The pandemic of coronavirus disease 2019 (COVID-19), first reported in China, in December 2019 and since then the digestive tract involvement of COVID-19 has been progressively described. In this review, I summed recent studies, which have addressed the pathophysiology of COVID-19-induced gastrointestinal symptoms, their prevalence, and bowel pathological and radiological findings of infected patients. The effects of gut microbiota on SARS-CoV-2 and the challenges of nutritional therapy of the infected patients are depicted. Moreover, I provide a concise summary of the recommendations on the management of inflammatory bowel disease, colorectal cancer, and performing endoscopy in the COVID era. Finally, the COVID pancreatic relation was explored. Conclusions: digestive symptoms in COVID-19 patients can be the only manifestation and they may be correlated with worse clinical outcomes. The likelihood of fecal-oral transmission of COVID-19 has significant consequences and requires further research. A clear link may exist between the gut microbiome and COVID-19 progression and it may have a therapeutic and prognostic value. No evidence for an increased frequency of covid-19 cases in IBD and stopping immunosuppressive medications is not advised. Triage and risk assessment of patients with suspected or confirmed COVID-19 before endoscopy is essential; deferral of elective endoscopies should be considered.
Background: Gastroesophageal reflux disease, is a quite prevalent gastrointestinal disease, among which gastric content (excluding the air) returns into the oral cavity. Many 0ral manifestations related t0 this disease include tooth wear, dental caries also changes in salivary flow rate and pH. This study was conducted among gastroesophageal reflux disease patients in order to assess tooth wear in relation to salivary flow rate and pH among these patients and the effect of gastroesophageal reflux disease duration on this relation. Materials and methods: One hundred patients participate in this cross-sectional study for both genders and having an age range of 20-40 years old, patients had been endoscopically identified as having gastroeso
... Show MoreBackground: The highest concentrations of
blood glucose during the day are usually found
postprandialy. Postprandial hyperglycemia (PPH)
is likely to promote or aggravate fasting
hyperglycemia. Evidence in recent years suggests
that PPH may play an important role in functional
& structural disturbances in different body organs
particularly the cardiovascular system.
Objective: To evaluate the effect of (PPH) as a
risk factor for coronary Heart disease in Type 2
diabetic patients.
Methods: Sixty-three type2 diabetic patients
were included in this study. All have controlled
fasting blood glucose, with HbA1c correlation.
They were all followed for five months period
(from May to October 2008)
Background: Hemoglobin A1c (HbA1c) is a widely used test for glycemic control. It is done for chronic kidney disease (CKD) patients. Renal disease is accompanied by thyroid abnormalities, which affect HbA1c, especially in those taking erythropoiesis-stimulating agents (ESAs). We aimed to find the effect of thyroid dysfunction on HbA1c in hemodialysis patients taking ESAs and those who do not. Materials and Method: Fifty six patients were included in this study, which was done between September 2017 and June 2018, in Baghdad Teaching Hospital. Thyroid stimulating hormone, free T3, free T4 and HbA1c measurements were done. The patients were divided into 2 groups; those who took ESAs and those who did not, then they were subdivided into those
... Show MoreIn this paper, the general framework for calculating the stability of equilibria, Hopf bifurcation of a delayed prey-predator system with an SI type of disease in the prey population, is investigated. The impact of the incubation period delay on disease transmission utilizing a nonlinear incidence rate was taken into account. For the purpose of explaining the predation process, a modified Holling type II functional response was used. First, the existence, uniform boundedness, and positivity of the solutions of the considered model system, along with the behavior of equilibria and the existence of Hopf bifurcation, are studied. The critical values of the delay parameter for which stability switches and the nature of the Hopf bifurcat
... Show MoreSystemic Lupus Erythematosus (SLE) is a multifactorial chronic systemic autoimmune disease. It is characterized by a lack of immune tolerance to autoantigens such as nuclear antigens. The aim of the study is to assess the interferon-alpha (IFN-α) serum level in Iraqi patients with SLE and determine its potential relation to different clinical and laboratory parameters and disease activity. 100 SLE patients were all females and with a mean of age 31.3 ± 10 years (16-63years) and disease duration of 5.8 ± 3.7years (1 month to 15 years). The average of SLEDAI score ranged from 2 to 22 with a mean of (8.53 ±3.42). Proteinuria, ESR, creatinine and AST were significantly higher (65% vs. 10% and 0.62±0.11 vs. 0.70±0.14 mg/dl resp
... Show MoreFifty celiac disease (CD) patients (21 males and 29 females) with an age range of 2-35 years and 25 apparently healthy controls were investigated for 10 autoantibodies (anti-tissue transglutaminase IgA antibody; ATA, anti-tissue transglutaminase IgG antibody; ATG, anti-gliadine IgA antibody; AGA, anti-gliadine IgG antibody; AGG, anti-nuclear antibody; ANA, anti-double strand DNA antibody; AdsDNA, anti-thyroid peroxidase antibody; ATP, anti-phospholipid antibody; APP, anti-myeloperoxidase antibody; AMP and anti-proteinase 3 antibody; AP3) in their sera. Six autoantibodies (ATA, ATG, AGA, AGG, AMP and AP3) showed significant variations between CD patients and controls. The first four antibodies were not detected in sera of controls, while
... Show MoreIn recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne
... Show MoreA hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m
... Show MoreAbstract Background Hemoglobin A1c (HbA1c) is a widely used test for glycemic control. It is done for chronic kidney disease (CKD) patients. Renal disease is accompanied by thyroid abnormalities, which affect HbA1c, especially in those taking erythropoiesis-stimulating agents (ESAs). We aimed to find the effect of thyroid dysfunction on HbA1c in hemodialysis patients taking ESAs and those who do not. Materials and Method Fifty six patients were included in this study, which was done between September 2017 and June 2018, in Baghdad Teaching Hospital. Thyroid stimulating hormone, free T3, free T4 and HbA1c measurements were done. The patients were divided into 2 groups; those who took ESAs and those who did not, then they were subdivided into
... Show MoreHeart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac
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