Surgical Antimicrobial Prophylaxis : Challenges in translating evidence to practice

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Hepatitis B virus and hepatitis C virus in pregnant Sudanese women

<p>Abstract</p> <p>Background</p> <p>The epidemiology of viral hepatitis during pregnancy is essential for health planners and programme managers. While much data exist concerning viral hepatitis during pregnancy in many African countries, no proper published data are available in Sudan.</p> <p>Aim</p> <p>The study aimed to investigate the sero-prevalance and the possible risk factors for hepatitis B virus (HBV) and hepatitis C virus (HCV) among antenatal care attendants in central Sudan.</p> <p>Methods</p> <p>During 3 months from March–June 2006, sera were collected from pregnant women at Umdurman Maternity Hospital in Sudan, and they were tested for markers of hepatitis B virus (HBVsAg) and HCV.</p> <p>Results</p> <p>HBVsAg was detected in 41 (5.6%) out 728 women, Anti-HCV was detected in 3 (0.6%) out of 423 women, all of them were not aware of their condition. Age, parity, gestational age, residence, history of blood transfusion, dental manipulations, tattooing and circumcision did not contribute significantly to increased HBVsAg sero-positivity.</p> <p>Conclusion</p> <p>Thus 5.6% of pregnant women were positive for HBVsAg irrespective of their age, parity and socio-demographic characteristics. There was low prevalence of Anti-HCV.</p

Differential regulation of the Drosophila sleep homeostat by circadian and arousal inputs.

One output arm of the sleep homeostat in Drosophila appears to be a group of neurons with projections to the dorsal fan-shaped body (dFB neurons) of the central complex in the brain. However, neurons that regulate the sleep homeostat remain poorly understood. Using neurogenetic approaches combined with Ca2+ imaging, we characterized synaptic connections between dFB neurons and distinct sets of upstream sleep-regulatory neurons. One group of the sleep-promoting upstream neurons is a set of circadian pacemaker neurons that activates dFB neurons via direct glutaminergic excitatory synaptic connections. Opposing this population, a group of arousal-promoting neurons downregulates dFB axonal output with dopamine. Co-activating these two inputs leads to frequent shifts between sleep and wake states. We also show that dFB neurons release the neurotransmitter GABA and inhibit octopaminergic arousal neurons. We propose that dFB neurons integrate synaptic inputs from distinct sets of upstream sleep-promoting circadian clock neurons, and arousal neurons

The effect of water deprivation on the pharmacokinetics of antipyrine and sulphadimidine following intravenous administration in Nubian goats

The effect of water deprivation on the pharmacokinetic parameters of antipyrine and sulphadimidine in the Nubian goat was studied. Water deprivation, to a level of dehydration at which the animals lost an average of 7.5% body weight, resulted in a significant reduction in antipyrine clearance (p < 0.05), and a consequently increased AUC value (p < 0.05). No effect was observed on the distribution parameters of the drug. In dehydrated animals which had lost an average of 10% or 12.5% of their body weight owing to water deprivation, significant changes were found in the distribution and elimination pharmacokinetic parameters of antipyrine and sulphadimidine. The volume of distribution was significantly decreased, resulting n elevated plasma levels for the two drags compared to normally watered animals. Significant decreases in clearance and subsequent prolongation of the elimination half-lives were observed during these periods of water deprivation. These changes in the disposition kinetics of the two drugs may be attributed to the loss of total body water and extracellular fluids and changes in the liver and kidney functions taking place during dehydration

BotCap: Machine Learning Approach for Botnet Detection Based on Statistical Features

In this paper, we describe a detailed approach to develop a botnet detection system using machine learning (ML)techniques. Detecting botnet member hosts, or identifying botnet traffic has been the main subject of manyresearch efforts. This research aims to overcome two serious limitations of current botnet detection systems:First, the need for Deep Packet Inspection-DPI and the need to collect traffic from several infected hosts. Toachieve that, we have analyzed several botware samples of known botnets. Based on this analysis, we haveidentified a set of statistical features that may help to distinguish between benign and botnet malicious traffic.Then, we have carried several machine learning experiments in order to test the suitability of ML techniques andalso to pick a minimal subset of the identified features that provide best detection. We have implemented ourapproach in a tool called BotCap whose test results showed its proven ability to detect individually infected hostsin a local network

Torsion Improvement of Reinforced Self-Compacting Concrete Beams Using Epoxy Injection and CFRP

Few researchers have investigated the internal torsional reinforcement of box beams, So, this study aims to find out the possibility of adding a certain percentage of RCA to the NC mixtures, as well as verifying the success achieved in repairing the cracks that occurred as a result of torsion with CFRP or injecting with epoxy, which has not been addressed in previous research and literature reviews. This study reinforces reinforced SCC box columns subjected to complete torsion with CFRP sheets and epoxy resin injections. Four types SCC specimens (the first beam with 0%, the second beams with 33.3%, the third beams with 67.7%, and the fourth beams with 100% RCA by weight) were subjected to pure torsion until failure. The dimensions and reinforcement of every specimen are identical. In addition, the applied torque-twist angle relationship at the midspan and end span was investigated. Bending experiments were performed to establish load-deflection curves and assess failure modes. After structural rehabilitation, all beams exhibited increased rigidity values, according to the results. Epoxy resin and CFRP sheet contributed to the specimens' increased ultimate load. The ultimate strength of RCA beams strengthened with CFRP and injected with epoxy increased. The specimens' flexural strength was considerably enhanced by the combination of surface roughness and fracture injection, and the effectiveness of using RCA was very good; it could be replaced with NCA in concrete mixtures, according to the ratio and need. Doi: 10.28991/CEJ-2023-09-11-05 Full Text: PD

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects

\ua9 2024 by the authors. Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer’s and Parkinson’s diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research

Ebola preparedness in Oman: An experience from the Middle East

LETTER TO THE EDITOR

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS):Current Applications and Future Prospects

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer’s and Parkinson’s diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research

An agricultural investment map based on geographic information system and multi-criteria method.

The study aimed to produce an investment classification map, which shows the potential areas of investment in agriculture in Sinnar, Sudan. The spatial multi-criteria analysis was used to rank and display potential locations, while the analytical hierarchy process method was used to compute the priority weights of each criterion. The study attempted to explore the utilization of Geographic Information System (GIS) to map the potential investment areas, therefore, it did not cover a comprehensive analysis of all factors that influence investment in agriculture. In addition, the analysis was limited to criteria that had spatial reference. The investment criteria for spatial analysis were defined from the guidelines provided by the Ministry of Investment, Sudan. Even with the shortcomings of the data, it was found that the results obtained were very encouraging and provided clear indicative areas for agricultural investment in Sinnar. Government agencies can use GIS to access information regarding the potential areas of investment, and minimize investment risks. On the other hand, the economic development organizations will now have the ability to benefit from the Geographic Information System (GIS) solutions by leveraging on this technology to attract and retain business from worldwide sources. Thus, the model will serve as a decision support tool for investors and decision makers at various levels