Prevalence of Vaginal Candidiasis among Pregnant Women with Abnormal Vaginal Discharge in Maiduguri

BACKGROUND: Pregnancy represents a risk factor in the occurrence of vaginal candidiasis. OBJECTIVES: To determine the prevalence and clinical features associated with abnormal vaginal discharge and C. albicans infection in pregnant women.METHODS: High vaginal swab samples and data on epidemiological characteristics were collected from 400 pregnant women with complaints of abnormal vaginal discharge at booking clinic of University of Maiduguri Teaching Hospital. The data was analysed using SPSS 16.0 statistical software.RESULTS: The prevalence of abnormal vaginal discharge in pregnancy was 31.5%. The frequency of abnormal vaginal discharge was 183 (45.8%) among those aged 20-24 years, 291 (72.8%) in multipara, 223 (55.8%) in those with Primary education and 293 (73.2%) in unemployed. Vulval pruritus 300 (75.0%) was significantly related to abnormal vaginal discharge (P<0.001). The prevalence of C. albicans was 41%. The frequencies of Vulval itching, Dyspareunia and vulval excoriation among those with candidiasis were 151 (50.3%), 14 (56.0%) and 75 (75.0%) respectively (P<0.001). CONCLUSION: The prevalence of abnormal vaginal discharge in pregnancy was high in this study and C. albicans was the commonest cause. It is recommended that a pregnant woman complaining of abnormal vaginal discharge be assessed and Laboratory diagnosis done in order to give appropriate treatment. Erratum Note: Ibrahim SM, Mohammed B, Yahaya M, Audu BM, Ibrahim HA on the article ”Prevalence of VaginalCandidiasis among Pregnant Women with Abnormal Vaginal Discharge in Maiduguri” on Page Nig. J. Med2013. 138-142. Should read: Ibrahim SM, Bukar M, Mohammed Y, Audu BM, Ibrahim HM

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Correlation of axial length and corneal power with refractive status of patients with refractive error in Kano, North-Western Nigeria

Background: Uncorrected refractive errors are a major cause of blindness and low vision. Determination of the etiology is essential in planning appropriate treatment modalities. Aim of this study was to determine the correlation between axial length and corneal power with refractive status of patients with refractive error in Kano, Nigeria. Methods: Observational cross-sectional study in which 385 eligible patients were recruited. Relevant history was obtained from the patients and ocular examination was done. Objective and Subjective refraction were performed. Spherical equivalent was calculated for patients with astigmatism. Keratometric readings (k1 and k2) and measurement of axial length were taken. Data was analyzed using the statistical package for the social sciences (SPSS) version 22. Results: Statistically significant inverse association (r=-1.7, r2=56.8%, p<0.0001) was found between Spherical equivalent objective refraction and axial length of right eye. Statistically significant inverse association (r=-1.2, r2=53.3%, p<0.0001) was found between Spherical equivalent subjective refraction and axial length of right eye. Statistically significant inverse association (r=-0.5, r2=8.5%, p<0.0001) was found between spherical equivalent objective refraction and corneal power of the right eye. Statistically significant inverse association (r=-0.3, r2=6.4%, p<0.0001) was found between spherical equivalent subjective refraction and Corneal power of right eye. Negative correlation   existed between axial length and corneal power but was not statistically significant (r=-0.0, p<0.4). Conclusions: The study established that axial length and corneal power are the determinants of refractive status and that axial length is a stronger determinant

Visual Twin for Pipeline Leak Detection

We describe a visual digital twin system to allow for both operation and training of a data-driven pipeline leak detection system. We show system design in terms of its data inputs and the software system which incorporates this data in real time. This system allows visualization of pipeline data and machine learning-driven leak detection in a pipeline sitting in a subsea context. The intended purpose of the system is to both train operators of the leak detection system in its use and also provide high situational awareness to those tasked with monitoring pipeline deployments. The visual digital twin system uses gaming engine technology to achieve high visual quality. We also construct a novel software system enhancement to incorporate live data streams into the gaming engine environment. This allows real-time driving of gaming engine visualization elements with which we may augment the gaming engine environment. In terms of visualization, we focus on addressing problems of large ranges of multiple scales and providing high situational awareness which minimize operator fatigue and cognitive load. We show how multiple camera views in combination with a convenient user interface can help to address these issues. We demonstrate a digital twin system for leak detection. We show its realtime operation in a gaming engine environment with the ability to instantaneously incorporate outside data sources into the visualizations. We demonstrate using simulated pipeline flow data from sensors such as pressure, temperature, etc. This is visualized in the context of a subsea pipeline on a sea floor. Given the large range of scales, we demonstrate how we can view both the full kilometer scale pipeline and smaller subsections in the context of specific sensor data streams. The overall system demonstrates a novel combination of advanced software systems which incorporates real-time data stream with visualization using a high-fidelity gaming engine. The data used represents a leak detection scenario where both operator training and situational awareness are key desired outcomes

Increasing Incidence of Plasmodium knowlesi Malaria following Control of P. falciparum and P. vivax Malaria in Sabah Malaysia

BackgroundThe simian parasite Plasmodium knowlesi is a common cause of human malaria in Malaysian Borneo and threatens the prospect of malaria elimination. However, little is known about the emergence of P. knowlesi, particularly in Sabah. We reviewed Sabah Department of Health records to investigate the trend of each malaria species over time.MethodsReporting of microscopy-diagnosed malaria cases in Sabah is mandatory. We reviewed all available Department of Health malaria notification records from 1992–2011. Notifications of P. malariae and P. knowlesi were considered as a single group due to microscopic near-identity.ResultsFrom 1992–2011 total malaria notifications decreased dramatically, with P. falciparum peaking at 33,153 in 1994 and decreasing 55-fold to 605 in 2011, and P. vivax peaking at 15,857 in 1995 and decreasing 25-fold to 628 in 2011. Notifications of P. malariae/P. knowlesi also demonstrated a peak in the mid-1990s (614 in 1994) before decreasing to ≈100/year in the late 1990s/early 2000s. However, P. malariae/P. knowlesi notifications increased >10-fold between 2004 (n = 59) and 2011 (n = 703). In 1992 P. falciparum, P. vivax and P. malariae/P. knowlesi monoinfections accounted for 70%, 24% and 1% respectively of malaria notifications, compared to 30%, 31% and 35% in 2011. The increase in P. malariae/P. knowlesi notifications occurred state-wide, appearing to have begun in the southwest and progressed north-easterly.ConclusionsA significant recent increase has occurred in P. knowlesi notifications following reduced transmission of the human Plasmodium species, and this trend threatens malaria elimination. Determination of transmission dynamics and risk factors for knowlesi malaria is required to guide measures to control this rising incidence

Visual Twin for Pipeline Leak Detection

We describe a visual digital twin system to allow for both operation and training of a data-driven pipeline leak detection system. We show system design in terms of its data inputs and the software system which incorporates this data in real time. This system allows visualization of pipeline data and machine learning-driven leak detection in a pipeline sitting in a subsea context. The intended purpose of the system is to both train operators of the leak detection system in its use and also provide high situational awareness to those tasked with monitoring pipeline deployments. The visual digital twin system uses gaming engine technology to achieve high visual quality. We also construct a novel software system enhancement to incorporate live data streams into the gaming engine environment. This allows real-time driving of gaming engine visualization elements with which we may augment the gaming engine environment. In terms of visualization, we focus on addressing problems of large ranges of multiple scales and providing high situational awareness which minimize operator fatigue and cognitive load. We show how multiple camera views in combination with a convenient user interface can help to address these issues. We demonstrate a digital twin system for leak detection. We show its realtime operation in a gaming engine environment with the ability to instantaneously incorporate outside data sources into the visualizations. We demonstrate using simulated pipeline flow data from sensors such as pressure, temperature, etc. This is visualized in the context of a subsea pipeline on a sea floor. Given the large range of scales, we demonstrate how we can view both the full kilometer scale pipeline and smaller subsections in the context of specific sensor data streams. The overall system demonstrates a novel combination of advanced software systems which incorporates real-time data stream with visualization using a high-fidelity gaming engine. The data used represents a leak detection scenario where both operator training and situational awareness are key desired outcomes

IN-OVO INCULATION OF SELENIUM NANOPARTICLES IMPROVES PRODUCTIVE PERFORMANCE, BLOOD BIOCHEMICAL PROFILE, ANTIOXIDANT STATUS AND IMMUNE RESPONSE OF HATCHED CHICKS

Selenium is a trace element essential in animal nutrition and exerts multiple actions related to enhance animal production, fertility, immune response and antioxidant defense system of chickens. The aim of the present study is investigating the benefit effects of selenium nanoparticles (SEN) in-ovo injection on productive performance, express stimulate antioxidant defense system and immune response of hatched chicks.  A total of 210 broiler breeder eggs ( Habbard Star-Bro) were divided into three in-ovo injection treatment groups,( 0, 5 and 10 ppm SEN) and incubated. Hatchability traits , productive performance,  biochemical profile, antioxidant status and immune response of hatched chicks were estimated. Results indicated significant increase in HDLcholesterol, T3, GSR, GSH, IGA, IGM and IGG as affected by in-ovo inoculated SEN levels. However feed conversion ratio, triglycerides and MDA significantly decreased by in-ovo treatments. No significant alternations were recorded in hatched chicks weight, feed intake, body weight, body weight gain, carcass characteristics, and serum levels of protein fractions, cholesterol, LDL- cholesterol, ALT, AST, ALP, uric acid, creatinine and glucose in in-ovo treated groups compared with the control one. It is summarized that, in-ovo inoculation of different levels of SEN can improve feed conversion ratio, lipid profile, antioxidants status and immunity of  broiler hatched chicks

Metabolic Pathways and Targets in Chondrosarcoma

Chondrosarcomas are the second most common primary bone malignancy. Chondrosarcomas are characterized by the production of cartilaginous matrix and are generally resistant to radiation and chemotherapy and the outcomes are overall poor. Hence, there is strong interest in determining mechanisms of cancer aggressiveness and therapeutic resistance in chondrosarcomas. There are metabolic alterations in chondrosarcoma that are linked to the epigenetic state and tumor microenvironment that drive treatment resistance. This review focuses on metabolic changes in chondrosarcoma, and the relationship between signaling via isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), hedgehog, PI3K-mTOR-AKT, and SRC, as well as histone acetylation and angiogenesis. Also, potential treatment strategies targeting metabolism will be discussed including potential synergy with immunotherapies