A comparative study of laboratory findings in PCR-positive and PCR-negative COVID-19 hospitalized patients.

INTRODUCTION: Given the many misconceptions in terms of both diagnosis and treatment, SARS-CoV-2 continues to infect and victimize. Notwithstanding molecular testing is the gold standard method of in vitro diagnostic, the often long-waiting time, as well as false-negative results are daunting challenges facing us. In this study, we aimed to report the diagnostic value of laboratory findings in COVID-19 patients, with an extensive focus on the differences between PCR-positive and PCR-negative cases. PATIENTS AND METHODS: We did a retrospective single-centre study on a large cohort of 1546 COVID-19 patients in Tehran, Iran. Based on clinical symptoms, chest CTs were performed for all the patients. Also, molecular testing of swab specimens was also performed for 1450 cases. RESULTS: All the data on laboratory results were retrospectively extracted from medical records. Of the 1546 patients, 1040 (67.5%) were male and 506 (32.5%) were female with the mean age of 55.67. On admission, 31.4% of the whole study population displayed lymphopenia and 38.9% showed neutrophilia. Decreased hemoglobin and mild thrombocytopenia were also found in 40% and 18.6% of cases, respectively. Elevated lactate dehydrogenase in nearly 75% of COVID-19 cases was the most common alteration amongst biochemical parameters which together with increased ESR and CRP could serve as diagnostic markers in SARS-CoV-2 infection. Of the 1450 patients with a PCR result, 439 (28.3%) were PCR-negative and 1011 (65.3%) were PCR-positive. Notably, lymphopenia and increased AST were higher in the PCR-positive group than their negative counterparts. Albeit being in the normal range, a significant decrease in the number of monocytes was also evident in the PCR-positive cases. CONCLUSIONS: As far we are aware, this is the first time that we reported a comprehensive exploration of laboratory characteristics of a large cohort of hospitalized COVID-19 patients from Iran, hoping that these data will cast more light on the diagnostic significance of these parameters.

The feasibility of using acoustic measures for predicting the Total Opacity Scores of chest computed tomography scans in patients with COVID-19.

To study the possibility of using acoustic parameters, i.e., Acoustic Voice Quality Index (AVQI) and Maximum Phonation Time (MPT) for predicting the degree of lung involvement in COVID-19 patients. This cross-sectional case-control study was conducted on the voice samples collected from 163 healthy individuals and 181 patients with COVID-19. Each participant produced a sustained vowel/a/, and a phonetically balanced Persian text containing 36 syllables. AVQI and MPT were measured using Praat scripts. Each patient underwent a non-enhanced chest computed tomographic scan and the Total Opacity score was rated to assess the degree of lung involvement. The results revealed significant differences between patients with COVID-19 and healthy individuals in terms of AVQI and MPT. A significant difference was also observed between male and female participants in AVQI and MPT. The results from the receiver operating characteristic curve analysis and area under the curve indicated that MPT (0.909) had higher diagnostic accuracy than AVQI (0.771). A significant relationship was observed between AVQI and TO scores. In the case of MPT, however, no such relationship was observed. The findings indicated that MPT was a better classifier in differentiating patients from healthy individuals, in comparison with AVQI. The results also showed that AVQI can be used as a predictor of the degree of patients' and recovered individuals' lung involvement. A formula is suggested for calculating the degree of lung involvement using AVQI.

Deep learning framework for prediction of infection severity of COVID-19.

With the onset of the COVID-19 pandemic, quantifying the condition of positively diagnosed patients is of paramount importance. Chest CT scans can be used to measure the severity of a lung infection and the isolate involvement sites in order to increase awareness of a patient's disease progression. In this work, we developed a deep learning framework for lung infection severity prediction. To this end, we collected a dataset of 232 chest CT scans and involved two public datasets with an additional 59 scans for our model's training and used two external test sets with 21 scans for evaluation. On an input chest Computer Tomography (CT) scan, our framework, in parallel, performs a lung lobe segmentation utilizing a pre-trained model and infection segmentation using three distinct trained SE-ResNet18 based U-Net models, one for each of the axial, coronal, and sagittal views. By having the lobe and infection segmentation masks, we calculate the infection severity percentage in each lobe and classify that percentage into 6 categories of infection severity score using a k-nearest neighbors (k-NN) model. The lobe segmentation model achieved a Dice Similarity Score (DSC) in the range of [0.918, 0.981] for different lung lobes and our infection segmentation models gained DSC scores of 0.7254 and 0.7105 on our two test sets, respectfully. Similarly, two resident radiologists were assigned the same infection segmentation tasks, for which they obtained a DSC score of 0.7281 and 0.6693 on the two test sets. At last, performance on infection severity score over the entire test datasets was calculated, for which the framework's resulted in a Mean Absolute Error (MAE) of 0.505 ± 0.029, while the resident radiologists' was 0.571 ± 0.039.

Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models.

Introduction: While the COVID-19 pandemic was waning in most parts of the world, a new wave of COVID-19 Omicron and Delta variants in Central Asia and the Middle East caused a devastating crisis and collapse of health-care systems. As the diagnostic methods for this COVID-19 variant became more complex, health-care centers faced a dramatic increase in patients. Thus, the need for less expensive and faster diagnostic methods led researchers and specialists to work on improving diagnostic testing. Method: Inspired by the COVID-19 diagnosis methods, the latest and most efficient deep learning algorithms in the field of extracting X-ray and CT scan image features were used to identify COVID-19 in the early stages of the disease. Results: We presented a general framework consisting of two models which are developed by convolutional neural network (CNN) using the concept of transfer learning and parameter optimization. The proposed phase of the framework was evaluated on the test dataset and yielded remarkable results and achieved a detection sensitivity, specificity, and accuracy of 0.99, 0.986, and 0.988, for the first phase and 0.997, 0.9976, and 0.997 for the second phase, respectively. In all cases, the whole framework was able to successfully classify COVID-19 and non-COVID-19 cases from CT scans and X-ray images. Conclusion: Since the proposed framework was based on two deep learning models that used two radiology modalities, it was able to significantly assist radiologists in detecting COVID-19 in the early stages. The use of models with this feature can be considered as a powerful and reliable tool, compared to the previous models used in the past pandemics.

Statistical analysis of COVID-19 infection severity in lung lobes from chest CT.

Detection of the COVID 19 virus is possible through the reverse transcription-polymerase chain reaction (RT-PCR) kits and computed tomography (CT) images of the lungs. Diagnosis via CT images provides a faster diagnosis than the RT-PCR method does. In addition to low false-negative rate, CT is also used for prognosis in determining the severity of the disease and the proposed treatment method. In this study, we estimated a probability density function (PDF) to examine the infections caused by the virus. We collected 232 chest CT of suspected patients and had them labeled by two radiologists in 6 classes, including a healthy class and 5 classes of different infection severity. To segment the lung lobes, we used a pre-trained U-Net model with an average Dice similarity coefficient (DSC) greater than 0.96. First, we extracted the PDF to grade the infection of each lobe and selected five specific thresholds as feature vectors. We then assigned this feature vector to a support vector machine (SVM) model and made the final prediction of the infection severity. Using the T-Test statistics, we calculated the p-value at different pixel thresholds and reported the significant differences in the pixel values. In most cases, the p-value was less than 0.05. Our developed model was developed on roughly labeled data without any manual segmentation, which estimated lung infection involvements with the area under the curve (AUC) in the range of [0.64, 0.87]. The introduced model can be used to generate a systematic automated report for individual patients infected by COVID-19.

"Pulmonary target sign" as a diagnostic feature in chest computed tomography of COVID-19.

BACKGROUND: In chest computed tomography (CT) scan, bilateral peripheral multifocal ground-glass opacities, linear opacities, reversed halo sign, and crazy-paving pattern are suggestive for coronavirus disease 2019 (COVID-19) in clinically suspicious cases, but they are not specific for the diagnosis, as other viral pneumonias, like influenza and some viral pneumonia may show similar imaging findings. AIM: To find a specific imaging feature of the disease would be a welcome guide in diagnosis and management of challenging cases. METHODS: Chest CT imaging findings of 650 patients admitted to a university Hospital in Tehran, Iran between January 2020 and July 2020 with confirmed COVID-19 infection by RT-PCR were reviewed by two expert radiologists. In addition to common non-specific imaging findings of COVID-19 pneumonia, radiologic characteristics of "pulmonary target sign" (PTS) were assessed. PTS is defined as a circular appearance of non-involved pulmonary parenchyma, which encompass a central hyperdense dot surrounded by ground-glass or alveolar opacities. RESULTS: PTS were presented in 32 cases (frequency 4.9%). The location of the lesions in 31 of the 32 cases (96.8%) was peripheral, while 4 of the 31 cases had lesions both peripherally and centrally. In 25 cases, the lesions were located near the pleural surface and considered pleural based and half of the lesions (at least one lesion) were in the lower segments and lobes of the lungs. 22 cases had multiple lesions with a > 68% frequency. More than 87% of cases had an adjacent bronchovascular bundle. Ground-glass opacities were detectable adjacent or close to the lesions in 30 cases (93%) and only in 7 cases (21%) was consolidation adjacent to the lesions. CONCLUSION: Although it is not frequent in COVID-19, familiarity with this feature may help radiologists and physicians distinguish the disease from other viral and non-infectious pneumonias in challenging cases.

Simultaneous Giant cavity pulmonary lesion and pneumothorax following COVID-19 pneumonia.

Cavitary lung formation with spontaneous pneumothorax has been rarely reported as a complication of COVID-19 pneumonia. We report a rare case of a 38 years-old male patient affected by COVID-19 pneumonia, exceptionally complicated by a simultaneous giant cavity in the right upper lung and a small right pneumothorax in the right hemithorax. Whilst pneumothorax emphysema, giant bullae and pneumothorax with alveolar rupture are known to potentially develop in COVID-19 patients as a result of high-flow O2 support, the exact origin of the giant lung cavitation in our patient could be not confirmed. Cavitary lesions - featured by high mortality rate - are reportedly associated with lung infarctions and can be the aftermaths of pulmonary embolism, a rather common sequela of COVID-19 pneumonia. Radiological imaging is critical to support clinical decision making in the management of COVID-19 pneumonia, since not only it can visualize and stage the disease, but it can also detect and monitor the eventual onset of complications over time, even following patient discharge from hospital.

Typical Covid-19 case with primary pneumomediastinum in a 37 year old male.

We report the case of a 37-year-old man who was admitted to Baqiyatallah hospital in Tehran (Iran) for retrosternal pain, fever, fatigue, dyspnoea and severe non-productive cough. The patient was subsequently confirmed as positive for COVID-19 at real-time polymerase chain reaction (RT-PCR) test. Chest computed tomography (CT) revealed also the presence of pneumomediastinum. This case highlights the importance of chest CT imaging for COVID-19 pneumonia to detect co-existing conditions as pneumomediastinum.

Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study.

BACKGROUND: Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide. OBJECTIVE: Machine vision-based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)-based algorithm. METHODS: NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used. RESULTS: After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. CONCLUSIONS: The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.

A Challenging Diagnosis of Two Ruptured and Intact Pulmonary Echinococcal Cysts in a 54-Year-Old Woman: A Case Report.

PURPOSE: Echinococcosis is one of the most important parasitic zoonotic diseases around the world. Echinococcus granulosus is the most widespread species of the genus Echinococcus that can develop cysts in different parts of the body. We tried to present a case of pulmonary cystic echinococcosis. METHODS: Here, we report a rare case of two ruptured and intact cysts in a 54-year-old woman with weakness, lethargy, body pain, stomachache, dizziness, and vision problems. RESULTS: According to the patient's manifestations and imaging findings, besides the COVID-19 pandemic, she was suspected of having COVID-19 and tuberculosis. However, when the aspirated sample was stained, hooklets of E. granulosus were observed. Surgical removal and chemotherapy were used for treatment. CONCLUSION: Treatment of pulmonary cystic echinococcosis is based on surgery, but, along with it, the chemotherapy makes a better prognosis.

Identification, Monitoring, and Prediction of Disease Severity in Patients with COVID-19 Pneumonia Based on Chest Computed Tomography Scans: A Retrospective Study.

Background and Aims Non-contrast chest computed tomography (CT) scans can accurately evaluate the type and extent of lung lesions. The aim of this study was to investigate the chest CT features associated with critical and non-critical patients with coronavirus disease 2019 (COVID-19). Methods A total of 1078 patients with COVID-19 pneumonia who underwent chest CT scans, including 169 critical cases and 909 non-critical cases, were enrolled in this retrospective study. The scans of all participants were reviewed and compared in two groups of study. In addition, the risk factors associated with disease in critical and non-critical patients were analyzed. Results Chest CT scans showed bilateral and multifocal involvement in most (86.4%) of the participants, with 97.6 and 84.3% reported in critical and non-critical patients, respectively. The incidences of pure consolidation (p = 0.019), mixed ground-glass opacities (GGOs) and consolidation (p < 0.001), pleural effusion (p < 0.001), and intralesional traction bronchiectasis (p = 0.007) were significantly higher in critical compared to non-critical patients. However, non-critical patients showed higher incidence of pure GGOs than the critical patients (p < 0.001). Finally, the total opacity scores of the critical patients were significantly higher than those of non-critical patients (13.71 ± 6.26 versus 4.86 ± 3.52, p < 0.001), with an area under the curve of 0.91 (0.88-0.94) for COVID-19 detection. Conclusions Our results revealed that the chest CT examination was an effective means of detecting pulmonary parenchymal abnormalities in the natural course of COVID-19. It can distinguish the critical patients from the non-critical patients (AUC = 0.91), which is helpful for the judgment of clinical condition and has important clinical value for the diagnosis and follow-up of COVID-19 pneumonia.

Ozone therapy for the treatment of COVID-19 pneumonia: A scoping review.

Severe forms of COVID-19 can evolve into pneumonia, featured by acute respiratory failure due to acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). In viral diseases, the replication of viruses is seemingly stimulated by an imbalance between pro-oxidant and antioxidant activity as well as by the deprivation of antioxidant mechanisms. In COVID-19 pneumonia, oxidative stress also appears to be highly detrimental to lung tissues. Although inhaling ozone (O3) gas has been shown to be toxic to the lungs, recent evidence suggests that its administration via appropriate routes and at small doses can paradoxically induce an adaptive reaction capable of decreasing the endogenous oxidative stress. Ozone therapy is recommended to counter the disruptive effects of severe COVID-19 on lung tissues, especially if administered in early stages of the disease, thereby preventing the progression to ARDS.

From Radiological Manifestations to Pulmonary Pathogenesis of COVID-19: A Bench to Bedside Review.

In this review, we aim to assess previous radiologic studies in COVID-19 and suggest a pulmonary pathogenesis based on radiologic findings. Although radiologic features are not specific and there is heterogeneity in symptoms and radiologic and clinical manifestation, we suggest that the dominant pattern of computed tomography is consistent with limited pneumonia, followed by interstitial pneumonitis and organizing pneumonia.

COVID-19 in rheumatoid arthritis cases: an Iranian referral center experience.

Coronavirus infections, known as COVID-19, can induce a fatal respiratory system infection and also affect other organs, such as the kidney and heart. The mortality rate has been estimated between 1 and 5% in previous reports; however, the mortality and morbidity can be higher in patients with the immune-deficiency condition. Rheumatoid arthritis (RA) is one of the most rheumatoid disorders, and it is important to report their clinical and paraclinical data when affected with COVID-19. Evidence about their laboratory and radiologic findings is limited. In this case series, 10 cases of chronic and approved rheumatoid arthritis (RA) affected by COVID-19 are presented. Only 40% had dry cough, but myalgia and weakness as the general first presentation of infections was reported in most cases (80%). Gastrointestinal symptoms, including nausea/vomiting, diarrhea, anorexia, and abdominal pain, were reported in 50% of individuals. In blood cell count, 30% of cases had thrombocytopenia, and ESR in all cases was positive. Abnormal CRP and elevated LDH were seen in 90% of cases. In HRCT assessment, all cases had an abnormal parenchymal pattern, and 90% of cases presented the usual pattern of COVID-19 (bilateral multifocal GGO/consolidation). Although it is a limited report, these findings are helpful for comparison of clinical and paraclinical cases in RA cases with normal cases.

A patient affected by critical COVID-19 pneumonia, successfully treated with convalescent plasma.

We present a critically ill patient affected by COVID-19, whose chest computed tomography (CT) scan featured lung consolidations and severe patchy ground-glass opacitie. On day 3 since hospital admission the patient was placed on convalescent plasma treatment. A combined treatment with supportive care, hemoperfusion and convalescent plasma successfully managed to save the patient's life. Convalescent plasma probably contributed to heal this patient and should always be considered in the management of critically ill COVID-19 cases.