Diagnostic value and key features of computed tomography in Coronavirus Disease 2019

Low-Dose CT Image Super-resolution Network with Noise Inhibition Based on Feedback Feature Distillation Mechanism

Low-dose computed tomography (LDCT) has been widely used in medical diagnosis. In practice, doctors often zoom in on LDCT slices for clearer lesions and issues, while, a simple zooming operation fails to suppress low-dose artifacts, leading to distorted details. Therefore, numerous LDCT super-resolution (SR) methods have been proposed to promote the quality of zooming without the increase of the dose in CT scanning. However, there are still some drawbacks that need to be addressed in existing methods. First, the region of interest (ROI) is not emphasized due to the lack of guidance in the reconstruction process. Second, the convolutional blocks extracting fix-resolution features fail to concentrate on the essential multi-scale features. Third, a single SR head cannot suppress the residual artifacts. To address these issues, we propose an LDCT CT joint SR and denoising reconstruction network. Our proposed network consists of global dual-guidance attention fusion modules (GDAFMs) and multi-scale anastomosis blocks (MABs). The GDAFM directs the network to focus on ROI by fusing the extra mask guidance and average CT image guidance, while the MAB introduces hierarchical features through anastomosis connections to leverage multi-scale features and promote the feature representation ability. To suppress radial residual artifacts, we optimize our network using the feedback feature distillation mechanism (FFDM) which shares the backbone to learn features corresponding to the denoising task. We apply the proposed method to the 3D-IRCADB and PANCREAS datasets to evaluate its ability on LDCT image SR reconstruction. The experimental results compared with state-of-the-art methods illustrate the superiority of our approach with respect to peak signal-to-noise (PSNR), structural similarity (SSIM), and qualitative observations. Our proposed LDCT joint SR and denoising reconstruction network has been extensively evaluated through ablation, quantitative, and qualitative experiments. The results demonstrate that our method can recover noise-free and detail-sharp images, resulting in better reconstruction results. Code is available at https://github.com/neu-szy/ldct_sr_dn_w_ffdm .

Efficacy and safety of inactivated SARS-CoV-2 vaccination in COVID-19-associated pneumonia among patients with rheumatic and musculoskeletal diseases: A real-world retrospective observational study

OBJECTIVES

To identify the effectiveness and safety of inactivated SARS-CoV-2 vaccines in rheumatic and musculoskeletal diseases (RMDs) patients.

METHODS

RMD patients with COVID-19 in Jiangsu Province were polled between December 8, 2022, and February 1, 2023. Information on demographics, disease characteristics, antirheumatic drug use, vaccination status and survival state were collected. COVID-19-associated pneumonia was the primary outcome. The effect of COVID-19 immunization on RMD patients was assessed using multivariate logistic regression, and the adverse events (AEs) following vaccination were evaluated.

RESULTS

Among 592 RMD patients with COVID-19, 276 (46.6%) individuals experienced COVID-19-associated pneumonia, and 290 (49.0%) patients were injected with inactivated vaccines. In multivariate logistic regression analysis, vaccines reduced the incidence of COVID-19-associated pneumonia, and receiving booster vaccine was an independent protective factor for COVID-19-associated pneumonia in RMD patients (OR 0.64, 95% CI 0.41-0.98, p = .034). In particular, inactivated vaccines have a protective impact on RMD patients with a high risk of developing pneumonia, including those aged 45 years and older (OR 0.53, 95% CI 0.34-0.83), and who have lung involvement (OR 0.43, 95% CI 0.23-0.82). The total AEs rate of vaccines was 13.9% (40/290), only 11 (3.8%) experienced the recurrence or deterioration of RMDs, and no serious AEs occurred.

CONCLUSION

Inactivated COVID-19 vaccines were safe and effective in reducing the risk of COVID-19-associated pneumonia of RMD patients in China.

Detecting Respiratory Viruses Using a Portable NIR Spectrometer-A Preliminary Exploration with a Data Driven Approach

Respiratory viruses' detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings. One obstacle to its effective application lies in its common limitations, which include relatively low specificity and general quality. Characteristically, the spectra curves show an interweaving feature for the virus-present and virus-absent samples. This then provokes the idea of using machine learning methods to overcome the difficulty. While a subsequent obstacle coincides with the fact that a direct deployment of the machine learning approaches leads to inadequate accuracy of the modelling results. This paper presents a data-driven study on the detection of two common respiratory viruses, the respiratory syncytial virus (RSV) and the Sendai virus (SEV), using a portable NIR spectrometer supported by a machine learning solution enhanced by an algorithm of variable selection via the Variable Importance in Projection (VIP) scores and its Quantile value, along with variable truncation processing, to overcome the obstacles to a certain extent. We conducted extensive experiments with the aid of the specifically developed algorithm of variable selection, using a total of four datasets, achieving classification accuracy of: (1) 0.88, 0.94, and 0.93 for RSV, SEV, and RSV + SEV, respectively, averaged over multiple runs, for the neural network modelling of taking in turn 3 sessions of data for training and the remaining one session of an 'unknown' dataset for testing. (2) the average accuracy of 0.94 (RSV), 0.97 (SEV), and 0.97 (RSV + SEV) for model validation and 0.90 (RSV), 0.93 (SEV), and 0.91 (RSV + SEV) for model testing, using two of the datasets for model training, one for model validation and the other for model testing. These results demonstrate the feasibility of using portable NIR spectroscopy coupled with machine learning to detect respiratory viruses with good accuracy, and the approach could be a viable solution for population screening.

Comparison of Ultrasound and CT Imaging for the Diagnosis of Coronavirus Disease and Influenza A Pneumonia

OBJECTIVE

The outbreak of coronavirus disease (COVID-19) coincided with the season of influenza A pneumonia, a common respiratory infectious disease. Therefore, this study compared ultrasonography and computed tomography (CT) for the diagnosis of the two diseases.

METHODS

Patients with COVID-19 or influenza A infection hospitalized at our hospital were included. The patients were examined by ultrasonography every day. The CT examination results within 1 day before and after the day of the highest ultrasonography score were selected as the controls. The similarities and differences between the ultrasonography and CT results in the two groups were compared.

RESULTS

There was no difference between the ultrasonography and CT scores (P = .307) for COVID-19, while there was a difference between ultrasonography and CT scores for influenza A pneumonia (P = .024). The ultrasonography score for COVID-19 was higher than that for influenza A pneumonia (P = .000), but there was no difference between the CT scores (P = .830). For both diseases, there was no difference in ultrasonography and CT scores between the left and right lungs; there were differences between the CT scores of the upper and middle lobes, as well as between the upper and lower lobes of the lungs; however, there was no difference between the lower and middle lobes of the lungs.

CONCLUSION

Ultrasonography is equivalent to the gold standard CT for diagnosing and monitoring the progression of COVID-19. Because of its convenience, ultrasonography has important application value. Furthermore, the diagnostic value of ultrasonography for COVID-19 is higher than that for influenza A pneumonia.

Comparative Analysis of 198 SARS-CoV-2 Genomes from Iran and West Asia, February 2020 to December 2021

Background & Objective

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in a worldwide pandemic. The first case of COVID-19 was reported from Wuhan in the Hubei Province of China in December 2019; however, the disease's origin is still mysterious. Whole-genome sequence analysis is essential for monitoring the spread of infectious diseases as well as studying the pathogenesis and evolution of viruses. In this study, analysis of 198 fully sequenced genomes from Iran and West Asia was carried out to study mutations, phylogeny, amino acid changes, clades, and lineages of these genomes as well as comparison of these sequences with those of reference Wuhan genome of NC_045512.2.

Methods

In total, 198 completely sequenced genome data from Iran and West Asia were collected from GenBank. Mutation detection was carried out using a trial version of CLC Genomics Workbench v.21.0 (QIAGEN, Germany). Online tools such as GISAID Mutations App and Pangolin were used for further analysis of the results.

Results

In this study, several unique mutation sites were identified in the Iranian genomes (n = 8); positions 1397 G>A and 29742 G>T were the most frequent changes in more than 85% of the Iranian genomes. Mutation rate, mutation per sequence, and transition versus transversion for the Iranian genomes included 4.73, 14.14, and 1.6, respectively. Generally, C>T alteration was the most common substitution in all the sequences.

Conclusion

The ORF1ab, N, and S were the genes with the most changes. The current data can help researchers predict future epidemics and establish better strategies to control viral pandemics.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study

OBJECTIVES

Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence.

METHODS

We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence.

RESULTS

The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores.

CONCLUSION

This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence.

KEY POINTS

• AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Diagnostic and therapeutic potential of protease inhibition

Proteases are enzymes that hydrolyze peptide bonds in proteins and peptides; thus, they control virtually all biological processes. Our understanding of protease function has advanced considerably from nonselective digestive enzymes to highly specialized molecular scissors that orchestrate complex signaling networks through a limited proteolysis. The catalytic activity of proteases is tightly regulated at several levels, ranging from gene expression through trafficking and maturation to posttranslational modifications. However, when this delicate balance is disturbed, many diseases develop, including cancer, inflammatory disorders, diabetes, and neurodegenerative diseases. This new understanding of the role of proteases in pathologic physiology indicates that these enzymes represent excellent molecular targets for the development of therapeutic inhibitors, as well as for the design of chemical probes to visualize their redundant activity. Recently, numerous platform technologies have been developed to identify and optimize protease substrates and inhibitors, which were further used as lead structures for the development of chemical probes and therapeutic drugs. Due to this considerable success, the clinical potential of proteases in therapeutics and diagnostics is rapidly growing and is still not completely explored. Therefore, small molecules that can selectively target aberrant protease activity are emerging in diseases cells. In this review, we describe modern trends in the design of protease drugs as well as small molecule activity-based probes to visualize selected proteases in clinical settings.

Challenges in the Differential Diagnosis of COVID-19 Pneumonia: A Pictorial Review

COVID-19 pneumonia represents a maximum medical challenge due to the virus's high contagiousness, morbidity, and mortality and the still limited possibilities of the health systems. The literature has primarily focused on the diagnosis, clinical-radiological aspects of COVID-19 pneumonia, and the most common possible differential diagnoses. Still, few studies have investigated the rare differential diagnoses of COVID-19 pneumonia or its overlap with other pre-existing lung pathologies. This article presents the main radiological features of COVID-19 pneumonia and the most common alternative diagnoses to establish the vital radiological criteria for a differential diagnosis between COVID-19 pneumonia and other lung pathologies with similar imaging appearance. The differential diagnosis of COVID-19 pneumonia is challenging because there may be standard radiologic features such as ground-glass opacities, crazy paving patterns, and consolidations. A multidisciplinary approach is crucial to define a correct final diagnosis, as an overlap of COVID-19 pneumonia with pre-existing lung diseases is often possible and suggests possible differential diagnoses. An optimal evaluation of HRTC can help limit the clinical evolution of the disease, promote therapy for patients and ensure an efficient allocation of human and economic resources.

CT image super-resolution reconstruction based on global hybrid attention

Computer tomography (CT) has played an essential role in the field of medical diagnosis, but the blurry edges and unclear textures in traditional CT images usually interfere the subsequent judgement from radiologists or clinicians. Deep learning based image super-resolution methods have been applied for CT image restoration recently. However, different levels of information of CT image details are mixed and difficult to be mapped from deep features by traditional convolution operations. Moreover, features representing regions of interest (ROIs) in CT images are treated equally as those for background, resulting in low concentration of meaningful features and high redundancy of computation. To tackle these issues, a CT image super-resolution network is proposed based on hybrid attention mechanism and global feature fusion, which consists of the following three parts: 1) stacked Swin Transformer blocks are used as the backbone to extract initial features from the degraded CT image; 2) a multi-branch hierarchical self-attention module (MHSM) is proposed to adaptively map multi-level features representing different levels of image information from the initial features and establish the relationship between these features through a self-attention mechanism, where three branches apply different strategies of integrating convolution, down-sampling and up-sampling operations according to three different scale factors; 3) a multidimensional local topological feature enhancement module (MLTEM) is proposed and plugged into the end of the backbone to refine features in the channel and spatial dimension simultaneously, so that the features representing ROIs could be enhanced while meaningless ones eliminated. Experimental results demonstrate that our method outperform the state-of-the-art super-resolution methods on restoring CT images with respect to peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indices.

High sensitivity detection of SARS-CoV-2 by an optofluidic hollow eccentric core fiber

Since the outbreak of coronavirus disease 2019 (COVID-19), efficient real-time monitoring has become one of the challenges faced in SARS-CoV-2 virus detection. A compact all-fiber Mach-Zehnder interferometer optofluidic sensor based on a hollow eccentric core fiber (HECF) for the detection and real-time monitoring of SARS-CoV-2 spike glycoprotein (SARS-CoV-2 S2) is proposed, analyzed and demonstrated. The sensor is comprised of fusion splicing single mode fiber (SMF), hollow core fiber (HCF) and HECF. After the incident light passes through the HCF from the SMF, it uniformly enters the air hole and the suspended micrometer-scale fiber core of the HECF to form a compact all-fiber Mach-Zehnder interferometer (MZI). HECF is side polished to remove part of the cladding that the suspended fiber core can contact the external environment. Subsequently, the mouse anti SARS-CoV-2 S2 antibody is fixed on the surface of the suspended-core for the sake of achieving high sensitivity and specific sensing of SARS-CoV-2 S2. The limit of detection (LOD) of the sensor is 26.8 pM. The proposed sensor has high sensitivity, satisfactory selectivity, and can be fabricated at low cost making it highly suitable for point-of-care testing and high-throughput detection of early stage of COVID-19 infection.

The Role of Imaging in the Detection of Non-COVID-19 Pathologies during the Massive Screening of the First Pandemic Wave

During the COVID-19 pandemic induced by the SARS-CoV-2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non-COVID-19 chest pathologies discovered de novo. The fortuitous discovery of de novo non-COVID-19 lesions was generally not detected by the automated systems for COVID-19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT by the radiologist. The objective is to use the study of the occurrence of non-COVID-19-related chest abnormalities (known and unknown) in a large cohort of patients having suffered from confirmed COVID-19 infection and statistically correlate the clinical data and the occurrence of these abnormalities in order to assess the potential of increased early detection of lesions/alterations. This study was performed on a group of 362 COVID-19-positive patients who were prescribed a CT scan in order to diagnose and predict COVID-19-associated lung disease. Statistical analysis using mean, standard deviation (SD) or median and interquartile range (IQR), logistic regression models and linear regression models were used for data analysis. Results were considered significant at the 5% critical level (p < 0.05). These de novo non-COVID-19 thoracic lesions detected on chest CT showed a significant prevalence in cardiovascular pathologies, with calcifying atheromatous anomalies approaching nearly 35.4% in patients over 65 years of age. The detection of non-COVID-19 pathologies was mostly already known, except for suspicious nodule, thyroid goiter and the ascending thoracic aortic aneurysm. The presence of vertebral compression or signs of pulmonary fibrosis has shown a significant impact on inpatient length of stay. The characteristics of the patients in this sample, both from a demographic and a tomodensitometric point of view on non-COVID-19 pathologies, influenced the length of hospital stay as well as the risk of intra-hospital death. This retrospective study showed that the potential importance of the detection of these non-COVID-19 lesions by the radiologist was essential in the management and the intra-hospital course of the patients.

Progression of severity in coronavirus disease 2019 patients before treatment and a self-assessment scale to predict disease severity

OBJECTIVES

This study aims to further investigate the association of COVID-19 disease severity with numerous patient characteristics, and to develop a convenient severity prediction scale for use in self-assessment at home or in preliminary screening in community healthcare settings.

SETTING AND PARTICIPANTS

Data from 45,450 patients infected with COVID-19 from January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System in Wuhan, China.

PRIMARY AND SECONDARY OUTCOME MEASURES

We categorized COVID-19 disease severity, based on The Chinese Diagnosis and Treatment Protocol for COVID-19, as "nonsevere" (which grouped asymptomatic, mild, and ordinary disease) versus "severe" (grouping severe and critical illness).

RESULTS

Twelve scale items-age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage-were identified and showed good predictive ability (area under the curve = 0·72). After excluding the community healthcare laboratory parameters, the remaining model (the final self-assessment scale) showed similar area under the curve (= 0·71).

CONCLUSIONS

Our COVID-19 severity self-assessment scale can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings. Our study constructed a COVID-19 severity self-assessment scale that can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance.

Sensitive Detection of SARS-CoV-2 Using a Novel Plasmonic Fiber Optic Biosensor Design

The coronavirus (COVID-19) pandemic has put the entire world at risk and caused an economic downturn in most countries. This work provided theoretical insight into a novel fiber optic-based plasmonic biosensor that can be used for sensitive detection of SARS-CoV-2. The aim was always to achieve reliable, sensitive, and reproducible detection. The proposed configuration is based on Ag-Au alloy nanoparticle films covered with a layer of graphene which promotes the molecular adsorption and a thiol-tethered DNA layer as a ligand. Here, the combination of two recent approaches in a single configuration is very promising and can only lead to considerable improvement. We have theoretically analyzed the sensor performance in terms of sensitivity and resolution. To highlight the importance of the new configuration, a comparison was made with two other sensors. One is based on gold nanoparticles incorporated into a host medium; the other is composed of a bimetallic Ag-Au layer in the massive state. The numerical results obtained have been validated and show that the proposed configuration offers better sensitivity (7100 nm\RIU) and good resolution (figure of merit; FOM = 38.88RIU - 1 and signal-to-noise ratio; SNR = 0.388). In addition, a parametric study was performed such as the graphene layers' number and the size of the nanoparticles.

Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules

Background

The accuracy of CT and tumour markers in screening lung cancer needs to be improved. Computer-aided diagnosis has been reported to effectively improve the diagnostic accuracy of imaging data, and recent studies have shown that circulating genetically abnormal cell (CAC) has the potential to become a novel marker of lung cancer. The purpose of this research is explore new ways of lung cancer screening.

Methods

From May 2020 to April 2021, patients with pulmonary nodules who had received CAC examination within one week before surgery or biopsy at First Affiliated Hospital of Zhengzhou University were enrolled. CAC counts, CT scan images, serum tumour marker (CEA, CYFRA21–1, NSE) levels and demographic characteristics of the patients were collected for analysis. CT were uploaded to the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) to assess the malignancy probability of nodules. We compared diagnosis based on PNAIDS, CAC, Mayo Clinic Model, tumour markers alone and their combination. The combination models were built through logistic regression, and was compared through the area under (AUC) the ROC curve.

Results

A total of 93 of 111 patients were included. The AUC of PNAIDS was 0.696, which increased to 0.847 when combined with CAC. The sensitivity (SE), specificity (SP), and positive (PPV) and negative (NPV) predictive values of the combined model were 61.0%, 94.1%, 94.7% and 58.2%, respectively. In addition, we evaluated the diagnostic value of CAC, which showed an AUC of 0.779, an SE of 76.3%, an SP of 64.7%, a PPV of 78.9%, and an NPV of 61.1%, higher than those of any single serum tumour marker and Mayo Clinic Model. The combination of PNAIDS and CAC exhibited significantly higher AUC values than the PNAIDS (P = 0.009) or the CAC (P = 0.047) indicator alone. However, including additional tumour markers did not significantly alter the performance of CAC and PNAIDS.

Conclusions

CAC had a higher diagnostic value than traditional tumour markers in early-stage lung cancer and a supportive value for PNAIDS in the diagnosis of cancer based on lung nodules. The results of this study offer a new mode of screening for early-stage lung cancer using lung nodules.

COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization

The coronavirus outbreak 2019, called COVID-19, which originated in Wuhan, negatively affected the lives of millions of people and many people died from this infection. To prevent the spread of the disease, which is still in effect, various restriction decisions have been taken all over the world. In addition, the number of COVID-19 tests has been increased to quarantine infected people. However, due to the problems encountered in the supply of RT-PCR tests and the ease of obtaining Computed Tomography and X-ray images, imaging-based methods have become very popular in the diagnosis of COVID-19. Therefore, studies using these images to classify COVID-19 have increased. This paper presents a classification method for computed tomography chest images in the COVID-19 Radiography Database using features extracted by popular Convolutional Neural Networks (CNN) models (AlexNet, ResNet18, ResNet50, Inceptionv3, Densenet201, Inceptionresnetv2, MobileNetv2, GoogleNet). The determination of hyperparameters of Machine Learning (ML) algorithms by Bayesian optimization, and ANN-based image segmentation are the two main contributions in this study. First of all, lung segmentation is performed automatically from the raw image with Artificial Neural Networks (ANNs). To ensure data diversity, data augmentation is applied to the COVID-19 classes, which are fewer than the other two classes. Then these images are applied as input to five different CNN models. The features extracted from each CNN model are given as input to four different ML algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), and Decision Tree (DT) for classification. To achieve the most successful classification accuracy, the hyperparameters of each ML algorithm are determined using Bayesian optimization. With the classification made using these hyperparameters, the highest success is obtained as 96.29% with the DenseNet201 model and SVM algorithm. The Sensitivity, Precision, Specificity, MCC, and F1-Score metric values for this structure are 0.9642, 0.9642, 0.9812, 0.9641 and 0.9453, respectively. These results showed that ML methods with the most optimum hyperparameters can produce successful results.

The association of clinical features and laboratory findings of COVID-19 infection with computed pneumonia volume

Coronavirus disease 2019 (COVID-19) was first detected in China in December 2019, and declared as a pandemic by the World Health Organization (WHO) on March 11, 2020.To study the clinical features of patients with COVID-19, we analyzed the correlation between some inflammation-related indicators in patients' serum and the severity of the disease, especially PV (pneumonia volume under CT scan) and pneumonia volume ratio (PVR).Sixty-six COVID-19 patients in Huai'an, China were selected as the research subjects. We collected the clinical data, including general characteristics, clinical symptoms, serum test results and CT performance, explored the relationship between inflammation-related indexes, oxygenation index, PV, PVR, while indicators of mild to moderate patients and severe patients were compared.The most prominent manifestations of COVID-19 patients were fever (47, 71.2%); cough (41, 62.1%), with or without respiratory and other systemic symptoms; There was no difference in gender (P = .567) and age (P = .865) between mild to moderate and severe groups. High sensitivity C-reactive protein (hs-CRP), erythrocyte sedimentation rate (ESR), and interleukin-6 (IL-6) of overall patients were higher than the normal range (P < .001, respectively). hs-CRP was negatively correlated with oxygenation index (OI) (r = -0.55), whereas positively correlated with PV, PVR and ESR (r = 0.89; r = 0.87; r = 0.47, respectively); ESR was negatively correlated with OI (r = -0.45), meanwhile it was positively correlated with PV and PVR (r = 0.44; r = 0.46, respectively). OI was negatively correlated with PV and PVR (r = -0.6, respectively). PV had a clear correlation with PVR (r = 1). Severe patients' hs-CRP, PV, PVR were higher than mild to moderate group (P = .006; P = .001; P < .001, respectively), but OI was lower (P < .001).The clinical features of COVID-19 were similar to general viral pneumonia. hs-CRP, ESR showed a certain correlation with the PV and PVR, which might play a certain role in assessing the severity of COVID-19.

Dentistry during COVID-19: patients' knowledge and satisfaction toward health protocols COVID-19 during dental treatment

BACKGROUND

Coronavirus disease 2019 (COVID-19) as an infectious disease primarily spreading through droplet infection in dental treatment. Patient satisfaction is an indicator of healthcare quality service. Quality of healthcare service and patient satisfaction has been affected by the COVID‑19 pandemic. This study aims to assess the knowledge and satisfaction toward health protocols COVID-19 during dental treatment among dental patients.

METHODS

An institutional-based cross-sectional study was conducted on 270 dental patients using a self‑designed questionnaire consisting of knowledge and satisfaction about health protocols COVID-19 during dental treatment through a random sampling technique. Data were imported to SPSS version 21 for analysis. Descriptive and analytical statistics were used to identify the factors associated with their knowledge and satisfaction. A p value < 0.05 was considered statistical significance.

RESULTS

Totally, 270 dental patients with mean age of 37.6 ± 6.7 years participated in the study. The mean knowledge score was 36.7 ± 3.5, as considerable number of participants were unaware about the risk associated with dental treatment as well as restrictions imposed on dental procedures. About 18% of participants experienced one or other form of dental complaints during the lockdown period. The overall level of patient satisfaction was 44.6%.

CONCLUSION

It can be concluded that, public knowledge is to be improved about risk of virus transmission that can be related with dental treatment and also people should be encouraged to use virtual facilities, such as teledentistry, so that no dental emergencies is left untreated during the pandemic time. In addition, the level of satisfaction was in a medium level for dental patients in the study area. Specifically, we deduced from the results that social/physical distancing measures are one of the mechanisms to decrease the fear of exposure to the COVID-19.

High-resolution chest computed tomography findings of coronavirus disease 2019 (COVID-19) - A retrospective single center study of 152 patients

Introduction:

Coronavirus disease 2019 (COVID-19) pandemic has engulfed the world, within a short span of time crippling many health systems. The disease in its ever-evolving course is exhibiting a myriad of symptoms and imaging manifestations. This retrospective study was conducted to generate evidence from the chest computed tomography (CT) findings of patients with COVID-19 pneumonia to aid in the diagnosis and disease management.

Methods:

This retrospective study included all patients with reverse transcriptase polymerase chain reaction confirmed COVID-19 disease who underwent chest CT between 1^st June to 31^st December 2020 at a tertiary care institute of North India. Anonymized data of 152 COVID-19 positive patients was used for the evaluation of the clinical profile and imaging findings.

Results:

The common presenting clinical symptoms were fever, cough, myalgia and sore throat. The most frequent CT imaging feature consisted of ground-glass opacities (GGOs), consolidation and crazy paving distributed bilaterally, peripherally in subpleural location with a predilection for the posterior parts of lungs. Reverse halo sign was observed in 12 patients and halo sign in 3 patients. Dilated pulmonary vessels with mild bronchiolectasis were observed in the involved lung parenchyma. Less common findings included pleural effusion, mediastinal lymphadenopathy, and pericardial effusion. The mean CT severity score gradually increased with increasing age.

Conclusion:

The predominant imaging finding of COVID-19 pneumonia was peripheral GGO's distributed bilaterally in peripheral subpleural region and having predilection for the posterior parts of the lungs which gradually evolve into organizing pneumonia patterns. Although COVID-19 shares imaging findings with other viral pneumonias, however in the context of the current pandemic, we must keep COVID-19 a differential diagnosis, in all patients with fever and respiratory symptoms.

Early CT features of COVID-19 pneumonia, association with patients’ age and duration of presenting complaint

Background

Coronavirus disease (COVID-19) is a respiratory syndrome with a variable degree of severity. Imaging is a vital component of disease monitoring and follow-up in coronavirus pulmonary syndromes. The study of temporal changes of CT findings of COVID-19 pneumonia can help in better understanding of disease pathogenesis and prediction of disease prognosis. In this study, we aim to determine the typical and atypical CT imaging features of COVID-19 and discuss the association of typical CT imaging features with the duration of the presenting complaint and patients’ age.

Results

The lesions showed unilateral distribution in 20% of cases and bilateral distribution in 80% of cases. The lesions involved the lower lung lobes in 30% of cases and showed diffuse involvement in 58.2% of cases. The lesions showed peripheral distribution in 74.5% of cases. The most common pattern was multifocal ground glass opacity found in 72.7% of cases. Atypical features like cavitation and pleural effusion can occur early in the disease course. There was significant association between increased number of the lesions, bilaterality, diffuse pattern of lung involvement and older age group (≥ 50 years old) and increased duration of presenting complaint (≥ 4 days). There was significant association between crazy-paving pattern and increased duration of presenting complaint. No significant association could be detected between any CT pattern and increased patient age.

Conclusion

The most common CT feature of COVID-19 was multifocal ground glass opacity. Atypical features like cavitation and pleural effusion can occur early in the course of the disease. Our cases showed more extensive lesions with bilateral and diffuse patterns of distribution in the older age group and with increased duration of presenting complaint. There was a significant association between crazy-paving pattern and increased duration of presenting complaint. No significant association could be detected between any CT pattern and increased patient age.

Diagnostic imaging in COVID-19 pneumonia: a literature review

In December 2019 in Wuhan (China), a bat-origin coronavirus (2019-nCoV), also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified, and the World Health Organization named the related disease COVID-19. Its most severe manifestations are pneumonia, systemic and pulmonary thromboembolism, acute respiratory distress syndrome (ARDS), and respiratory failure. A swab test is considered the gold standard for the diagnosis of COVID-19 despite the high number of false negatives. Radiologists play a crucial role in the rapid identification and early diagnosis of pulmonary involvement. Lung ultrasound (LUS) and computed tomography (CT) have a high sensitivity in detecting pulmonary interstitial involvement. LUS is a low-cost and radiation-free method, which allows a bedside approach and needs disinfection of only a small contact area, so it could be particularly useful during triage and in intensive care units (ICUs). High-resolution computed tomography (HRCT) is particularly useful in evaluating disease progression or resolution, being able to identify even the smallest changes.

The Role and Challenges of Clinical Imaging During COVID-19 Outbreak

Objective:

The Radiology department played a crucial role in detecting and following up with the COVID-19 disease during the pandemic. The purpose of this review was to highlight and discuss the role of each imaging modality, in the radiology department, that can help in the current pandemic and to determine the challenges faced by staff and how to overcome them.

Materials and Methods:

A literature search was performed using different databases, including PubMed, Google scholar, and the college electronic library to access 2020 published related articles.

Results:

A chest computed tomogram (CT) was found to be superior to a chest radiograph, with regards to the early detection of COVID-19. Utilizing lung point of care ultrasound (POCUS) with pediatric patients, demonstrated excellent sensitivity and specificity, compared to a chest radiography. In addition, lung ultrasound (LUS) showed a high correlation with the disease severity assessed with CT. However, magnetic resonance imaging (MRI) has some limiting factors with regard to its clinical utilization, due to signal loss. The reported challenges that the radiology department faced were mainly related to infection control, staff workload, and the training of students.

Conclusion:

The choice of an imaging modality to provide a COVID-19 diagnosis is debatable. It depends on several factors that should be carefully considered, such as disease stage, mobility of the patient, and ease of applying infection control procedures. The pros and cons of each imaging modality were highlighted, as part of this review. To control the spread of the infection, precautionary measures such as the use of portable radiographic equipment and the use of personal protective equipment (PPE) must be implemented.

Self-supervised CT super-resolution with hybrid model

Software-based methods can improve CT spatial resolution without changing the hardware of the scanner or increasing the radiation dose to the object. In this work, we aim to develop a deep learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high-resolution (HR) CT images. We mathematically analyzed imaging processes in the CT SR imaging problem and synergistically integrated the SR model in the sinogram domain and the deblur model in the image domain into a hybrid model (SADIR). SADIR incorporates the CT domain knowledge and is unrolled into a DL network (SADIR-Net). The SADIR-Net is a self-supervised network, which can be trained and tested with a single sinogram. SADIR-Net was evaluated through SR CT imaging of a Catphan700 physical phantom and a real porcine phantom, and its performance was compared to the other state-of-the-art (SotA) DL-based CT SR methods. On both phantoms, SADIR-Net obtains the highest information fidelity criterion (IFC), structure similarity index (SSIM), and lowest root-mean-square-error (RMSE). As to the modulation transfer function (MTF), SADIR-Net also obtains the best result and improves the MTF50% by 69.2% and MTF10% by 69.5% compared with FBP. Alternatively, the spatial resolutions at MTF50% and MTF10% from SADIR-Net can reach 91.3% and 89.3% of the counterparts reconstructed from the HR sinogram with FBP. The results show that SADIR-Net can provide performance comparable to the other SotA methods for CT SR reconstruction, especially in the case of extremely limited training data or even no data at all. Thus, the SADIR method could find use in improving CT resolution without changing the hardware of the scanner or increasing the radiation dose to the object.

Case report of mild form of a new coronavirus infection in patient with idiopathic pulmonary hypertension

The article presents a clinical case of mild novel coronavirus infection COVID-19 complicated with bilateral interstitial pneumonia in a female patient with idiopathic pulmonary hypertension.

An assessment of Sri Lankan radiographer's knowledge and awareness of radiation protection and imaging parameters related to patient dose and image quality in computed tomography (CT)

INTRODUCTION

As computed tomography (CT) examinations have considerably risen, safe operation is essential to reduce the patients' dose. The main objective of this study was to evaluate the level of knowledge and awareness regarding the CT exposure parameters and radiation protection in CT imaging among Sri Lankan radiographers.

METHODS

An online survey-based study was devised and distributed among the Sri Lankan CT radiographers working in 63 CT units. Questions were divided into three subsections that collected data on the participants' demographic features, knowledge of the radiation protection, and imaging parameters.

RESULTS

Eighty-eight radiographers from 32 CT units (out of 63 CT units) distributed across 11 districts (out of 27 districts) participated in this survey.The percentages of correct responses for the questions related to radiation protection, imaging parameters, noise, Diagnostic Reference Level (DRL), and CT dosimetric parameters were 71%, 79%, 87%, 50%, and 66%, respectively. Although the years of experience did not influence any of above aspects, the level of education significantly impacted the knowledge about radiation protection, exposure parameters, and noise.

CONCLUSION

The radiographer's knowledge of radiation protection and most imaging parameters associated with patient safety and image quality is satisfactory. However, findings also show that participants should fill the knowledge gap in radiation-related risks, CT exposure parameters, dosimetric parameters, and DRL.

IMPLICATIONS FOR PRACTICE

The study suggests the necessity of initiating continuous education programs for radiographers in line with national radiation protection legislation requirements that can be linked with code of practice.

Incidental lung findings in a COVID-19 patient on 18F-FDG PET/CT done for preoperative evaluation of Marjolin's ulcer-first report from Chhattisgarh

The aim of this case is to illustrate the¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography findings of a patient who was admitted in AIIMS, Raipur, for the preoperative evaluation of Marjolin ulcer and was later diagnosed with COVID-19 infection. Apart from the primary lesion in the right foot and pelvic lymph nodes, the scan revealed mild FDG-avid basal ground-glass opacities in bilateral lung fields with mediastinal and hilar lymph nodal involvement, in an otherwise asymptomatic patient.

Using Statistical Measures and Density Maps Generated From Chest Computed Tomography Scans to Identify and Monitor COVID-19 Cases in Radiation Oncology Rapidly

Objectives This study aimed to evaluate quantitative and qualitative screening measures for anomalous computed tomography (CT) scans in cancer patients with potential coronavirus disease 2019 (COVID-19) as an automated detection tool in a radiation oncology treatment setting. Methods We identified a non-COVID-19 cohort and patients with suspected COVID-19 with chest CT scans from February 1, 2020 to June 30, 2020. Lungs were segmented, and a mean normal Hounsfield Unit (HU) histogram was generated for the non-COVID-19 CT scans; these were used to define thresholds for designating the COVID-19-suspected histograms as normal or abnormal. Statistical measures were computed and compared to the threshold levels, and density maps were generated to examine the difference between lungs with and without COVID-19 qualitatively. Results The non-COVID-19 cohort consisted of 70 patients with 70 CT scans, and the cohort of suspected COVID-19 patients consisted of 59 patients with 80 CT scans. Sixty-two patients were positive for COVID-19. The mean HUs and skewness of the intensity histogram discriminated between COVID-19 positive and negative cases, with an area under the curve of 0.948 for positive and 0.944 for negative cases. Skewness correctly identified 57 of 62 positive cases, whereas mean HUs correctly identified 17 of 18 negative cases. Density maps allowed for visualization of the temporal evolution of COVID-19 disease. Conclusions The statistical measures and density maps evaluated here could be employed in an automated screening algorithm for COVID-19 infection. The accuracy is high enough for a simple and rapid screening tool for early identification of suspected infection in patients treated with chemotherapy and radiation therapy already receiving CT scans as part of clinical care. This screening tool could also identify other infections that present critical risks for patients undergoing chemotherapy and radiation therapy, such as pneumonitis.

Catechins: Therapeutic Perspectives in COVID-19-Associated Acute Kidney Injury

Data obtained from several intensive care units around the world have provided substantial evidence of the strong association between impairment of the renal function and in-hospital deaths of critically ill COVID-19 patients, especially those with comorbidities and requiring renal replacement therapy (RRT). Acute kidney injury (AKI) is a common renal disorder of various etiologies characterized by a sudden and sustained decrease of renal function. Studies have shown that 5-46% of COVID-19 patients develop AKI during hospital stay, and the mortality of those patients may reach up to 100% depending on various factors, such as organ failures and RRT requirement. Catechins are natural products that have multiple pharmacological activities, including anti-coronavirus and reno-protective activities against kidney injury induced by nephrotoxic agents, obstructive nephropathies and AKI accompanying metabolic and cardiovascular disorders. Therefore, in this review, we discuss the anti-SARS-CoV-2 and reno-protective effects of catechins from a mechanistic perspective. We believe that catechins may serve as promising therapeutics in COVID-19-associated AKI due to their well-recognized anti-SARS-CoV-2, and antioxidant and anti-inflammatory properties that mediate their reno-protective activities.

Chest Radiological Findings and Clinical Characteristics of Laboratory-Confirmed COVID-19 Patients from Saudi Arabia

Background

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that first emerged in China in December 2019 and quickly spread worldwide. As the prevalence of COVID-19 increases, radiological examination is becoming an essential diagnostic tool for identifying and managing the disease’s progression. Therefore, we aimed to identify the chest imaging features and clinical characteristics of patients with laboratory-confirmed COVID-19 in Saudi Arabia.

Material/Methods

In this retrospective study, data of laboratory-confirmed COVID-19 patients were collected from 4 hospitals in Jeddah, Saudi Arabia. Their common clinical characteristics, as well as imaging features of chest X-rays and computed tomography (CT) images, were analyzed.

Results

A total of 297 patients with laboratory-confirmed COVID-19 who underwent chest imaging were investigated in this study. Of these patients, 77.9% were male and 22.2% were female. Their mean age was 48 years old. The most common clinical symptoms were fever (187 patients; 63%) and cough (174 patients; 58.6%). The predominant descriptive chest imaging findings were ground-glass opacities and consolidation. Locations of abnormalities were bilateral, mainly distributed peripherally, in the lower lung zones, and in the middle lung zones.

Conclusions

This study provides an understanding of the most common clinical and radiological features of patients with laboratory-confirmed COVID-19 in Saudi Arabia. The majority of COVID-19 patients in our study cohort had either stable or worse progression of lung lesions during follow-ups; thus, they presented moderate disease cases. Elderly males were more affected by COVID-19 than females, with fever and cough being the most common clinical symptoms.

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

The Characteristics, Manifestations and Cardiopulmonary Imaging (CT/MRI) of COVID-19 in SARS-CoV-2 Infection

The World Health Organization (WHO) declared the transmission of SARS-CoV-2 a Public Health Emergency of International Concern. The novel coronavirus has diverse manifestations, usually similar to a common cold or influenza. The majority of patients with coronavirus disease have typical imaging features. The typical CT characteristics of patients with COVID-19 pneumonia are ground-glass opacities and consolidative lesions with a peripheral and posterior distribution. Noninvasive imaging methods are precise and rapid means of diagnosing pneumonia and cardiovascular complications caused by COVID-19 infection. Therefore, it is important for clinicians to understand the implications of this pandemic and to be familiar with the different imaging aspects of the novel coronavirus disease. This review focuses on the most commonly reported imaging findings of COVID-19 infection in different patients from different countries, the expert recommendations, and the cardiac manifestations of SARS-CoV-2 infection.

Alkaline phosphatase and score of HRCT as indicators for predicting the severity of COVID-19

BACKGROUND

The clinical symptoms, blood laboratory data, O2 saturation and high-resolution computed tomography (HRCT) findings are critical factors in diagnosis of COVID-19 infection.

METHODS

In this study, 105 hospitalized patients suspected of having COVID-19 were evaluated. Finally, the laboratory and HRCT and related factors data of 83 confirmed cases by HRCT and RT-PCR were analyzed. To compare the median of quantitative variables in the two groups, the Mann-Whitney U test was used. Also, to determine the factors associated with the positiveness of the HRCT result, a univariate logistic model was fitted. Moreover, receiver operating characteristic (ROC) curves were constructed to test the ability of the final model to predict the positiveness of HRCT result.

RESULTS

61.40% of the patients had a comorbidity disease. 89.20% had fever, 92.00% cough, 91.40% dyspnea. Abnormal CRP was seen in 77.80% of the patients, followed by 66.70% lymphopenia, and 60.30% neutrophilia. Also, ALP (abnormal vs. normal) and score of HRCT assessment variables had a significant effect on the positiveness of HRCT findings. 87.95% had abnormal HRCT with 41% bilateral multi lobar patchy ground glass opacity (GGO). Moreover, there was a statistically significant association between the level of O2 saturation and HRCT results.

CONCLUSION

Our findings showed that male patients with middle age and comorbidity disease were more susceptible to the COVID-19 infection. Additionally, clinical features, blood laboratory findings, O2 saturation and HRCT findings are critical factors in the prognosis of COVID-19 infection.

Evaluation of Chest CT Scan as a Screening and Diagnostic Tool in Trauma Patients with Coronavirus Disease 2019 (COVID-19): A Cross-Sectional Study

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.

Value of Bedside Lung Ultrasound in Severe and Critical COVID-19 Pneumonia

BACKGROUND

Lung ultrasound (LUS) is an effective imaging modality that can differentiate pathological lung from non-diseased lung. We aimed to explore the value of bedside LUS in patients with severe and critical coronavirus disease 2019 (COVID-19)-associated lung injury.

METHODS

Sixty-three severe and 33 critical hospitalized subjects with COVID-19 were enrolled in this study. Bedside LUS was performed in all subjects; chest computed tomography was performed on the same day as bedside LUS in 23 cases. The LUS protocol consisted of 12 scanning zones. LUS score based on B-lines and lung consolidation was evaluated.

RESULTS

The most common abnormality of LUS was the various forms of B-lines, detected in 93 (96.9%) subjects; as the second most frequent abnormality, 80 (83.3%) subjects exhibited lung consolidation, mainly located in the posterior lung region. Twenty-four (25.0%) subjects had pleural line abnormalities, and 16 (16.7%) had pleural effusion; 78 (81.3%) subjects had ≥ 2 abnormal LUS patterns, and 93 (96.9%) had bilateral lung involvement. The proportion of bilateral or unilateral lung consolidation and pleural effusion in the critical COVID-19 group were higher than that in the severe group (P < .05). The lung consolidation of critical subjects showed a marked increase in most lung areas, including bilateral lateral lung, posterior lung, and left anterior-inferior lung area. The median (interquartile range) LUS scores of critical cases were higher than those of severe cases: left: 14 (12-17) vs 7 (5-12); right: 14 (10-16) vs 8 (3-12); bilateral: 28 (23-31) vs 15 (8-22) (P < .001 for all). There was a good correlation between the LUS score and the chest computed tomography score (r = 0.887, P < .001).

CONCLUSIONS

The most common abnormal LUS pattern in subjects with severe and critical COVID-19 pneumonia was B-lines, followed by lung consolidation. Bedside LUS can provide important information for pulmonary involvement in patients with COVID-19.

Correlation of Krebs von den Lungen-6 and fibronectin with pulmonary fibrosis in coronavirus disease 2019

OBJECTIVE

Coronavirus Disease 2019 (COVID-19) caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still spreading worldwide, which may progress to pulmonary fibrosis (PF), leading to the worsen outcome. As the markers of lung injury, the correlation of Krebs von den Lungen-6 (KL-6) and fibronectin (Fn) with pulmonary fibrosis in COVID-19 was still unclear.

METHODS

113 patients diagnosed as COVID-19 were enrolled in this retrospective study, and divided into three categories as mild, moderate and severe cases. The concentrations of serum KL-6 and Fn at hospital admission were tested using the method of latex agglutination assay and immunoturbidimetic assay, respectively.

RESULTS

Compared with that in the non-severe COVID-19 cases and normal control subjects, serum KL-6 concentration on admission was significantly higher in the severe group, which was positively correlated with C-reactive protein, and negatively correlated with lymphocytes count. Whereas, no obvious elevation in serum Fn concentration was investigated in COVID-19 patients with the different phenotypes. The severe cases displayed the higher incident rate of pulmonary fibrosis at hospital discharge. Compared with non-PF patients, the COVID-19 cases with PF had the higher serum KL-6 values.

CONCLUSION

Serum KL-6 concentration was significantly elevated in severe COVID-19 patients, which may be useful for evaluating the disease severity. For early prevention of the development of pulmonary fibrosis, high concentrations of serum KL-6 in the early stage of COVID-19 should be paid close attention.

A phantom study to optimise the automatic tube current modulation for chest CT in COVID-19

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19) pandemic. The expert organisations recommend more cautious use of thoracic computed tomography (CT), opting for low-dose protocols. We aimed at determining a threshold value of automatic tube current modulation noise index below which there is a chance to miss an onset of ground-glass opacities (GGO) in COVID-19. A team of radiologists and medical physicists performed 25 phantom CT studies using different automatic tube current modulation settings (^SUREExposure3D technology). We then conducted a retrospective evaluation of the chest CT images from 22 patients with COVID-19 and calculated the density difference between the GGO and unaffected tissue. Finally, the results were matched to the phantom study results to determine the minimum noise index threshold value. The minimum density difference at the onset of COVID-19 was 252 HU (p < 0.001). This was found to correspond to the ^SUREExposure 3D noise index of 36. We established the noise index threshold of 36 for the Canon scanner without iterative reconstructions, allowing for a decrease in the dose-length product by 80%. The proposed protocol needs to be validated in a prospective study.

Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia

OBJECTIVES

To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes.

METHODS

All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated.

RESULTS

A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO2 (r = 0.176), HCO3- (r = 0.284), and PaO2/FiO2 (P/F) values (r = - 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = -0.225), CRP (r = 0.306), PaCO2 (r = 0.227), pH (r = 0.162), HCO3- (r = 0.394), and P/F (r = - 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05).

CONCLUSION

The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation.

KEY POINTS

• Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient's outcome, in particular concerning invasive ventilation.

Persistent interstitial lung abnormalities in post-COVID-19 patients: a case series

A new concept of multisystem disease has emerged as a long-term condition following mild-severe COVID-19 infection. The main symptoms of this affection are breathlessness, chest pain, and fatigue. We present here the clinical case of four COVID-19 patients during hospitalization and 60 days after hospital discharge. Physiological impairment of all patients was assessed by spirometry, dyspnea score, arterial blood gas, and 6-minute walk test 60 days after hospital discharge, and computed tomographic scan 90 days after discharge. All patients had fatigue, which was not related to hypoxemia or impaired spirometry values, and interstitial lung alterations, which occurred in both mechanically ventilated and non-mechanically ventilated patients. In conclusion, identifying the prevalence and patterns of permanent lung damage is paramount in preventing and treating COVID-19-induced fibrotic lung disease. Additionally, and based on our preliminary results, it will be also relevant to establish long-term outpatient programs for these individuals.

ASSURED-SQVM diagnostics for COVID-19: addressing the why, when, where, who, what and how of testing

Introduction: SARS-CoV-2, the new coronavirus that originated in 2019, continues to impact every aspect of society in a profound manner. Testing will remain an important tool to mitigate the effects of this pandemic as early and accurate diagnosis can lead to appropriate countermeasures to reduce mortality and morbidity. However, testing isn’t a simple yes/no answer as the target and host are complex, the virus is a moving target, there is a plethora of tests that identify different parts of the virus and have their own limits and range of detection, and when prevalence is low, false positives and negatives can be very high.

Areas covered: This article covers all the major questions related to COVID-19 diagnostics, the why, when, where, who, what and how of testing, the different types of tests, interpretation of results and the ideal ASSURED-SQVM diagnostic. A comprehensive literature review using all the publicly available databases and government websites and reports was performed.

Expert opinion: Diagnostics that meet the ‘ASSURED-SQVM’ (Affordable, Selective and Sensitive, User-friendly, Rapid and Robust, Equipment-free, Deliverable to end-users and additionally, allows for Self-testing, Quantifiable, detects if pathogens are Viable and can detect Multiple pathogens) would make a major impact in our fight against the current pandemic. While a significant majority of researchers focus on developing novel diagnostics that are highly selective and sensitive, it is the opinion of these authors that other aspects of the ASSURED-SQVM principles also be considered early in the development process for widespread use.

COVID-19 Pandemic: Review of Contemporary and Forthcoming Detection Tools

Recent severe acute respiratory syndrome 2 (SARS-CoV-2) known as COVID-19, presents a deadly challenge to the global healthcare system of developing and developed countries, exposing the limitations of health facilities preparedness for emerging infectious disease pandemic. Opportune detection, confinement, and early treatment of infected cases present the first step in combating COVID-19. In this review, we elaborate on various COVID-19 diagnostic tools that are available or under investigation. Consequently, cell culture, followed by an indirect fluorescent antibody, is one of the most accurate methods for detecting SARS-CoV-2 infection. However, restrictions imposed by the regulatory authorities prevented its general use and implementation. Diagnosis via radiologic imaging and reverse transcriptase PCR assay is frequently employed, considered as standard procedures, whereas isothermal amplification methods are currently on the verge of clinical introduction. Notably, techniques such as CRISPR-Cas and microfluidics have added new dimensions to the SARS-CoV-2 diagnosis. Furthermore, commonly used immunoassays such as enzyme-linked immunosorbent assay (ELISA), lateral flow immunoassay (LFIA), neutralization assay, and the chemiluminescent assay can also be used for early detection and surveillance of SARS-CoV-2 infection. Finally, advancement in the next generation sequencing (NGS) and metagenomic analysis are smoothing the viral detection further in this global challenge.

Association between clinical, laboratory findings and chest CT in COVID-19 in a secondary hospital in Jakarta, Indonesia

Introduction

A new emerging infectious disease caused by SARS-CoV-2 has caused a global pandemic. Early diagnosis is essential to prevent and halt the spread of the disease, patient management and isolation. In this study, we aimed to reveal correlations between clinical and laboratory findings with chest CT.

Methods

This in an observational case series single center study in a secondary hospital in Jakarta, Indonesia. Patients were included if they had typical symptoms and positive RT-PCR for SARS-CoV-2.

Results

Forty-two patients with positive RT-PCR were included in this study. Typical CT findings were present in 33 (78.6%). We found a positive correlation between patients in whom the imaging was performed after the 4^th day of symptoms and chest CT findings (r=0.365 p<0.05). In receiver operating characteristic analysis of this parameter, the area under curve (AUC) was 0.678, and the sensitivity and specificity were 0.96 and 0.44, respectively.

Conclusions

Early diagnosis of COVID-19 is essential to promptly isolate and treat suspected patients. Utilization of chest CT to help diagnosis in this pandemic era needs to be considered by healthcare facilities especially if RT-PCR is limited.

Correlation between lung ultrasound and chest CT patterns with estimation of pulmonary burden in COVID-19 patients

Purpose

The capability of lung ultrasound (LUS) to distinguish the different pulmonary patterns of COVID-19 and quantify the disease burden compared to chest CT is still unclear.

Methods

PCR-confirmed COVID-19 patients who underwent both LUS and chest CT at the Emergency Department were retrospectively analysed. In both modalities, twelve peripheral lung zones were identified and given a Severity Score basing on main lesion pattern. On CT scans the well-aerated lung volume (%WALV) was visually estimated. Per-patient and per-zone assessments of LUS classification performance taking CT findings as reference were performed, further revisioning the images in case of discordant results. Correlations between number of disease-positive lung zones, Severity Score and %WALV on both LUS and CT were assessed. The area under receiver operating characteristic curve (AUC) was calculated to determine LUS performance in detecting %WALV ≤ 70 %.

Results

The study included 219 COVID-19 patients with abnormal chest CT. LUS correctly identified as positive 217 (99 %) patients, but per-zone analysis showed sensitivity = 75 % and specificity = 66 %. The revision of the 121 (55 %) cases with positive LUS and negative CT revealed COVID-compatible lesions in 42 (38 %) CT scans. Number of disease-positive zones, Severity Score and %WALV between LUS and CT showed moderate correlations. The AUCs for LUS Severity Score and number of LUS-positive zones did not differ in detecting %WALV ≤ 70 %.

Conclusion

LUS in COVID-19 is valuable for case identification but shows only moderate correlation with CT findings as for lesion patterns and severity quantification. The number of disease-positive lung zones in LUS alone was sufficient to discriminate relevant disease burden.

Diagnostic accuracy of lung ultrasound for SARS-CoV-2: a retrospective cohort study

Background

As medical infrastructures are strained by SARS-CoV-2, rapid and accurate screening tools are essential. In portions of the world, reverse transcription polymerase chain reaction (RT-PCR) testing remains slow and in limited supply, and computed tomography is expensive, inefficient, and involves exposure to ionizing radiation. Multiple studies evaluating the efficiency of lung point-of-care ultrasound (POCUS) have been published recently, but include relatively small cohorts and often focus on characteristics associated with severe illness rather than screening efficacy. This study utilizes a retrospective cohort to evaluate the test characteristics (sensitivity, specificity, likelihood ratios, predictive values) of lung POCUS in the diagnosis of SARS-CoV-2, and to determine lung score cutoffs that maximize performance for use as a screening tool.

Results

Lung POCUS examinations had sensitivity 86%, specificity 71.6%, NPV 81.7%, and PPV 77.7%. The Lung Ultrasound Score had an area under the curve of 0.84 (95% CI 0.78, 0.90). When including only complete examinations visualizing 12 lung fields, lung POCUS had sensitivity 90.9% and specificity 75.6%, with NPV 87.2% and PPV 82.0% and an area under the curve of 0.89 (95% CI 0.83, 0.96). Lung POCUS was less accurate in patients with a history of interstitial lung disease, severe emphysema, and heart failure.

Conclusions

When applied in the appropriate patient population, lung POCUS is an inexpensive and reliable tool for rapid screening and diagnosis of SARS-CoV-2 in symptomatic patients with influenza-like illness. Adoption of lung POCUS screening for SARS-CoV-2 may identify patients who do not require additional testing and reduce the need for RT-PCR testing in resource-limited environments and during surge periods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13089-021-00217-7.

Hematological and Inflammatory Parameters to Predict the Prognosis in COVID-19

We aimed to evaluate the predictive ability of hematological and inflammatory parameters for mortality in COVID-19 patients. This was a retrospective study of hospitalized COVID-19 patients over 18 years old between March 2020 and May 2020. Patients were diagnosed to have COVID-19 based either on chest computed tomography findings or reverse transcriptase-polymerase chain reaction test. Age, gender, chronic medical conditions, and laboratory values including hemogram parameters (white blood cell, neutrophil, lymphocyte, and platelet counts), neutrophil to lymphocyte ratio, D-dimer, ferritin, fibrinogen, C-reactive protein, procalcitonin, prothrombin time, activated partial thromboplastin time and the international normalized ratio were recorded. Overall, we included 302 patients. Of these, 148 patients were male; the male to female ratio was 0.961. The mean age of the entire study cohort was 57.1 ± 17.6 years. The most common chronic medical condition was hypertension (38.1%). Half of the patients received low molecular weight heparin. During the study period, 25 patients (8.2%) died. White blood cell count and neutrophil count were significantly higher, whereas lymphocyte count was significantly lower in the deceased patients. The median neutrophil to lymphocyte ratio was 11.6 in the deceased patients, it was significantly higher than the surviving patients (p < 0.001). The values of C-reactive protein, procalcitonin, D-dimer, and ferritin were significantly higher among the deceased patients. Prothrombin time, activated partial thromboplastin time and the international normalized ratio were significantly longer in the deceased group compared with the surviving group. Logistic regression analysis showed age > 65 years, neutrophil to lymphocyte ratio, activated partial thromboplastin time, and hypertension as the independent predictors of mortality. The rate of abnormal inflammatory and hematologic-coagulation parameters increased with the COVID-19 severity. Age > 65 years, hypertension, activated partial thromboplastin time and neutrophil to lymphocyte ratio were the independent predictors of mortality.

The Rheumatology Drugs for COVID-19 Management: Which and When?

INTRODUCTION

While waiting for the development of specific antiviral therapies and vaccines to effectively neutralize the SARS-CoV2, a relevant therapeutic strategy is to counteract the hyperinflammatory status, characterized by an increase mainly of interleukin (IL)-1β, IL-2, IL-6, IL-7, IL-8, and tumor necrosis factor (TNF)-α, which hallmarks the most severe clinical cases. 'Repurposing' immunomodulatory drugs and applying clinical management approved for rheumatic diseases represents a game-changer option. In this article, we will review the drugs that have indication in patients with COVID-19, including corticosteroids, antimalarials, anti-TNF, anti-IL-1, anti-IL-6, baricitinib, intravenous immunoglobulins, and colchicine. The PubMed, Medline, and Cochrane Library databases were searched for English-language papers concerning COVID-19 treatment published between January 2020 and October 2020. Results were summarized as a narrative review due to large heterogeneity among studies. In the absence of specific treatments, the use of immunomodulatory drugs could be advisable in severe COVID-19 patients, but clinical outcomes are still suboptimal. An early detection and treatment of the complications combined with a multidisciplinary approach could allow a better recovery of these patients.

Effects of Chinese Medicine on Symptoms, Syndrome Evolution, and Lung Inflammation Absorption in COVID-19 Convalescent Patients during 84-Day Follow-up after Hospital Discharge: A Prospective Cohort and Nested Case-Control Study

OBJECTIVE

To observe the changes of symptoms, Chinese medicine (CM) syndrome, and lung inflammation absorption during convalescence in patients with coronavirus disease 2019 (COVID-19) who had not totally recovered after hospital discharge and whether CM could promote the improvement process.

METHODS

This study was designed as a prospective cohort and nested case-control study. A total of 96 eligible patients with COVID-19 in convalescence were enrolled from Beijing Youan Hospital and Beijing Huimin Hospital and followed up from the hospital discharged day. Patients were divided into the CM (64 cases) and the control groups (32 cases) based on the treatment with or without CM and followed up at 14, 28, 56, and 84 days after discharge. In the CM group, patients received the 28-day CM treatment according to two types of CM syndrome. Improvements in clinical symptoms, CM syndrome, and absorption of lung inflammation were observed.

RESULTS

All the 96 patients completed the 84-day follow-up from January 21 to March 28, 2020. By the 84th day of follow-up, respiratory symptoms were less than 5%. There was no significant difference in the improvement rates of symptoms, including fatigue, sputum, cough, dry throat, thirst, and upset, between the two groups (P>0.05). Totally 82 patients (85.42%) showed complete lung inflammation absorption at the 84-day follow-up. On day 14, the CM group had a significantly higher absorption rate than the control group (P<0.05) and the relative risk of absorption for CM vs. control group was 3.029 (95% confidence interval: 1.026-8.940). The proportions of CM syndrome types changed with time prolonging: the proportion of the pathogen residue syndrome gradually decreased, and the proportion of both qi and yin deficiency syndrome gradually increased.

CONCLUSIONS

Patients with COVID-19 in convalescence had symptoms and lung inflammation after hospital discharge and recovered with time prolonging. CM could improve lung inflammation for early recovery. The types of CM syndrome can be transformed with time prolonging. (Registration No. ChiCTR2000029430).

COVID-19 in the paediatric population of Kazakhstan

Background: To date, there have been no studies of COVID-19 infection in children in Central Asia, particularly the Republic of Kazakhstan. This report analyses the epidemiological data on COVID-19 infection in children in Kazakhstan.Methods: The study included 650 paediatric patients diagnosed with COVID-19. Demographic and epidemiological data and the symptoms and radiological evidence of complications were collected and analysed. Children were sub-divided into four groups: neonates/infants, young children, older children and adolescents.Results: All of the 650 children were under 19 years of age, 56.3% of whom were male, and 122 (18.8%) were newborns and infants. The majority of cases (n = 558, 85.8%) were asymptomatic and only four cases were severe (0.6%). The symptoms were as follows in descending order: cough (14.8%), sore throat (12.8%), fever (9.1%) and rhinorrhoea (5.5%). Diarrhoea (2%), dyspnoea (1.8%) and muscle pain were rare (1.1%). Only three children required intensive care, including invasive ventilation. One patient had acute respiratory distress syndrome. There were no deaths.Conclusion: Most cases of COVID-19 infection in children in Kazakhstan were asymptomatic or the symptoms were mild. Only three patients required intensive care.

The role of CT imaging for management of COVID-19 in epidemic area: early experience from a University Hospital

BACKGROUND

To evaluate the clinical value of the chest CT scan compared to the reference standard real-time polymerase chain reaction (RT-PCR) in COVID-19 patients.

METHODS

From March 29th to April 15th of 2020, a total of 240 patients with respiratory distress underwent both a low-dose chest CT scan and RT-PCR tests. The performance of chest CT in diagnosing COVID-19 was assessed with reference to the RT-PCR result. Two board-certified radiologists (mean 24 years of experience chest CT), blinded for the RT-PCR result, reviewed all scans and decided positive or negative chest CT findings by consensus.

RESULTS

Out of 240 patients, 60% (144/240) had positive RT-PCR results and 89% (213/240) had a positive chest CT scans. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of chest CT in suggesting COVID-19 were 100% (95% CI: 97-100%, 144/240), 28% (95% CI: 19-38%, 27/240), 68% (95% CI: 65-70%) and 100%, respectively. The diagnostic accuracy of the chest CT suggesting COVID-19 was 71% (95% CI: 65-77%). Thirty-three patients with positive chest CT scan and negative RT-PCR test at baseline underwent repeat RT-PCR assay. In this subgroup, 21.2% (7/33) cases became RT-PCR positive.

CONCLUSION

Chest CT imaging has high sensitivity and high NPV for diagnosing COVID-19 and can be considered as an alternative primary screening tool for COVID-19 in epidemic areas. In addition, a negative RT-PCR test, but positive CT findings can still be suggestive of COVID-19 infection.

Coronavirus Disease 2019 (COVID-19) in Italy: Double Reading of Chest CT Examination

To assess the performance of the second reading of chest compute tomography (CT) examinations by expert radiologists in patients with discordance between the reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test for COVID-19 viral pneumonia and the CT report. Three hundred and seventy-eight patients were included in this retrospective study (121 women and 257 men; 71 years median age, with a range of 29-93 years) and subjected to RT-PCR tests for suspicious COVID-19 infection. All patients were subjected to CT examination in order to evaluate the pulmonary disease involvement by COVID-19. CT images were reviewed first by two radiologists who identified COVID-19 typical CT patterns and then reanalyzed by another two radiologists using a CT structured report for COVID-19 diagnosis. Weighted к values were used to evaluate the inter-reader agreement. The median temporal window between RT-PCRs execution and CT scan was zero days with a range of (-9,11) days. The RT-PCR test was positive in 328/378 (86.8%). Discordance between RT-PCR and CT findings for viral pneumonia was revealed in 60 cases. The second reading changed the CT diagnosis in 16/60 (26.7%) cases contributing to an increase the concordance with the RT-PCR. Among these 60 cases, eight were false negative with positive RT-PCR, and 36 were false positive with negative RT-PCR. Sensitivity, specificity, positive predictive value and negative predictive value of CT were respectively of 97.3%, 53.8%, 89.0%, and 88.4%. Double reading of CT scans and expert second readers could increase the diagnostic confidence of radiological interpretation in COVID-19 patients.