Seroprevalence of Besnoitia besnoiti in Assiut Governorate, Egypt

Background: Bovine besnoitiosis is a widespread disease caused by Besnoitia besnoiti with significant economic losses in cattle production. There is a lack of knowledge about it in Egypt. Aim: This study was conducted to detect the seroprevalence of B. besnoiti in cattle and to find out the presence of the disease and the most important symptoms of besnoitiosis in cattle in Assiut Governorate, Egypt. Methods: A total of 190 cattle from Assiut city and its different rural centers were examined clinically and serologically for the presence of B. besnoiti. The serological examination was carried out by using the indirect enzyme-linked immunosorbent assay (ELISA) kit in serum (ID.Vet Innovative Diagnostics Louis Pasteur. Grabeis, France). The results were analyzed statistically using the chi-square test to assess the association between seroprevalence and different parameters (age, sex, season, housing, and health status). Result: Thirteen cattle were seropositive for B. besnoiti by ELISA and showed symptoms of besnoitiosis. Acute symptoms included fever, tachycardia, edematous swellings of intermandibular space and limbs with polyarthritis, diarrhea, ruminal atony, and enlarged lymph nodes. The chronic symptoms included cough, mastitis, exophthalmia, cysts on the sclera and conjunctiva, nodules in the skin, and alopecia associated with tick infestation. The overall seroprevalence of B. besnoiti was 22.1%. Regarding sex, the seroprevalence was higher for females 34.6% than for males 6.97%. While, according to age susceptibility, the seroprevalence was highest (50.9%) with age ≥5 years, followed by age >1 to <5 years (14.6%), and only one animal of age ≤1 year was recorded at 2.2%. Concerning seasonal variations, the seroprevalence was highest in spring 42.9%, followed by autumn 29.3%, winter 13.6%, and summer 7.5%. Whereas, according to the housing system, it was 60% and 8.6% in farm and household rearing, respectively. Depending on the health status, the seroprevalence was 21.6% of clinically healthy and 23.2% of clinically diseased cattle. Conclusion: The existence of B. besnoiti antibodies has been demonstrated in clinical and subclinical infected cattle in Assiut Governorate, Egypt. The ELISA test is considered to be a good diagnostic method for detecting infection. Furthermore, additional studies are essential to minimize and prevent the spread of infection

Medical image analysis for the early prediction of hypertension

Recently, medical image analysis has become a vital evolving technology that is used in the early diagnosis of various diseases. Medical imaging techniques enable physicians to capture noninvasive images of structures inside the human body (such as bones, tissues, or blood vessels) as well as their functions (such as brain activity). In this study, magnetic resonance angiography (MRA) images have been analyzed to help physicians in the early prediction of hypertension. Hypertension is a progressive disease that may take several years before being fully understood. In the United States, hypertension afflicts one in every three adults and is a leading cause of mortality in more than half a million patients every year. Specific alterations in human brains’ cerebrovasculature have been observed to precede the onset of hypertension. This study presents a computer-aided diagnosis system (CAD) that can predict hypertension prior to the systemic onset of the disease. This MRA-based CAD system is able to detect, track, and quantify the hypertension-related cerebrovascular alterations, then it makes a decision based on the analyzed data about whether each subject is at a high risk of developing hypertension or not. Such kind of prediction can help clinicians in taking proactive and preventative steps to stop the progress of the disease and mitigate adverse events

Left ventricle segmentation and quantification using deep learning

Cardiac MRI is a widely used noninvasive tool that can provide us with an evaluation of cardiac anatomy and function. It can also be used for heart diagnosis. Heart diagnosis through the estimation of physiological heart parameters requires careful segmentation of the left ventricle (LV) from the images of cardiac MRI. Therefore we aim at building a new deep learning method for the automated delineation and quantification of the LV from cine cardiac MRI. Our goal is to reach lower errors for the calculated heart parameters than the previous works by introducing a new deep learning cardiac segmentation method. Our pipeline starts with an accurate LV localization by finding LV cavity center point using a fully convolutional neural network (FCN) model called FCN1. Then, from all heart sections, we extract a region of interest (ROI) that encompasses the LV. A segmentation for the LV cavity and myocardium is performed from the extracted ROIs using FCN called FCN2. The FCN2 model is associated with multiple bottleneck layers and uses less memory footprint than traditional models such as U-net. Furthermore, we introduced a novel loss function called radial loss that works on minimizing the distance between the ground truth LV contours and the predicted contours. After myocardial segmentation, we estimate the functional and mass parameters of the LV. We used the Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset to validate our pipeline, which provided better segmentation, accurate calculation of heart parameters, and produced fewer errors compared to other approaches applied on the same dataset. Additionally, our segmentation approach showed that it can generalize well across different datasets by validating its performance on a locally collected cardiac dataset. To sum up, we propose a novel deep learning framework that we can translate it into a clinical tool for cardiac diagnosis

10 A noninvasive approach for the early detection of diabetic retinopathy

This chapter introduces one of the most critical problems in ophthalmology, specifically the diagnosis and detection of diabetic retinopathy (DR). Developing a fast, accurate, and reliable method for the early detection of DR is of great clinical importance to prevent blindness in patients. For this reason, various methods for early detection of DR have been investigated and used such as a dilated eye examination, tonometry, fluorescein angiography, optical coherence tomography, and ultrawide-field retinal imaging. With the increased popularity of machine learning, researchers have formulated their own algorithms and methods to detect DR with various rates of success. This chapter overviews past and current diagnostic methods that have been developed for DR. In addition, this chapter addresses new methodologies being developed/researched and some challenges that researchers face in developing fast, accurate, and reliable diagnosis

Towards A Robust Cad System For Early Diagnosis Of Autism Using Structural Mri

This chapter discusses a promising computer-aided diagnosis system, devised by our research team, for diagnosing autism at various stages of life, making use of the shape information in brain magnetic resonance imaging. Our system integrates the shape features extracted from both the cerebral white matter and the cerebral cortex

Phytochemical Screening, Gas Chromatography-mass Spectrometry Analysis, and Antidiabetic Effects of Corchorus olitorius Leaves in Rats

BACKGROUND: Therapies for diabetes mellitus are still meeting failure in most cases, especially in the developed stages of the disease due to incredible associating complications. Hence, there is a need for continuous development of curative therapies for that stubborn disease. AIM: We aimed to investigate the antidiabetic effects of one of the most popular plants cultivated in Egypt, C. olitorius. METHODS: Phytochemical screening of total alcoholic extract of Corchorus olitorius leaves and its aqueous and chloroform fractions revealed the presence of flavonoids, saponins, carbohydrates, tannins, coumarins, and alkaloids. RESULTS: The gas chromatography-mass spectrometry analysis showed the presence of 12 and nine chemical compounds in aqueous and chloroform extracts, respectively. C. olitorius decreased serum glucose level and α-amylase activity. This effect was more pronounced in the total alcoholic extract and its chloroform fraction than the aqueous one. The extracts also adjusted the lipid profile, reduced liver injury parameters, and caused remarkable improvement and increase number, size, and density of functioning β-cells. CONCLUSION: The findings suggest the antihyperglycemic and antioxidant effects of C. olitorius besides its beneficial effect on diabetic complications such as hyperlipidemia and liver injury. The presence of some phytochemicals such as theophylline, trans-2, 3-dimethoxycinnamic acid, 7-hydroxy-4-methyl coumarin, apigenin 7-glucoside, and glycitein may contribute to such pharmacological effects

Matrix-assisted laser desorption-ionization-time-of-flight mass spectrometry as a reliable proteomic method for characterization of Escherichia coli and Salmonella isolates

Aim: Identification of pathogenic clinical bacterial isolates is mainly dependent on phenotypic and genotypic characteristics of the microorganisms. These conventional methods are costive, time-consuming, and need special skills and training. An alternative, mass spectral (proteomics) analysis method for identification of clinical bacterial isolates has been recognized as a rapid, reliable, and economical method for identification. This study was aimed to evaluate and compare the performance, sensitivity and reliability of traditional bacteriology, phenotypic methods and matrix-assisted laser desorption-ionization-time-of-flight mass spectrometry (MALDI-TOF MS) in the identification of clinical Escherichia coli and Salmonella isolates recovered from chickens. Materials and Methods: A total of 110 samples (cloacal, liver, spleen, and/or gall bladder) were collected from apparently healthy and diseased chickens showing clinical signs as white chalky diarrhea, pasty vent, and decrease egg production as well as freshly dead chickens which showing postmortem lesions as enlarged liver with congestion and enlarged gall bladder from different poultry farms. Results: Depending on colonial characteristics and morphological characteristics, E. coli and Salmonella isolates were recovered and detected in only 42 and 35 samples, respectively. Biochemical identification using API 20E identification system revealed that the suspected E. coli isolates were 33 out of 42 of colonial and morphological identified E. coli isolates where Salmonella isolates were represented by 26 out of 35 of colonial and morphological identified Salmonella isolates. Serological identification of isolates revealed that the most predominant E. coli serotypes were O1 and O78 while the most predominant Salmonella serotype of Salmonella was Salmonella Pullorum. All E. coli and Salmonella isolates were examined using MALDI-TOF MS. In agreement with traditional identification, MADI-TOF MS identified all clinical bacterial samples with valid scores as E. coli and Salmonella isolates except two E. coli isolates recovered from apparently healthy and diseased birds, respectively, with recovery rate of 93.9% and 2 Salmonella isolates recovered from apparently healthy and dead birds, respectively, with recovery rate of 92.3%. Conclusion: Our study demonstrated that Bruker MALDI-TOF MS Biotyper is a reliable rapid and economic tool for the identification of Gram-negative bacteria especially E. coli and Salmonella which could be used as an alternative diagnostic tool for routine identification and differentiation of clinical isolates in the bacteriological laboratory. MALDI-TOF MS need more validation and verification and more study on the performance of direct colony and extraction methods to detect the most sensitive one and also need using more samples to detect sensitivity, reliability, and performance of this type of bacterial identification

Vulnerability and Weaknesses of Eating Habits of Overweight School Children as an Entry Risk for COVID-19

BACKGROUND: In developing countries, overweight among children becomes an alarming problem and a health concern. Obesity is a factor in disease severity of coronavirus disease (COVID-19) having the greatest impact on patients. AIM: The aim of this study was to determine the prevalence of overweight in some of the Egyptian governmental primary school children, its nutritional and socioeconomic determinants. Special focus was directed to identify the current dietary practices including risky nutritional habits of overweight children as a weak point leading to increasing their vulnerability to catching COVID-19 infection. METHODS: A cross-sectional observational study was conducted on primary school children aged 6–12 years. General demographic data, socioeconomic data, dietary pattern, intake of a diversity of nutrient-rich food versus calorie-dense food, and anthropometrical data were collected. RESULTS: Of 1600 child, there were 8% overweight who are considered at risk of COVID-19 infection. Considering the weekly share of the stomach, only one-third of the food consumed by overweight children is nutrient-rich, with high consumption of French fries and Candies (once per day among 95% and 78 % of overweight children, respectively). Moreover, 90% of them consume sugar-sweetened beverages (SSB) more than once per day. The majority of overweight children belonged to small, middle- income families, and had illiterate or read and write mothers. CONCLUSION: Overweight children eat narrow diversity of nutrient-rich food that includes vegetables, fruits, protein, and dairy products. They eat more calorie-dense foods, every day. The increase of family income increased the likelihood of having overweight children with a high intake of SSB, candies, and chips; consumption of snacks between meals and before sleep. Protective predictors against overweight were highly educated mothers, taking breakfast before school, having dinner, and taking meals on time. RECOMMENDATION: Nutritional behavioral education aiming at choosing nutritious and varied options of food that is effective for improving children’s nutritional status is the key to decreasing vulnerability toward COVID 19

Association of HCV with diabetes mellitus: an Egyptian case-control study

<p>Abstract</p> <p>Background</p> <p>The highest Hepatitis C Virus (HCV) prevalence in the world occurs in Egypt. Several studies from different parts of the world have found that 13% to 33% of patients with chronic HCV have associated diabetes, mostly type II Diabetes Mellitus (DM). In Egypt the prevalence of DM is 25.4% among HCV patients. Therefore, it is important to identify the magnitude of the problem of diabetes in order to optimize the treatment of chronic hepatitis C.</p> <p>Methods</p> <p>The objective of this case-control study was to evaluate the prevalence of DM and other extrahepatic (EH) manifestations among patients with different HCV morbidity stages including asymptomatic, chronic hepatic and cirrhotic patients. In this study, 289 HCV patients older than 18 were selected as cases. Also, 289 healthy controls were included. Laboratory investigations including Liver Function tests (LFT) and blood glucose level were done. Also serological assays including cryoglobulin profile, rheumatoid factor, antinuclear antibody, HCV-PCR were performed.</p> <p>Results</p> <p>Out of 289 HCV cases, 40 (13.84%) were diabetic. Out of 289 healthy controls, 12 (4.15%) were diabetic. It was found that the diabetic HCV group mean age was [48.1 (± 9.2)]. Males and urbanians represented 72.5% and 85% respectively. Lower level of education was manifested in 52.5% and 87.5% were married. In the nondiabetic HCV group mean age was [40.7 (± 10.4)]. Males and urbanians represented 71.5% and 655% respectively. secondary and higher level of education was attained in 55.4% and 76.7% were married. Comparing between the diabetic HCV group and the non diabetic HCV group, age, residence and alcohol drinking were the only significant factors affecting the incidence of diabetes between the two groups. There was no significant difference regarding sonar findings although cirrhosis was more prevalent among diabetic HCV cases and the fibrosis score was higher in diabetic HCV patients than among the non diabetic HCV cases.</p> <p>Conclusion</p> <p>The diabetic patients in the HCV group were older, more likely to have a history of alcohol drinking than the non diabetic HCV cases. Age and alcohol drinking are factors that could potentially contribute to the development of type 2 diabetes. Logistic regression analyses showed that age and residence in urban regions were the predictive variables that could be associated with the presence of diabetes. Alcohol consumption was not a significant predictive factor.</p

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator.Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator