Background: Malaria remains a leading cause of mortality in sub-Saharan Africa (including Sudan). C-reactive protein (CRP) is useful as   a marker of severity in malaria. African studies have shown that serum CRP levels correlate with parasite burden and complications in malaria, especially falciparum. However, there are no data on CRP levels in Sudanese malaria patients.

This study aims to evaluate the association between CRP levels with comorbidities, species, and complications of severe malaria

Subjects and Methods: A cross-sectional study enrolled 65 severe malaria patients at Khartoum state hospitals during the period from April to June2021. Manifestations of severe

The study was carried out to detection of H.pylori in (200) patients who attended two teaching hospitals in Baghdad. The diagnosis done by Immunochromatography methods. Stools and blood samples was taken from each patient as well as other (30) healthy control matching in their age. The study included detection the Levels of Interleukin-40 and CRP in sera of patients and control. The result indicated presence of H pylori antigen in 115 cases 59 cases of males and 51 of females, Also, the result indicated increasing levels of IL-40, cholesterol, Triglycerides, Low density lipoprotein, Very Low density lipoprotein increased significantly while the level of High density lipoprotein decreased in patients sera in comparison with healthy c

This study aimed to evaluate serum levels of steroid hormones and human chorionic gonadotropin (hCG) hormone in preeclamptic Iraqi pregnancies compared to those of healthy pregnancies.This study enrolled 120 pregnant women, divided into four groups:1. 30 healthy pregnant women.2. 37 pregnant women with mild preeclampsia3. 53 pregnant women with severe preeclampsia4. 90 pregnant women with preeclampsia Preeclamptic women and their severe cases but not mild cases had significantly (P<0.01) increased levels of serum hCG as compared with healthy pregnancies. By contrast, sera levels of estradiol were significantly (P<0.01) decreased in total preeclamptic groups and their severe cases but not in mild group as compared to healthy pregnant wome

The amicus curiae is one of the judicial procedures adopted in many judicial and legal systems around the world, under which a person who is not a party to the case, and without having a personal and direct interest in it, intervenes to draw the court’s attention to many factual and legal aspects

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a n

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In