Spontaneous Weight Change during Chronic Hepatitis C Treatment: Association with Virologic Response Rates

Objective: We examined weight changes during chronic hepatitis C (CHC) therapy and association with virologic response. Methods: Weight changes were compared between subjects achieving rapid, early, and sustained virologic response rates (RVR, EVR, and SVR). RVR, EVR and SVR were compared among patients with or without weight loss of ≥ 0.5 body mass index (BMI) units (kg/m2) at 4, 12, 48 weeks. Results: CHC therapy was initiated in 184 cases. Median pretreatment BMI was 27.7 (18.4-51.3) with 38% overweight and 31% obese (BMI ≥25 and ≥ 30, respectively). Among patients with liver biopsies (n = 90), steatosis was present in 31.6%; fibrosis grade of 1-2/6 in 46%, 3-4 in 37.3% and 5-6 in 14.7%. Mean weight loss at 4, 12, 24 and 48 weeks of therapy were 1.2, 2.6, 3.8 and 3.3 kg, respectively. After 4 and 12 weeks of treatment, 38% and 54.3% had a BMI decrement of ≥ 0.5 kg/m2. For genotype 1, weight loss at 4 weeks was associated with significantly higher EVR (90.0% vs. 70%, p = 0.01) and a tendency towards better RVR and SVR (42.9% vs. 26.0% and 55.2% vs. 34.8%, respectively, p = 0.08). In multivariate analysis, weight loss at 4 weeks was independently associated with EVR (OR 6.3, p = 0.02) but was not significantly associated with RVR or SVR Conclusions: Spontaneous weight loss at 4 and 12 weeks of CHC therapy was associated with improved EVR. Weight loss at 4 weeks was an independent predictor of EVR but not SVR

New markers for the detection of polycystic ovary syndrome

Polycystic Ovary Syndrome (PCOS) is a highly prevalent, complex genetic disorder of the endocrine system in women. Alterations that occur in women with PCOS can be due to several predisposing factors; among these contributors are genetic and epigenetic variations. Environmental factors play a weaker role, mainly in worsening insulin resistance. Enzyme, protein and genetic markers can depend as a biochemical diagnosis of PCOs. The genetic markers have been identified to be related to PCOS wasn’t useful for early diagnosis, which can only be used to confirm PCOS in patients already exhibiting the definitive symptoms. Protein and enzyme markers are commonly used for prognosis and monitoring the patient to prevent the development of the complications of PCOS. Proteins of the adipose tissue have been found to be greatly related to insulin resistance and the development of PCOS. The nature of enzymes and proteins of instability and easily degradable have prevented sufficient research from being carried out on them. Therefore, the diagnosis of PCOS relies on the analysis of multiple factors

Neutrophil Gelatinase Associated Lipocalin: Is not an Early Marker Inductor for Diabetic Nephropathy in Qatari Population

Background: The WHO Global Report on Diabetes (2016) showed that the number of diabetic patients quadrupled between 1980 and 2016, while causing the death of 1.5 million people. While the global prevalence of diabetes is 9%, the prevalence of diabetes in Qatar is between 17-20%, 45% of which developed diabetic nephropathy. Diabetic Nephropathy is the largest cause of End Stage Renal Disease, and it develops in 20% of diabetic patients. Currently, DN is diagnosed by the detection of microalbumin in urine samples. However, nephropathy can be present even in the absence of albuminuria, and the levels of microalbumin in urine does not correlate with the degree of nephropathic damage. Early detection can prevent total renal failure. Studies have shown that neutrophil gelatinase-associated lipocalin (NGAL) was highly expressed even before the appearance of pathological microalbuminuria in both type 1 and type 2 diabetic patients. The levels of NGAL in urine also correlates with the degree of nephropathic damage. However, currently no information exists about the presence of NGAL in diabetic patients of the Qatari population. Objective: This study aims to determine if there is a relationship between the concentrations of NGAL in urine and kidney function. Methodology: Urine samples of 123 patients were acquired from the Qatar Biobank. Of these, 38 were non-diabetic controls, while 85 were diabetic patients. Type 1 diabetics, pregnant females, smokers, and kidney, liver and cardiovascular disease patients were excluded from the control and case population. Using Enzyme linked immunosorbent assay (ELISA), all samples were tested for the presence of NGAL, and a select few were also tested for microalbumin through an external laboratory. The results obtained were analyzed using Statistical Package for Social Sciences (SPSS) version 24 and Microsoft Excel 2016. Results: No significant difference was found in mean values of uNGAL concentrations in healthy patients, diabetic patients with HbA1c>6% and diabetic patients with HbA1c0.05). However, weak correlation was demonstrated between uNGAL concentrations with serum albumin, HbA1c, serum glucose concentration and albuminuria in diabetic patients with HbA1c>6% (p<0.05). Conclusion: According to the current study uNGAL concentrations does not correlate with any of the kidney function tests, such as glomerular filtration rate, serum creatinine and blood urea nitrogen. So, it cannot be used as a marker to detect diabetic nephropathy in the early stages

Cloud Servers: Resource Optimization Using Different Energy Saving Techniques

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique

Association of Urinary Vitamin D-binding Protein and Megalin as Biomarkers for Diabetic Nephropathy in Type 2 Diabetes Mellitus in Qatari Patients

Abstract Background: Nephropathy is a common complication of type 2 diabetes mellitus (T2DM). Previous studies revealed that T2DM patients with nephropathy have higher concentrations of urinary Vitamin D Binding Protein (VDBP) that carries vitamin D to the target tissues, and megalin that mediates endocytosis in the proximal tubule than those who are healthy. Methodology: 196 urine samples with their blood data were obtained from Qatar Biobank, of which 21 samples were measured for VDBP and megalin using enzyme-linked immunosorbent assay (ELISA). They were divided intothree groups; group 1 (control group with eGFR ≥ 90 mL/min/1.73 m²), group 2 (T2DM patients with eGFR ≥ 90 mL/min/1.73 m²) and group 3; (T2DM patients with eGFR < 90 mL/min/1.73 m²). Results: Urinary VDBP and Megalin levels were non-significantly elevated in T2DM patients with DN (P=0.198) and (P=0.293) respectively. Moreover, a weak negative correlation was observed between the urinary VDBP and Megalin levels with eGFR (r=-0.326, P=0.149) and (r=-0.315, P=0.165) respectively. Conclusion: Previous studies revealed that uVDBP and Megalin are potential biomarkers for DN in T2DM patients. However, the current study reveals that urinary VDBP and megalin levels were non-significantly elevated in T2DM patients with DN. Furthermore, eGFR showed a weak negative correlation with urinary VDBP and megalin levels. However, it is suggested that these results could be due to some limitations. Further tests should be performed on larger sample size to confirm the association of Megalin and VDBP in T2DM nephropathy

Using Artificial Intelligence for the Specification of m-Health and e-Health Systems

Artificial intelligence (AI) techniques such as machine learning (ML) have wide application in medical informatics systems. In this chapter, we employ AI techniques to assist in deriving software specifications of e-Health and m-Health systems from informal requirements statements. We use natural language processing (NLP), optical character recognition (OCR), and machine learning to identify required data and behaviour elements of systems from textual and graphical requirements documents. Heuristic rules are used to extract formal specification models of the systems from these documents. The extracted specifications can then be used as the starting point for automated software production using model-driven engineering (MDE). We illustrate the process using an example of a stroke recovery assistant app and evaluate the techniques on several representative systems

Pattern of solid tumors of infancy and childhood among sample of patients attending tertiary teaching hospitals in Baghdad

Background: Solid tumors are most common cause of death in the first fifteen years. In developed countries cancer is the leading cause of death from disease in children more than six month of age. The aim of this study was to assess: the relative frequency of the childhood tumor, the distribution of solid tumors of childhood in relation to age, sex of the patient, and histological types of the tumors over period (1992 -2015).Methods: Two thousand four hundreds and three cases of solid tumors of infancy and childhood has been studiedfor period from (1992-2015), 170 was a prospective cases where 2233 cases a retrospective. The study was carried out through histopathological examination of biopsies of patients attending major medical centres in Baghdad, Iraq.Results: Malignant neoplasms in descending order of frequency were, lymphoma (29.5%), central nervous system tumors (24.5%), soft tissue tumors (9.4%), Neuroblastoma (9.1%), wilms’ tumors, (7.4%), Bone tumors, (7.3%), Retinoblastoma (5.1%), Germ cell tumors, (3.5%), Liver tumors (0.2%), others (4.6). Males were more frequently affected with central nervous tumors (59.6%), Malignant lymphoma (69.5%), neuroblastoma (62%), Soft tissue tumors (60.3%), nephroblastoma (51.5%), retinoblastma (58.8%), liver tumor 81 and other miscellaneous tumors (59.6%) while females were more frequently effected with germ cell tumors 70.5% and bone tumors (53.9%). Central nervous system tumors reach a peak between (5-9) years whereas neuroblastoma, nephroblastoma, retinoblastoma germ cell tumors, liver tumors reach a peak between (0-4) years and malignant lymphoma, bone tumors and other – Miscellaneous – tumors reach a peak between (10-15 )years. Non Hodgkins lymphoma were the predominating lymphoma (62%), astrocytoma formed the majority of central nervous system tumors (44.6%) While rhabdomyosarcoma was the commonest histologic subtype of soft tissue tumors (76%) Ewing’s sarcoma was the commonest type of bone tumors (56%).Conclusions: A steady increase in the incidence rate of childhood tumors is noticed with a change in pattern from malignant lymphoma to CNS. tumors in the study period. A diagnostically important relationship exists between a particular type of pediatric tumors with age, sex and site

Association between Genetic Variants of GC Gene at 4q13.3 and Vitamin D Concentrations in Adult Females

Background: Vitamin D binding protein, encoded by the GC gene (on 4q13.3), plays an important role in transporting vitamin D. Several Genome-Wide Association Studies (GWASs) have established a significant association between variants of GC gene and circulating vitamin D. Objective: This study aims to determine the association of GC gene polymorphisms with vitamin D concentrations in young healthy Arab females. Methodology: 214 female subjects from Qatar University were enrolled in this cross-sectional study. The cut-off value for optimal vitamin D levels was set at 30 ng/mL. The serum vitamin D was measured using ELISA, the genotyping of SNPs (rs2298850, rs3755967, rs2282679, rs7041, rs1155563, and rs17467825) of GC gene was performed by TaqMan assay, and the data was analyzed using SPSS software. Results: The mean age of 214 participants was found to be 21.97 years. Of these, only 182 subjects were included in this study. The data showed that 14.8% were found to have optimal vitamin D levels and 85.2% with suboptimal levels. All studied SNPs were in HWE except SNPs rs7041 and rs1155563. Using the dominant model for rs2298850, the odds ratio to have low vitamin D is 1.48 (p=0.419). Similarly, rs3755967 has a risk of 1.62 (p=0.294); rs2282679 has an odds ratio of 1.32 (p=0.549); and rs17467825 with a risk of 1.48 (p=0.40). The genotypes for vitamin D levels had no significant difference (p>0.05) for all study subjects. Conclusion: The current data showed no significant association between risk alleles of SNPs (rs2298850, rs3755967, rs2282679, rs7041, rs1155563, and rs17467825) with vitamin D levels. Keywords: Vitamin D deficiency; 25-hydroxyvitamin D; GC gene; Vitamin D binding protein; SNPs Abbreviations: 25-hydroxyvitamin D/calcifediol (25-(OH)D); 1, 25-dihydroxyvitamin D/calcitriol (1,25-(OH)2D); Vitamin D Binding Protein (DBP); Group-specific Component (GC); Ultraviolet radiation B(UVB); Vitamin D Receptor (VDR); Retinoid X Receptor (RXR); Parathyroid Hormone (PTH); DNase Hypersensitivity Site (HSIV); Single Nucleotide Polymorphisms (SNP); Chemiluminescent Immunoassay (CLIA); Chemiluminescent- Microparticle Immunoassay (CMIA); Enzyme Linked Immunosorbent Assay (ELISA); High Performance Liquid Chromatography (HPLC); Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS); Body Mass Index (BMI); Overweight and Obese (OWOB); Waist Circumference (WC); Low Density Lipoprotein (LDL); High Density Lipoprotein (HDL); Triglycerides (TG); Interleukin-6 (IL-6); Minor Allele Frequency (MAF); Hardy-Weinberg Equilibrium (HWE); Confidence Intervals (CI); Analysis of Variance (ANOVA

A Multiple Classifiers Broadcast Protocol for VANET

Many types of artificial intelligent machines have been used for decision making purposes. In VANET broadcast protocols, vehicles must decide the received messages are to be rebroadcast or not. Several attributes such as sender-to-receiver distance, sender-receiver speed difference, number of neighboring vehicles, as well as vehicle’s movement direction are important measures to take the broadcast decision. As the relationships of attributes to the broadcast decision cannot be mathematically defined, the use of a classifier-based artificial intelligence may approximately predict the relationships of all the incorporated attributes to such a decision. As the decision is based on prediction, the use of multiple classifiers in decision making may increase accuracy. Therefore, this research employs a combined-classifiers at an abstract level to provide firmer broadcast decisions on VANET. Our research results justify that the performance of our combined multiple-classifiers outperformed a single-classifier scheme. The multi-classifiers scheme contributes to an average increase of 2.5% in reachability compared to that of the efficient counter–based scheme (ECS). The combined multi-classifiers scheme also improves the saving in rebroadcast tries by 38.9%

Sickle Cell Disease (SCD)

Sickle cell anemia (SCA) is a disease that is caused by the formation of an abnormal hemoglobin type, which can bind with other abnormal hemoglobin molecules within the red blood cells (RBCs) to cause rigid distortion of the cell. This distortion prevents the cell from passing through small blood vessels; leading to occlusion of vascular beds, followed by tissue ischemia and infarction. Infarction is frequent all over the body in patients with SCA, leading to the acute pain crisis. Over time, such insults result in medullary bone infarcts and epiphyseal osteonecrosis. In the brain, cognitive impairment and functional neurologic deficits may occur due to white matter and gray matter infarcts. Infarction may also affect the lungs increasing susceptibility to pneumonia. The liver, spleen, and kidney may show infarction as well. Sequestration crisis is an unusual life-threatening complication of SCA, in which a significant amount of blood is sequestered in an organ (usually the spleen), leading to collapse. Lastly, since the RBCs are abnormal, they are destroyed, resulting in a hemolytic anemia. However, the ischemic complications in patients with SCA disease far exceed the anemia in clinical significance