Hepatorenal syndrome: a review

The hepatorenal syndrome [HRS]is a reversible functional acute renal failure secondary to intense renal cortical vasoconstriction in a patient with liver disease. It affects around 40% of patients with cirrhosis and ascites1. The exact cause of the syndrome is not well understood. The state of liver dysfunction [Child-Pugh score] does not predict the occurrence of the disease. Genetic factors play no important role except as risk for liver disease and there is no sex difference. Patients with HRS characteristically have increased cardiac output, low arterial pressure, and reduced systemic vascular resistance. Diagnosis of HRS is one of exclusion. The definitive treatment is liver transplant. Nevertheless, a lot of work was done on medical therapy with promising results. Sudan Journal of Medical Sciences Vol. 1(1) 2006: 59-6

Phytochemical Analysis and Antibacterial Screening of Asparagus Flagellaris (Kunth) Bak used in The Traditional Treatment of Sexually Transmitted Diseases and Urinary Infections

The phytochemistry and antimicrobial effect of the stem, bark and leave of Asparagus flagellaris were studied. The phytochemical screening of the stem, bark and leaves showed appreciable amount of flavonoid and moderate amount of carbohydrate, cardiac glycoside and saponin while reducing sugar, ketones and pentose were detected in traces. The ethanol extract inhibited the growth of six organisms viz Escherichia coli, Corynebacteria, Klebsiella, Neiserra gonorrhoeae, Shegiella dysentariae and Candida albicans, at various concentrations, while the aqueous extract were susceptible on five organism namely Corynebacteria, Streptococcus pyogene, Proteus specie, Neiserra gonorrhoeae, and Treponema palladium. Keywords: Asparagus flagellaris, phytochemical, ethanol extract, aqueous extract and antibacterial screening.Ethiopian Journal of Environmental Studies of Management Vol. 1 (2) 2008: pp. 44-4

Staff Knowledge, Adherence to Infection Control Recommendations and Seroconversion Rates in Hemodialysis Centers in Khartoum

Introduction: We evaluated hemodialysis (HD) staff knowledge, adherence to infection control recommendations and seroconversion rates for hepatitis B virus (HBV) and hepatitis C virus (HCV) in 13 centers that continuously provided HD services in Khartoum State between June 2009 and November 2010. Methods: The knowledge of 182 HD staff members was evaluated by a self-filled questionnaire. Relevant data were obtained from 1011 HD patients by direct interviews and record review. Adherence of staff members to infection control recommendations was evaluated by direct observation. Results: HD staff members achieved a median score of 81% in knowledge evaluation (range 44-100%). Better scores were achieved by more experienced staff. We identified serious gaps in knowledge related to the environmental risk of viral transmission. Regular screening by enzyme-linked immunoassay (ELISA) was performed in 46% of centers. Only half susceptible patients were vaccinated against HBV. Staff dedicated for treatment of HBV positive patients were found in only 57% of centers that served such patients. Hand washing recommendations were strictly observed in 15% of centers, disinfection of HD stations between patients was strictly observed in 23% of centers, medications were prepared in a separate area in 8% of centers and delivered separately to each patient in none of the centers. There were 2.5 HCV seroconversions and 0.6 HBV seroconversions per 100 patient-years. Center characteristics that predicted HCV seroconversion were accommodation of HCV-positive patients in the same center, using ELISA for patient screening, and assigning more than 3 patients for one HD nurse. Conclusion: There are serious gaps in HD staff knowledge and adherence to infection control recommendations. A structured training program for HD staff members is urgently required. Keywords: Hemodialysis; HBV; HCV; Infection Control; Khartou

Phenolic Profile of Seedless Ziziphus mauritiana Fruits and Leaves Extracts with In Vivo Antioxidant and Anti-inflammatory Activities: Influence on Pro-Inflammatory Mediators

The study aimed to assess the antioxidant and anti-inflammatory activities of polyphenol-rich extracts of seedless variety of Ziziphus mauritiana (SZM). Reverse Phase High Performance Liquid Chromatography analysis of SZM leaves and fruit extracts in ethanol revealed the presence of sixteen phenolics including chlorogenic acid, p-coumeric acid, gallic acid, kaempferol and rutin. Leaf extract showed higher total phenolic and total flavonoid contents (177.6 mg/100 g and 46.2 mg/100 g) than in fruit extract (137.8 mg/100 g and 14.1 mg/100 g). The leaf extract exhibited higher DPPH radical-scavenging activity (63.5%) than the fruit extract (58.2%). The anti-inflammatory activity was evaluated on carrageenan-induced rat model and suppression of inflammatory biomarkers (Interleukin-6, Tumor necrosis factor-α and CRP) were studied.  The fruit extract exhibited inhibition (98.1%) at the dose of 500 mg/kg body weight (BW), comparable to the indomethacin (98.4%). Both extracts suppressed the inflammatory biomarkers, but pronounced results showed by the fruit extract including CRP, IL-6, and TNF-α. The leaf extract demonstrated the higher antioxidant potential as evident from the superoxide dismutase, catalase, malondialdehyde, glutathione peroxidase and glutathione levels. These findings suggest that SZM leaf and fruit extracts possess potential antioxidant and anti-inflammatory properties and can play a significant role in mitigating oxidative stress

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Respiratory difficulty caused by an ectopic brain tissue mass in the neck of a two-month-old baby: a case report

INTRODUCTION: Neuroglial heterotopia, heterotopic brain tissue, or differentiated neural tissue outside the cranial vault is uncommon, and these anomalies most commonly occur in the nasal cavity. CASE PRESENTATION: We report a case of rare pure cystic heterotopic brain tissue in a two-month-old Caucasian baby girl that presented as a large cystic neck mass and was confused with a cystic hygroma. Her mother reported a progressive increase in the size of this swelling and mild respiratory difficulty when the girl was sleeping. A computed tomography scan of the brain and neck showed a large heterogeneous mass extending from the base of the skull to the left submandibular region; a cystic component was also noted. Our patient under went total excision of the cystic mass and prevention of airway obstruction by a left submandibular approach. The final gross pathology diagnosis was heterotopic brain tissue. CONCLUSIONS: Pure cystic neck heterotopic brain tissue lesions are very uncommon, and a preoperative diagnosis of this lesion is difficult. Brain heterotopia is a rare, benign condition that should be considered in the differential diagnosis of the neonatal head and neck mass

A real time method for distinguishing COVID-19 utilizing 2D-CNN and transfer learning

Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models

Plant Disease Classifier: detection of dual-crop diseases using lightweight 2D CNN architecture

Tomatoes are the most widely grown crop in the world, and they may be found in a variety of forms in every kitchen, regardless of cuisine. It is, after potato and sweet potato, the most widely farmed crop on the planet. Cotton is another essential cash crop because most farmers grow it in huge quantities. However, many diseases reduce the quality and quantity of tomato and cotton crops, resulting in a significant loss in production and productivity. It is critical to detect these disorders at an early stage of diagnosis. The purpose of this work is to categorize 14 classes for both cotton and tomato crops, with 12 diseased classes and two healthy classes using a deep learning-based lightweight 2D CNN architecture and to implement the model in an android application named “Plant Disease Classifier” for smartphone-assisted plant disease diagnosis system, the results of the experiments reveal that the proposed model outperforms the pre-trained models VGG16, VGG19 and InceptionV3 despite having fewer parameters. With slightly larger parameters than MobileNet and MobileNetV2,proposed model also attains considerably larger accuracy than these models. The classification accuracy varies between 57% and 92% for these models, and the proposed model’s average accuracy is 97.36%. Also, the precision, recall, F1-score of the proposed model is 97 % and Area Under Curve (AUC) score of the model is 99.9% which is an indicator of the very good performance of the model. Class activation maps were shown using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to visually explain the disease detected by the proposed model, and a heatmap was produced to indicate the responsible region for classification. The app works very impressively and classified the correct disease in a shorter period of time of about 4.84 ms due to the lightweight nature of the model