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Spectrum of CT appearance and CT severity index of COVID-19 pulmonary infection in correlation with age, sex, and PCR test: an Iraqi experience
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Abstract<sec> <title>Background

Since June 2020, an explosion in number of new COVID-19 patients has been reported in Iraq with a steady increment in new daily reported cases over the next 3 months. The limited number of PCR kits in the country and the increment in the number of new COVID-19 cases makes the role of CT scan examinations rising and becoming essential in aiding the health institutions in diagnosing and isolating infected patients and those in close contacts. This study will review the spectrum of CT pulmonary changes due to COVID-19 infection and estimate the CT severity score index and its relation to age, sex, and PCR test results.

Results

The ground glass opacities were the most common encountered pattern of pulmonary changes and were seen in (79%). There was strong positive correlation between higher CT severity score and male gender (p value = 0.0002, R2 = 0.9). Also, there was significant correlation of CT severity score and increasing age (p value less than 0.00018). Significant correlation was seen between CT scan percentage of lung involvement and positive PCR test results (p value = 0.001917), as the CT severity index is increasing, the PCR test is more likely to be positive.

Conclusions

Chest CT is an important and fast imaging tool for the diagnosis of COVID-19-infected patients especially in developing countries. In addition, chest CT can predict the disease severity by showing the percentage of lung involvement and hence give an idea about the prognosis of the disease. Higher CT severity score is significantly correlated with male gender, older age group patients and likely positive PCR test.

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PREDICTION OF BEARING CAPACITY, ANGLE OF INTERNAL FRICTION, COHESION, AND PLASTICITY INDEX USING ANN (CASE STUDY OF BAGHDAD, IRAQ)
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In the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and

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