This paper considers the maximum number of weekly cases and deaths caused by the COVID-19 pandemic in Iraq from its outbreak in February 2020 until the first of July 2022. Some probability distributions were fitted to the data. Maximum likelihood estimates were obtained and the goodness of fit tests were performed. Results revealed that the maximum weekly cases were best fitted by the Dagum distribution, which was accepted by three goodness of fit tests. The generalized Pareto distribution best fitted the maximum weekly deaths, which was also accepted by the goodness of fit tests. The statistical analysis was carried out using the Easy-Fit software and Microsoft Excel 2019.

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

      Coronavirus is considered the first virus to sweep the world in the twenty-first century, it appeared by the end of 2019. It started in the Chinese city of Wuhan and began to spread in different regions around the world too quickly and uncontrollable due to the lack of medical examinations and their inefficiency. So, the process of detecting the disease needs an accurate and quickly detection techniques and tools. The X-Ray images are good and quick in diagnosing the disease, but an automatic and accurate diagnosis is needed. Therefore, this paper presents an automated methodology based on deep learning in diagnosing COVID-19. In this paper, the proposed system is using a convolutional neural network, which is considered one o

The analysis of COVID-19 data in Iraq is carried out. Data includes daily cases and deaths since the outbreak of the pandemic in Iraq on February 2020 until the 28th of June 2022. This is done by fitting some distributions to the data in order to find out the most appropriate distribution fit to both daily cases and deaths due to the COVID-19 pandemic. The statistical analysis includes estimation of the parameters, the goodness of fit tests and illustrative probability plots. It was found that the generalized extreme value and the generalized Pareto distributions may provide a good fit for the data for both daily cases and deaths. However, they were rejected by the goodness of fit test statistics due to the high variability of the data.<

      Coronavirus 2 is the cause of coronavirus disease 2019 (COVID-19), which leads to severe acute respiratory illness. Matrix metalloproteinases (MMPs) have been linked to leukocyte infiltration and chemokine activation during inflammatory responses. Tissue inhibitors of metalloproteinase (TIMP) family are thought to dampen the proinflammatory effects of these MMPs. The molecular pathways of lung fibrosis are mediated by MMPs and TIMPs. In this study, we sought to investigate the probable link between MMPs, specifically MMP-3, TIMP-2, and COVID-19. The study included 58 COVID-19 patients and 30 apparently healthy individuals matched in terms of age and sex. Multiplex real- time PCR was used to detect the ORF1ab, E, and N genes of

     The worldwide pandemic Coronavirus (Covid-19) is a new viral disease that spreads mostly through nasal discharge and saliva from the lips while coughing or sneezing. This highly infectious disease spreads quickly and can overwhelm healthcare systems if not controlled. However, the employment of machine learning algorithms to monitor analytical data has a substantial influence on the speed of decision-making in some government entities.        ML algorithms trained on labeled patients’ symptoms cannot discriminate between diverse types of diseases such as COVID-19. Cough, fever, headache, sore throat, and shortness of breath were common symptoms of many bacterial and viral diseases.

This research focused on the nu

Factor analysis is distinguished by its ability to shorten and arrange many variables in a small number of linear components. In this research, we will study the essential variables that affect the Coronavirus disease 2019 (COVID-19), which is supposed to contribute to the diagnosis of each patient group based on linear measurements of the disease and determine the method of treatment with application data for (600) patients registered in General AL-KARAMA Hospital in Baghdad from 1/4/2020 to 15/7/2020. The explanation of the variances from the total variance of each factor separately was obtained with six elements, which together explained 69.266% of the measure's variability. The most important variable are cough, idleness, fever, headach

Etodolac is choice of drug for pain and inflammation but has major side effects of gastric ulcers that are due to free carboxylic group. Etodolac belongs to the chemical class of non-selective COX-inhibitor but preferentially COX-2 inhibitor. Here the ester linked mutual prodrugs of etodolac with phytophenols like vanillin, carvacrol, umbelliferone, guaiacol, sesamol and syringaldehyde were synthesized. All the prodrugs were characterized by IR-spectroscopy, ¹H-NMR, ¹³C-NMR and mass spectrometry. Among the synthesized prodrugs, the Eto-van, Eto-umbe, Eto-sesa and Eto-syr showed improved analgesic and anti-inflammatory activity compared to etodolac. All the synthesized prodrugs showed less ulcerogenic side effects co

  On the basis of the results which was previously obtained from the structural and the theoretical studies on ~-adrenergic drugs, a series of 2-propanolamine derivatives containing triazole moiety have been prepared and evaluated for their cardiovascular activity . These derivatives were tested by using spontaneously-beating right atria of albino rats.