will also be used in providing some information about the symptoms of these diseases, and how each of them can be treated. The multimedia courseware contains a virtual representation of a hospital, some videos of actual cases of patients, and authentic learning activities intended to enhance health competencies during the pilgrimage. An examination of the courseware was conducted so as to study the manner in which the elements of the courseware are applied in real-time learning. More so, in this research, a discussion on the most dangerous diseases which may occur during the season of pilgrimage is provided. The use of the multimedia course is able to effectively and efficiently provide information to the pilgrims about these diseases. This technology performs this task by using the knowledge that has been accumulated from past experience, particularly in the field of disease diagnosis, medicine and treatment. The courseware has been created using an authoring tool known as ToolBook instructor to provide pilgrims with quality service

Intestinal parasitic infections among expatriate workers in various occupations in Sharjah, United Arab Emirates

Intestinal parasitic infections are prevalent throughout many countries. This study aimed to determine the prevalence of intestinal parasite carriers among 21,347 expatriate workers, including food handlers and housemaids attending the public health center laboratory in Sharjah, UAE. Stool sample collection was performed throughout the period between January and December 2013. All samples were examined microscopically. Demographic data were also obtained and analyzed. Intestinal parasites were found in 3.3% (708/21,347) of the studied samples (single and multiple infections). Among positive samples, six hundred and eighty-three samples (96.5%) were positive for a single parasite: Giardia lamblia (257; 36.3%) and Entamoeba histolytica/Entamoeba dispar (220; 31.1%), respectively, whereas mono-infections with helminths accounted for 206 (29.1%) of the samples. Infection rates with single worms were: Ascaris lumbricoides (84; 11.9%), Hookworm (34; 4.8%), Trichuris trichiura (33; 4.7%), Taenia spp. (27; 3.81%), Strongyloides stercoralis (13; 1.8%), Hymenolepis nana (13; 1.8%), and Enterobius vermicularis (2; 0.28%), respectively. Infections were significantly associated with gender (x2 = 14.18; p = 0.002) with males as the most commonly infected with both groups of intestinal parasites (protozoa and helminths). A strong statistical association was noted correlating the parasite occurrence with certain nationalities (x2= 49.5,

Intraventricular Hemorrhage in Preterm Infants, Review Article

Intraventricular hemorrhage (IVH) or germinal matrix (GM) in other words, is a condition that can occur in premature births and can lead to long-term medical and developmental effects. While GM/IVH can happen in full-term infants, the hemorrhage in this group of infants is different from periventricular hemorrhage (PVH)/IVH in premature infants. Family members and caregivers of preterm infants and those at risk of preterm birth are confronted with two significant uncertainties concerning these newborns: Is the survival of this child likely? Will the child experience long-term sequelae, particularly developmental sequelae, if they survive? The significance of these questions lies in their potential to impact future medical decisions, including the level of intensity in the care provided. Infants born prematurely can suffer from various acquired lesions in the central nervous system (CNS), leading to long-term disability. These lesions include GM/IVH, periventricular white matter injury, hemorrhage, and diffuse injury to the developing brain. GM/IVH continues to be a major contributor to both illness and death in premature newborns.  GM/IVH is primarily diagnosed by brain imaging techniques, typically cranial ultrasonography, as depicted below. Screening and serial examinations are essential for diagnosing GM/IVH, as it can occur without any noticeable clinical indications

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Analysis of H-index and Papers Citation in Computer Science Field using K-Means Clustering Algorithm

This paper provides an analysis of H-index and paper citations in the computer science field using K-Means Clustering Algorithm. By leveraging cutting edge visual analytics through the use of Power BI and Orange Data Mining tool with K-Means clustering algorithm, we are able to present a comprehensive review of how H-index and citations impact the scholarly evaluation of authors in computer science field. The analysis obtained will assist academics seeking to expand their research influence while providing additional context to the general audience seeking to understand how machine learning algorithms can contribute to data exploration. The analysis conducted has revealed that area of research which has the highest value of citation and H-index include the following topics: Artificial-Intelligence, Computational-Intelligence, Data-mining, Evolutionary-Algorithms, and Big Data Analytics. Finally, the analysis of this research clearly demonstrated that paper citations remain an important factor for determining scientific impact in disciplines such as computer science given its close relationship with established metrics such as h-index scores

Impact of Chat GPT on Scientific Research: Opportunities, Risks, Limitations, and Ethical Issues

The creation of chatbots, such as Generative Pre-trained Transformer (GPT), is a result of recent developments in natural language processing (NLP). Even though Chat GPT has demonstrated enormous promise in a number of areas, including scientific research, this impact is still developing. This paper attempts to investigate the possibilities, threats, limits, and ethical issues surrounding Chat GPT in scientific research. The assessment of the literature on Chat GPT and scientific research is followed by the presentation of case examples that demonstrate the potential advantages and difficulties of Chat GPT use in scientific research. Finally, we conclude by pointing about the ethical issues that need to be tackled before Chat GPT can be completely utilized in scientific research

Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer

Breast cancer is a considerable problem among the women and causes death around the world. This disease can be detected by distinguishing malignant and benign tumors. Hence, doctors require trustworthy diagnosing process in order to differentiate between malignant and benign tumors. Therefore, the automation of this process is required to recognize tumors. Numerous research works have tried to apply the algorithms of machine learning for classifying breast cancer and it was proven by many researchers that machine learning algorithms act preferable in the diagnosing process. In this paper, three machine-learning algorithms (Support Vector Machine, K-nearest neighbors, and Decision tree) have been used and the performance of these classifiers has been compared in order to detect which classifier works better in the classification of breast cancer. Furthermore, the dataset of Wisconsin Breast Cancer (Diagnostic) has been used in this study. The main aim of this work is to make comparison among several classifiers and find the best classifier which gives better accuracy. The outcomes of this study have revealed that quadratic support vector machine grants the largest accuracy of (98.1%) with lowest false discovery rates. The experiments of this study have been carried out and managed in Matlab which has a special toolbox for machine learning algorithms

Genetic case-based reasoning for improved mobile phone faults diagnosis

Different types of fault diagnostic applications that utilize case-based reasoning (CBR) are applied in the diagnosis process. However, CBR cannot provide solutions to unanticipated or unknown problems. Therefore, further investigation of the retrieval and revision mechanisms of CBR is essential in improving the diagnosis accuracy and precision of the method. This study proposes a hybrid scheme that combines the genetic algorithm and CBR (GCBR) to improve CBR diagnosis. CBR applies experience and knowledge on existing cases of fault diagnosis to newly provided cases. The genetic algorithm aggregates and revises relevant cases to provide solutions to unknown cases. GCBR is implemented in a mobile phone fault diagnosis application. This domain is a good testing environment because mobile phones are of various types and models. Test results show that GCBR can detect several mobile phone faults with average accuracy 98.7%