Enhancing Authentication in Online Distant Exams: A Proposed Method Utilizing Face and Voice Recognition

Due to COVID-19 pandemic, face-to-face teaching has been replaced by online education to reduce the risks of spreading the Coronavirus. Online examination is an important asset in the context of online learning to assess students, but observing students during testing and ensuring that they do not engage in misbehavior remains a major issue. Human observation is one of the most common methods when conducting an exam to ensure that students do not perform any unexpected behaviors, by entering the student in a laboratory or hall at the university and observing him throughout the exam period visually and soundly. However, this method is costly and labor-intensive. In this paper, a system is created that monitors students during an online test automatically based on face recognition and voice recognition using a machine learning algorithm. The camera on the students computer will be used to track the students facial movements, pupils, and lip movements, monitoring the students behavior throughout the test, and stopping any unexpected behavior. In this system, there are two parts: facial recognition and unexpected behavior detection. The face was recognized with an accuracy of 98.3%, and unexpected behavior was detected with an accuracy of 97.6%. There is also an opportunity to increase accuracy by improving the quality of the images in the dataset

Study of diagnostic value of D-Dimer Serum level as a marker in neonatal sepsis

Background: Neonatal septicemia is widely recognized around the world. A prominent cause of infant mortality and morbidity. Folinolysis in sepsis causes a rise in the D-dimer marker, which is generated when cross-linked fibrin breaks down.Objective: The goal of this work was to evaluate the clinical significance of D-Dimer level for diagnosis of neonatal sepsis.Patients and Methods: Our study was done on 90 neonates divided into two groups: 45 septic neonates as cases and 45 healthy controls. A complete medical history, clinical examination, and diagnostic tests were performed for all newborns (CBC, CRP, blood culture, D-dimer).Results: Most of blood cultures were negative (42.2%) and the other positive cultures showed that klebsiella was the most common organism (22.2%), E-coli was 15.5%, Pseudomonas was 8.89%, Staph. Aureus was 6.67% and the less common was GBS (4.4%). CRP and D-dimer levels were significantly elevated in neonatal sepsis cases compared to controls. D-dimer at a cutoff point higher than 2 had 97.8% accuracy for detection of neonatal sepsis with 100.0% sensitivity and 95.6% specificity. D-dimer levels were significantly higher in infant sepsis patients who died compared to those who survived (5.5 ± 1.3 versus 3.2 ± 1.4) respectively indicating that D-dimer increased with increased severity of cases who had bad prognosis.Conclusion: D-dimer had 97.8% accuracy for detection of neonatal sepsis with 100.0% sensitivity and 95.6% specificity. So it may be used as a marker in neonatal sepsis

Diffusion tensor magnetic resonance imaging in the gradingof liver fibrosis associated with congenital ductal plate malformations

Purpose: Liver biopsy is still the standard method for the diagnosis of ductal plate malformations (DPM). However, it is an invasive tool. Magnetic resonance imaging (MRI) has shown its accuracy in the diagnosis of this pathology. Herein, a study was conducted to elucidate the role of diffusion MRI parameters in predicting the degree of hepatic fibrosis. Material and methods: This prospective study included 29 patients with DPM and 20 healthy controls. Both groups underwent diffusion tensor magnetic resonance imaging (DT-MRI), and its parameters were compared between patients and controls, and then they were correlated with the degree of liver fibrosis in the patient group. Results: All patients with DPM, whatever its type, expressed a significantly lower hepatic apparent diffusion coefficient (ADC) compared to controls. However, fractional anisotropy (FA) showed no significant difference between them. The ADC value of 1.65 × 10-3 mm2/s had sensitivity and specificity of 82.1% and 90%, respectively, in differentiating DPM patients from healthy controls. It was evident that patients with higher fibrosis grades had significantly lower hepatic ADC, indicating a negative correlation between ADC and the grade of hepatic fibrosis; rs = -0.901, p < 0.001. Conclusions: DT-MRI showed good efficacy in the diagnosis of congenital DPM. Moreover, ADC could be applied to monitor the degree of liver fibrosis rather than the invasive liver biopsy. No significant correlation was noted between the FA and the grades of liver fibrosis

Outcome of Acute Kidney Injury (AKI) in Coronavirus Disease 2019 (COVID-19) patients

Background: Coronavirus Disease 2019 (COVID-19) is a globally emerging illness, resulting in potential effects on public health and global economies. Objectives: To assess the incidence of Acute Kidney Injury among patients who are infected with COVID-19, and to evaluate risk factors. Patients and methods: This study enrolled100 adult patients infected with COVID-19 and recently diagnosed with polymerase chain reaction (PCR). The patients were submitted to clinical examination and laboratory testing for ESR, CRP, CBC, Serum creatinine, and D-dimer. Patients were also assessed radiologically by CT Chest. Highresolution computed tomography Parenchymal abnormalities on HRCT were assessed. AKI patients were classified based on Acute Kidney Injury Network staging. Results: The mean age of all studied patients was 48.1 ± 10.8 years and mean BMI of all studied patients was 31.3 ± 4.6 kg/m2, 51 patients were males (51%) and 49 females (49%). There were 35 patients (35%) with a mild infection, 23 patients (23%) with moderate, and 42 patients (42%) with severe in the studied patients. The overall AKI prevalence among COVID-19 patients was 18 %. All of them were grade III AKI. Our study revealed that old age, severity of infection, dyspnea, elevated CRP, ALT, AST, PT, INR, Urea, and Creatinine were considerable distinct predictors for AKI. Conclusion: The Prevalence of AKI among COVID-19 patients was 18 %. Old age, the severity of infection, dyspnea, elevated CRP, increased serum urea, Creatinine were significant independent predictors for AKI

Bacterial Diseases Affecting the Cultured Sepia Officinalis Leading to Increase Mortality Rates in The Laboratory

The early growth, mortality rates and bacterial infections of the cultured Sepia Officinalis were experimentally studied in the laboratory. Two hundred eighty-five sepia larvae were hatched and placed in a 100-liter capacity rectangular glass aquarium (filled with seawater) in the laboratory. The Sepia individuals (285 individuals) were divided into two groups the first fed on a mixture of amphipods, rotifers and artemia and the second group fed only on amphipods to follow their growth and mortality. The second group was observed to grow faster with length 6.76 ± 0.06mm and weight 0.11 ± 0.01gm than the first one. The survival rate was 100% by the end of the first week and decreased gradually by the end of the second week. The recorded mortality rate reached 49% by the day 15th, where they infected with bacterial disease of Vibrio alginolyticus. The clinical signs of the diseased S. Officinalis were lethargic condition, food fasting and multiple skin ulcers with white-gray discoloration were observed and appeared on the body. The main postmortem lesions were congestion of the internal organ, beside the presence of ascetic fluid. The mortality among the diseased Sepia was increased by age; however, it may cause death of most individuals by increasing time more than two weeks. The findings of antibiotic sensitivity test cleared that the isolated V. alginolyticus was sensitive to amoxiclav (amoxicillin-clavulanate), streptomycin, ciprofloxacin and chloramphenicol. Controversially, it was resistant to oxytetracycline, tobramycin, gentamycin and enrofloxacin

Bacterial Diseases Affecting the Cultured Sepia Officinalis Leading to Increase Mortality Rates in The Laboratory

The early growth, mortality rates and bacterial infections of the cultured Sepia Officinalis were experimentally studied in the laboratory. Two hundred eighty five sepia larvae were hatched and placed in a 100 liter capacity rectangular glass aquarium (filled with seawater) in the laboratory. The Sepia individuals (285 individuals) were divided into two groups the first fed on a mixture of amphipods, rotifers and artemia and the second group fed only on amphipods to follow their growth and mortality. The second group was observed to grow faster with length 6.76 ± 0.06mm and weight 0.11 ± 0.01gm than the first one. The survival rate was 100% by the end of the first week and decreased gradually by the end of thesecond week. The recorded mortality rate reached 49% by the day 15th, where they infected with bacterial disease of Vibrio alginolyticus. The clinical signs of the diseased S. Officinalis were lethargic condition, food fasting and multiple skin ulcers with white-gray discoloration were observed and appeared on the body. The main post mortem lesions were congestion of the internal organ, beside the presence of ascetic fluid. The mortality among the diseased Sepia was increased by age; however it may causes death of most individuals by increasing time more than two weeks. The findings of antibiotic sensitivity test cleared that the isolated V. alginolyticus was sensitive to amoxiclav (amoxicillin-clavulanate), streptomycin, ciprofloxacin and chlormphinicol. Controversially, it was resistant to oxytetracycline, tobramycin, gentamycine and enrofloxacin.Keywords: Sepia Officinalis - growth rate - mortality rate - bacterial infection

Etiology, Pathogenesis, And Management Options Of Infra-Vesical Obstruction Due To Benign Prostatic Hyperplasia, Urinary Bladder Stone, Or Both: Review Article

Abstract Background: Urinary bladder stones may be a primary stone formed in the urinary bladder or migrating calculus from the upper urinary tract. Bladder stones become more symptomatic when associated with infra-vesical obstruction.The most common cause of infra-vesical obstruction in elderly men is benign prostatic hyperplasia. Benign prostatic hyperplasia can be identified clinically by a complex of symptoms. These symptoms, known as lower urinary tract symptoms, range from incomplete emptying, weak stream, nocturia, and increased urinary frequency, and can potentially progress to urinary urge incontinence and urinary retention. About 35% of elderly men above fifty years will seek medical advice and have medical treatment for infra-vesical obstruction. About 24% of patients with mild to moderate LUTS will undergo surgical management for BPH. The strong association between infra-vesical obstruction due to benign prostatic hyperplasia and urinary bladder stones has led to the dogma that any BPH associated with bladder stones should be managed surgically. This study aims to review the etiology, pathogenesis, and management options of infra-vesical obstruction caused by BPH, urinary bladder stones, or both. We have searched literature in the American National Center for Biotechnology Information (NCBI), PubMed, Google scholar, Egyptian bank of knowledge,and science direct

The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients

Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs energy markers were extracted from each CT scan by tuning the MGRF parameters on each lesion separately. The latter were healthy/mild, moderate, and severe lesions. To represent these markers more reliably, a Cumulative Distribution Function (CDF) was generated, then statistical markers were extracted from it, namely, 10th through 90th CDF percentiles with 10% increments. Subsequently, the three extracted markers were combined together and fed into a backpropagation neural network to make the diagnosis. The developed system was assessed on 76 COVID-19-infected patients using two metrics, namely, accuracy and Kappa. In this paper, the proposed system was trained and tested by three approaches. In the first approach, the MGRF model was trained and tested on the lungs. This approach achieved 95.83% accuracy and 93.39% kappa. In the second approach, we trained the MGRF model on the lesions and tested it on the lungs. This approach achieved 91.67% accuracy and 86.67% kappa. Finally, we trained and tested the MGRF model on lesions. It achieved 100% accuracy and 100% kappa. The results reported in this paper show the ability of the developed system to accurately grade COVID-19 lesions compared to other machine learning classifiers, such as k-Nearest Neighbor (KNN), decision tree, naïve Bayes, and random forest

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support