The diagnosis of COVID-19 in CT images using hybrid machine learning approaches (CNN & SVM)

The new coronavirus disease (2019) has spread quickly as an acute respiratory distress syndrome (ARDS) among millions of individuals worldwide. Furthermore, the number of COVID-19 checking obtainable in hospitals is very limited as compared to the rising number of infections every day. As an outcome, an automatic detection system must be implemented as a quick diagnostic tool for preventing or reducing the spread of COVID-19 among humans. The present paper aims to propose an automated system by means of a hybrid Deep Learning ("convolutional neural network"(CNN)) and "support vector machine (SVM)" approach for identifying COVID-19 pneumonia-infected patients on the basis of chest computed tomography (746 CT images of "COVID-19" and "non-COVID-19"). The proposed system is composed of three phases. The first, pre-processing phase begins with converting CT images into greyscale level CT images of equal size (256×256). The "contrast limited adaptive histogram equalization" technology is adopted to enhance the intensity levels, and demonstrate the feature of lung tissue. It is also necessary to normalize the division of the image elements by 255 to make the values between 0 and 1, as this will speed up the processing process. The second phase, the CNN (SimpNet model), was applied as a deep feature extraction technique to identify CT samples. The SVM classifier and SoftMax function are employed in the third phase to classify COVID-19 pneumonia-infected patients. Specificity, Sensitivity, "F-score", Accuracy, and "area under curve" are used as criteria to estimate the efficiency of the classification. The results showed a high accuracy rate of COVID-19 classification which reached (98%) and (99.1%) for CNN-SoftMax and CNN-SVM classifier, respectively in the tested dataset (225 CT images)

Effect of pre-eclampsia on glomerular filtration rate in Sudanese women

Background: Creatinine clearance is safest method to measure glomerular filtration rate (GFR) in pregnancy. The objectives was to study a case-control study conducted in Omdurman Maternity Hospital aimed to assess GFR, using creatinine clearance and magnitude of changes of serum creatinine in pre-eclampsia.Methods: Pre-eclamptic were 70, normal pregnant 96 and non-pregnant 63. Investigations were done at St Hellier's hospital London. Serum and urine creatinine were measured using Jaffé reaction and spectrophotometer. 24-hour urine output was measured and creatinine clearance calculated to find GFR. GFR was calculated in ml/min/mm2 using John Hopkins’ method.Results: The mean serum creatinine in pre-eclamptic (68.6µmol/L) was less than non-pregnant (75.5µmol/L) (P=0.001) but was higher than normal pregnant (62.4µmol/L) (P=0.003). Mean GFR pre-eclamptic (68.6ml/min.1.73m2) was less than non-pregnant (87.0ml/min/1.73m2) (P=0.0001) and normal pregnant (89.0ml/min/1.73ml/min/1.73m2) (P =0.0001).Conclusions: GFR decreased at term in normal pregnancy and even more in pre-eclampsia. Serum creatinine levels increased and did not correlate with GFR changes in pre-eclampsia

Spectrophotometric Assay of Noradrenaline in Pharmaceutical Formulation with Quinalizarin in Aqueous Solution

A simple, rapid and sensitive spectrophotometric method for the determination of noradrenaline was developed. The method is based on the proton transfer reaction with quinalizarin in aqueous neutral solution to form a violet product showing maximum absorbance at 560 nm with molar absorptivity of 6680 l.mol-1.cm-1. The method follows Beer’s law over the concentration range (5.91×10-6 -5.91×10-5) mol.L-1 The accuracy (average recovery) of the method is 99.72% and the precision (RSD) of the method is less than 1.5%.The method was successfully applied for the determination of noradrenaline in pharmaceutical formulation as injection and the results were in a good agreement with the standard addition procedure

Correlations of complete blood count, liver enzyme and serum uric Acid in Sudanese pre-eclamptic cases

Background: Pre-eclampsia is a serious disorder of pregnancy with unknown ethological factors that may occur at any stage of second or third trimester of pregnancy. The objectives of the present study were to assess changes in complete blood counts including platelets, liver enzymes and serum uric acid in pre-eclamptic cases compared to second-half normal pregnant and non-pregnant Sudanese women and their correlations to other biomarkers.Methods: This was a cross-sectional, case-control study performed from December 2008 to December 2010; in Omdurman Maternity Hospital, in concomitance with other studies in pre-eclampsia. The sample size included three groups, 72 up pre-eclamptic cases in their recent pregnancies, 96 normal pregnant in their second half of pregnancy and 63 non- pregnant (control) women; a total of 231 subjects. Questionnaire Interviews and clinical examination were done for all participants. Laboratory investigations were done including complete blood picture, liver enzymes and uric acid.  Results: The mean Hb concentration of the pre-eclamptic (11.3g/dl±1.7) was statistically significantly lower than that of the non-pregnant (12.1g/dl±0.2) (P=0.01) but not from that of the normal pregnant (11.4g/dl±0.1) (P=0.882) .There was no statistical significant difference in the mean WBC count between the pre-eclamptic (7.4x103/mm3±0.3) and non-pregnant (7.3x103/mm3±0.3) (P=0.797) and between the pre-eclamptic and normal pregnant (7.7x103/mm3±0.2) (P=0.270). There was a considerable statistical significant decrease in the mean platelets count of the pre-eclamptic (236.4/mm3±8.3) compared to the non-pregnant group (322.0/mm3±10.4) (P=0.0001) s well as to the normal pregnant (275.0/mm3±8.9) (P = 0.003). In the pre-eclamptic cases, serum ALT correlated significantly with TWCC (r=0.26, P=0.03) and serum AST (r=0.65, P=0.000). In the pre-eclamptic cases, serum AST correlated significantly with Hb (r=0.26, P=0.03), serum ALT and serum uric acid (r=0.36, P=0.01).Conclusions: There was a considerable statistical significant decrease in mean platelets count of the pre-eclamptic compared to the non-pregnant group and to the normal pregnant may be explained by hemodilution; whereas further decrease was due to pre-eclampsia. ALT and AST are strong prognostic indicators of pre-eclampsia

Anti-malarial effect of gum arabic

<p>Abstract</p> <p>Background</p> <p>Gum Arabic (GA), a nonabsorbable nutrient from the exudate of <it>Acacia senegal</it>, exerts a powerful immunomodulatory effect on dendritic cells, antigen-presenting cells involved in the initiation of both innate and adaptive immunity. On the other hand GA degradation delivers short chain fatty acids, which in turn have been shown to foster the expression of foetal haemoglobin in erythrocytes. Increased levels of erythrocyte foetal haemoglobin are known to impede the intraerythrocytic growth of <it>Plasmodium </it>and thus confer some protection against malaria. The present study tested whether gum arabic may influence the clinical course of malaria.</p> <p>Methods</p> <p>Human erythrocytes were <it>in vitro </it>infected with <it>Plasmodium falciparum </it>in the absence and presence of butyrate and mice were <it>in vivo </it>infected with <it>Plasmodium berghei </it>ANKA by injecting parasitized murine erythrocytes (1 × 10⁶) intraperitoneally. Half of the mice received gum arabic (10% in drinking water starting 10 days before the day of infection).</p> <p>Results</p> <p>According to the <it>in vitro </it>experiments butyrate significantly blunted parasitaemia only at concentrations much higher (3 mM) than those encountered <it>in vivo </it>following GA ingestion (<1 μM). According to the <it>in vivo </it>experiments the administration of gum arabic slightly but significantly decreased the parasitaemia and significantly extended the life span of infected mice.</p> <p>Discussion</p> <p>GA moderately influences the parasitaemia and survival of <it>Plasmodium-</it>infected mice. The underlying mechanism remained, however, elusive.</p> <p>Conclusions</p> <p>Gum arabic favourably influences the course of murine malaria.</p

glue sniffing neuropathy and review of literature

Glue sniffing neuropathy commonly known as n-hexane neuropathy. It is well documented that industrial exposure to n-hexane causes neuropathy, however it is less well recognized that inhalation of n-hexane present in the vapors can also cause neuropathy However such patients are not seen that frequently. The acute worsening also generates differential diagnosis of GBS. Most of literature is reported from west .We report such case for the first time from Saudi Arabia. A 35 year old male presented to us with progressive numbness followed by weakness in both legs since last three weeks. Over next two week he became chair bound and in the beginning of third week he also stated to feel numbness in both the hands and some weakness was also noted in hands. His past history was significant for carpet cleaning glue sniffing for many years. His exam was significant for distal weakness feet greater than hands, deep tendon reflexes were absent all over. All sensory modalities showed glove and stocking pattern. Nerve conduction velocities showed slowing. His CSF exam was normal. We conclude that n-hexane is neurotoxic when inhaled to excess and, that the neuropathy has characteristic electrophysiological and pathological features

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model’s feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm’s feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm’s regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon’s rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting

Origanum majorana L. polyphenols: in vivo antiepileptic effect, in silico evaluation of their bioavailability, and interaction with the NMDA receptor

Introduction: Epilepsy is a chronic brain disease characterized by repeated seizures and caused by excessive glutamate receptor activation. Many plants are traditionally used in the treatment of this disease. This study aimed to evaluate the bioavailability of a polyphenolic extract obtained from Origanum majorana L. (OMP) leaves, as well as its antiepileptic activity and its potential mechanism of action.Methods: We have developed and validated a simple, rapid, and accurate stability-indicating reversed-phase liquid chromatographic method for the simultaneous determination of caffeine and quercetin in rat plasma. The OMP antiepileptic effect was evaluated with pilocarpine-induced seizures, and a docking method was used to determine the possible interaction between caffeic acid and quercetin with the N-methyl-D-aspartate (NMDA) receptor.Results and Discussion: Both compounds tested showed low bioavailability in unchanged form. However, the tested extract showed an anticonvulsant effect due to the considerably delayed onset of seizures in the pilocarpine model at a dose of 100 mg/kg. The molecular docking proved a high-affinity interaction between the caffeic acid and quercetin with the NMDA receptor. Taken together, OLP polyphenols demonstrated good antiepileptic activity, probably due to the interaction of quercetin, caffeic acid, or their metabolites with the NMDA receptor

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EV type for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics such as accuracy, sensitivity, specificity, and F-score. The improvements observed in these metrics are 91.22%, 94.34%, 89.5%, and 88.5%, respectively. These results highlight the overall effectiveness of the proposed method. Hence, the suggested approach is seen more favorable for implementing the voting classifier in the context of the EV population across different geographical areas