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Background COVID-19 may cause respiratory distress syndrome and death. Treatment of COVID-19 to prevent complications remains a priority. Our investigation sought to determine whether combination of spironolactone and sitagliptin could reduce mortality for inpatient with SARS-CoV-2 infection. Methods This single blind, 4-arms, prospective randomized clinical trial was conducted at Shiraz and Bushehr University of Medical Sciences hospitals between December 2020 and April 2021. We randomized hospitalized adult patients with COVID-19 pneumonia into four groups: control, combination therapy, sitagliptin add on, or spironolactone add on. The primary outcome was the clinical improvement of the patients in the hospital as measured on an eight-point numerical scale. The secondary outcomes included intubation, ICU admission, end organ damages, CT findings and paraclinical information. Results 263 admitted patients were randomly assigned to control group (87 patients), combination group (60 patients), sitagliptin group (66 patients) and spironolactone group (50 patients). There were no significant differences in baseline characteristics, except for higher age in control group. The intervention groups, especially combination therapy, had better clinical outcomes (clinical score on 5th day of admission was 3.11 ± 2.45 for controls, 1.33 ± 0.50 for combination, 1.68 ± 1.02 for sitagliptin, and 1.64 ± 0.81 for spironolactone; with p-value = 0.004). However, the mortality rate was lower in patients who received spironolactone (21.84% control, 13.33% combination, 13.64% sitagliptin, 10.00% spironolactone; p-value = 0.275). Our intervention reduced lung infiltration but not the area of involvement in lung. Conclusion Sitagliptin and spironolactone can potentially improve clinical outcomes of hospitalized COVID-19 patients.

Background. The lack of enough medical evidence about COVID-19 regarding optimal prevention, diagnosis, and treatment contributes negatively to the rapid increase in the number of cases globally. A chest computerized tomography (CT) scan has been introduced as the most sensitive diagnostic method. Therefore, this research aimed to examine and evaluate the chest CT  scan as a screening measure of COVID-19 in trauma patients. Methods. This cross-sectional study was conducted in Rajaee Hospital in Shiraz from February to May 2020. All patients underwent unenhanced CT with a 16-slice CT scanner. The CT scans were evaluated in a blinded manner, and the main CT scan features were described and classified into four groups according to RSNA recommendation. Subsequently, the first two Radiological Society of North America (RSNA) categories with the highest probability of COVID-19 pneumonia (i.e., typical and indeterminate) were merged into the “positive CT scan group” and those with radiologic features with the least probability of COVID-19 pneumonia into “negative CT scan group.” Results. Chest CT scan had a sensitivity of 68%, specificity of 56%, positive predictive value of 34.8%, negative predictive value of 83.7%, and accuracy of 59.3% in detecting COVID-19 among trauma patients. Moreover, for the diagnosis of COVID-19 by CT scan in asymptomatic individuals, a sensitivity of 100%, specificity of 66.7%, and negative predictive value of 100% were obtained ( p value: 0.05). Conclusion. Findings of the study indicated that the CT scan’s sensitivity and specificity is less effective in diagnosing trauma patients with COVID-19 compared with nontraumatic people.

Introduction We aimed to describe shear wave elastography parameters of non-mass lesions of the breast and to assess the measures of diagnostic accuracy of shear wave elastography in the differentiation of non-mass lesions compared with conventional ultrasound, using histopathologic results as the reference standard. Methods This retrospective study included breast ultrasound-detected non-mass lesions with a confirmed pathologic diagnosis during a two-year study period. B-mode ultrasound and shear wave elastography were performed for all lesions before biopsy. Ultrasound features, shear wave elastography parameters (mean elasticity and maximum stiffness color), as well as Breast Imaging-Reporting and Data System categories were recorded for each lesion. Measures of diagnostic accuracy of ultrasound and ultrasound + shear wave elastography were also assessed. Results From a total of 567 breast lesions requiring core-needle biopsy, 49 (8.6%) were considered as non-mass lesions. Based on histopathologic reports, 32 patients (65.3%) had non-high-risk benign lesions, five (10.2%) had high-risk benign lesions, five (10.2%) had ductal carcinoma in situ, and seven (14.3%) had invasive carcinoma. There was no significant difference in patients’ age and palpability between benign and malignant lesions ( p = 0.16 and p = 0.12, respectively). Mean elasticity values and Breast Imaging-Reporting and Data System categories were significantly higher among malignant lesions compared with benign non-mass lesions (both p < 0.001). Furthermore, the addition of shear wave elastography to grayscale ultrasound increased the specificity, positive predictive value, and diagnostic accuracy. Conclusion The complementary use of shear wave elastography with conventional ultrasound might help in the differentiation of non-mass breast lesions and has the potential to decrease the frequency of unnecessary biopsies performed for benign non-mass lesions.

Objectives To investigate the chest CT and clinical characteristics of COVID-19 pneumonia and H1N1 influenza, and explore the radiologist diagnosis differences between COVID-19 and influenza. Materials and methods This cross-sectional study included a total of 43 COVID-19-confirmed patients (24 men and 19 women, 49.90 ± 18.70 years) and 41 influenza-confirmed patients (17 men and 24 women, 61.53 ± 19.50 years). Afterwards, the chest CT findings were recorded and three radiologists recorded their diagnoses of COVID-19 or of H1N1 influenza based on the CT findings. Results The most frequent clinical symptom in patients with COVID-19 and H1N1 pneumonia were dyspnea (96.6%) and cough (62.5%), respectively. The CT findings showed that the COVID-19 group was characterized by GGO (88.1%), while the influenza group had features such as GGO (68.4%) and consolidation (66.7%). Compared with the influenza group, the COVID-19 group was more likely to have GGO (88.1% vs. 68.4%, p = 0.032), subpleural sparing (69.0% vs. 7.7%, p <0.001) and subpleural band (50.0% vs. 20.5%, p = 0.006), but less likely to have pleural effusion (4.8% vs. 33.3%, p = 0.001). The agreement rate between the three radiologists was 65.8%. Conclusion Considering similarities of respiratory infections especially H1N1 and COVID-19, it is essential to introduce some clinical and para clinical modalities to help differentiating them. In our study we extracted some lung CT scan findings from patients suspected to COVID-19 as a newly diagnosed infection comparing with influenza pneumonia patients.