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The Efficacy of Bedside Chest Ultrasound in the Detection of Traumatic Pneumothorax
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Background

Chest X-rays have long been used to diagnose pneumothorax. In trauma patients, chest ultrasonography combined with chest CT may be a safer, faster, and more accurate approach. This could lead to better and quicker management of traumatic pneumothorax, as well as enhanced patient safety and clinical results.

Aim

The purpose of this study was to assess the efficacy and utility of bedside US chest in identifying traumatic pneumothorax and also its capacity to estimate the extent of the lesion in comparison to the gold standard modality chest computed tomography.

Methods

This was an observational cross-sectional study of 160 patients with traumatic pneumothorax. This sample was collected from all chest trauma patients admitted to Al-Kindy Teaching Hospital in Baghdad-Iraq between November 2021 and September 2022. Such patients were to have a bedside chest US and chest CT scan performed by a skilled radiologist to detect lung point signs and lung sliding, which would be used to determine the patient's pneumothorax status.

Results

According to the study's findings, about 77.5% of the patients evaluated were men. Furthermore, 40.6% of patients experienced blunt trauma. Chest ultrasound revealed positive pneumothorax in 50 cases (31.2%), while positive pneumothorax was confirmed by computed tomography in 53 cases (33.1%) with no significance in the detection of pneumothorax between the two imaging modalities p-value(0.719). Comparably, there was no significant difference in estimating the size of a pneumothorax between the two modalities (p-value = 0.547). Chest ultrasound diagnostic accuracy showed a sensitivity of approximately 92.45%, specificity of 99.07%, and diagnostic accuracy of 96.88%.

Conclusion

Our findings indicated that chest ultrasound might be a valuable rapid diagnostic tool for traumatic pneumothorax in the emergency department in addition to diagnosis. It eliminates the need to transport patients for a CT chest scan.

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Publication Date
Wed Oct 16 2019
Journal Name
Journal Of Research In Medical And Dental Science
Evaluation of the Antibacterial Efficacy of Silver Nanoparticles as an Irrigant against Enterococcus faecalis In vitro Study
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Background: Successful root canal therapy depends on thorough chemo mechanical debridement of pulpal tissue, dentin debris and infective microorganisms. Objective: This study aimed to investigate the antibacterial effect of silver nanoparticles, sodium hypochlorite and chlorhexidine in reducing the bacterial infection of the root canals. Materials and Methods: The root canals of 55 single-rooted teeth were cleaned, shaped, and sterilized. All the teeth samples were inoculated with Enterococcus faecalis and incubated at 37°C for 2 weeks. Then, the teeth were divided into four groups. Group I (n=15): 100 ppm silver nanoparticles, Group II (n=15): 2.5 sodium hypochlorite, Group III (n=15): 2% chlorhexidine, IV (n=10): Normal saline as a contr

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Tue Jun 30 2009
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individuals in both groups. Rheumatoid arthritis
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Publication Date
Thu Jun 25 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Comparing the Efficacy of Paracetamol, Diclofenac, and Ketorolac on Post-Appendectomy Outcomes in Children and Adolescents
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urgent surgical intervention and appendectomy until now represent standard treatment for uncomplicated cases of appendicitis. Nausea, vomiting and pain after surgery are the most frequent issues facing patients and affecting patient quality of life and responsible for many cases of readmission after surgery. Ketorolac and diclofenac represent the most commonly prescribed non-steroidal anti-inflammatory used in postoperative setting and they cause many side effects as gastrointestinal, kidney and cardiac adverse effect in addition to increased risk of bleeding. Paracetamol is currently among the most frequently prescribed medication worldwide and it can be used safely for all age groups. This study aimed

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Abstract

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Journal Name
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Classification of COVID-19 from CT chest images using Convolutional Wavelet Neural Network
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<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

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Publication Date
Wed Feb 01 2023
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Classification of COVID-19 from CT chest images using Convolutional Wavelet Neural Network
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Publication Date
Fri Sep 30 2022
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