Corona virus sickness has become a big public health issue in 2019. Because of its contact-transparent characteristics, it is rapidly spreading. The use of a face mask is among the most efficient methods for preventing the transmission of the Covid-19 virus. Wearing the face mask alone can cut the chance of catching the virus by over 70\%. Consequently, World Health Organization (WHO) advised wearing masks in crowded places as precautionary measures. Because of the incorrect use of facial masks, illnesses have spread rapidly in some locations. To solve this challenge, we needed a reliable mask monitoring system. Numerous government entities are attempting to make wearing a face mask mandatory; this process can be facilitated by using face mask detection software based on AI and image processing techniques. For face detection, helmet detection, and mask detection, the approaches mentioned in the article utilize Machine learning, Deep learning, and many other approaches. It will be simple to distinguish between persons having masks and those who are not having masks using all of these ways. The effectiveness of mask detectors must be improved immediately. In this article, we will explain the techniques for face mask detection with a literature review and drawbacks for each technique.
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
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Background: Heat shock proteins have a general role in the response of the arterial wall to stress and may serve as a mediator/inducer of atherosclerosis in particular circumstances when HSPs specifically bind to the Toll-like receptor 4/CD14 complex, initiating an innate immune response, including the production of pro-inflammatory cytokines, this also followed by cytokine amplification through transmigration of macrophages and neutrophils.
Objective: To investigate the percentage of expression of HSP60 by peripheral blood lymphocyte (PBL) in atherosclerotic coronary heart disease (CHD) patients using immunocytochemistry technique.
Method: A total of fifty patient (40 males and 10 females), ranged from the mean age (59.12±8.54) y
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Background: Chronic Obstructive Pulmonary Disease (COPD) represents one of the major causes of chronic morbidity where, airflow limitation is caused by a mixture of small airways disease and parenchyma destruction.
Objective: to correlate the clinical characteristics of patients with COPD with imaging classification into phenotypes.
Patients and Methods: Thirty patients with stable COPD were examined by chest CT. Bronchial wall thickness is evaluated by measuring the wall area percentage by identifying the trunk of the apical bronchus of the right upper lobe, while the extent of emphysema was assessed using the percentage of lung voxels with X-ray attenuation values less than -950 HU {automatically calculated by special software}.<
Background:
In 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
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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 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
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Heart 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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The main goal of this research is to determine the impact of some variables that we believe that they are important to cause renal failuredisease by using logistic regression approach.The study includes eight explanatory variables and the response variable represented by (Infected,uninfected).The statistical program SPSS is used to proform the required calculations
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Background: Autism spectrum disorder (ASD) is a general term for a group of complex disorders of brain development; these disorders have no single known cause, they are characterized, in varying degrees, by difficulties in social interaction, verbal and nonverbal communication and repetitive behaviors.
Objective: The aim of the study was to evaluate different biochemical parameters in some autistic Iraqi children, and to compare the results with healthy children who matched with age, looking for any alteration in the studied parameters in order to understand the biochemistry of this disorder.
Patients and Methods: Forty one consecutive autistic children admitted to (Al Safa center for autism and Iben- AL Rs
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