Chest imaging in pregnant patients with COVID-19: Recommendations, justification, and optimization

Evaluation of COVID-19 related complication is challenging in pregnancy, due to concerns about ionizing radiation risk to mother and the fetus. Although there are instances when diagnostic imaging is clinically warranted for COVID-19 evaluation despite the minimal risks of radiation exposure, often there are concerns raised by the patients and sometimes by the attending physicians. This article reviews the current recommendations on indications of chest imaging in pregnant patients with COVID-19, the dose optimization strategies, and the risks related to imaging exposure during pregnancy. In clinical practice, these imaging strategies are key in addressing the complex obstetrical complications associated with COVID-19 pneumonia.

Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19)

BACKGROUND

In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world.

PURPOSE

To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia.

MATERIAL AND METHODS

The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed.

RESULTS

Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts.

CONCLUSION

The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses.

Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT

Objectives

1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification.

Methods

This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy.

Results

The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; p<0.0001).

Conclusions

A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.

Multifractal analysis of chest CT images of patients with the 2019 novel coronavirus disease (COVID-19)

The present study was done to evaluate chest computed tomography (CT) images of patients with 2019 novel coronavirus disease (COVID-19) by multifractal technique as a new method to find a way for comparing lung infection quantitatively and identifying progression pattern of the disease. The multifractal spectra extracted from analysis of CT images showed that these spectra were correlated with lung infection amount and disease progression so that, multifractal parameters (α_min, α_max, ∆α, f(α_min), f(α_max), ∆f(α), α(q = 0), and f(α) max) were strongly dependent on amount of lung infection. The results demonstrated that multifractality of chest CT images was increased with the increase in lung infection in patients. The interesting and promising result was that capacity dimension (D₀) as a new diagnostic parameter varied linearly with progression and reduction of lung infection. A critical value was found for D₀, according to which patients with D₀ lower than 1.4 can be healed by treatment. Therefore, herein, a way was found for quantitative assessment of lung infection of patients with COVID-19 by analyzing chest CT images using the multifractal method. This method can be very effective for physicians in diagnosis and treatment of pneumonia caused by COVID-19 and timely identification of therapeutic effects.

CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis

Quick screening patients with COVID-19 is the most important way of controlling transmission by isolation and medical treatment. Chest computed tomography (CT) has been widely used during the initial screening process, including pneumonia diagnosis, severity assessment, and differential diagnosis of COVID-19. The course of COVID-19 changes rapidly. Serial CT imaging could observe the distribution, density, and range of lesions dynamically, monitor the changes, and then guide towards appropriate treatment. The aim of the review was to explore the chest CT findings and dynamic CT changes of COVID-19 using systematic evaluation methods, instructing the clinical imaging diagnosis. A systematic literature search was performed. The quality of included literature was evaluated with a quality assessment tool, followed by data extraction and meta-analysis. Homogeneity and publishing bias were analyzed. A total of 109 articles were included, involving 2908 adults with COVID-19. The lesions often occurred in bilateral lungs (74%) and were multifocal (77%) with subpleural distribution (81%). Lesions often showed ground-glass opacity (GGO) (68%), followed by GGO with consolidation (48%). The thickening of small vessels (70%) and thickening of intralobular septum (53%) were also common. The dynamic changes of chest CT manifestations showed that lesions were absorbed and improved gradually after reaching the peak (80%), had progressive deterioration (55%), were absorbed and improved gradually (46%), fluctuated (22%), or remained stable (26%). The review showed the common and key CT features and the dynamic imaging change patterns of COVID-19, helping with timely management during COVID-19 pandemic.

Experience With the Use of Baricitinib and Tocilizumab Monotherapy or Combined, in Patients With Interstitial Pneumonia Secondary to Coronavirus COVID19: A Real-World Study

Objective

To describe the experience of treatment with baricitinib (BARI) and/or tocilizumab (TCZ), in monotherapy or combined, in patients admitted for interstitial pneumonia secondary to COVID19, and for 30 days after discharge.

Methods

Medical records of patients admitted with COVID19 and IP with PaO₂/FiO₂ < 300, treated with BARI and/or TCZ, and compared with patients who did not, were retrospectively reviewed.

Results

Sixty patients were included; 43 (72%) are males, mean age 67 (SD: 14) years (<50 years: 17%; 51–70: 30%; >70: 53%), with 8.5 (SD: 1) days of symptoms. Sixteen (27%) patients required ICU (94% in <70 years). Fifteen (25%) patients died, 67% in >70 years; 11 (18%) patients died in the first 15 days of admission and 4 (7%) between days 16 to 30. Twenty-three (38%) patients received BARI, 12 (52%) monotherapy (Group 1), during 6 (SD: 2.6) days on average, none required ICU and 2 (17%) died. Thirty-one (52%) patients received TCZ, 20 (33%) as monotherapy (Group 2), 16 (52%) patients required ICU and 4 (20%) died. In the 11 (18%) patients who received BARI (2.8 [SD: 2.5] days average) and TCZ combined (Group 3), 3 (27%) required ICU and died. There were no severe side effects in BARI or TCZ patients. In the 17 (28%) patients who received neither BARI nor TCZ (Group 4), none required ICU and 6 (35%) died. Mean (SD) PaO₂/FiO₂ at admission between groups was respectively: 167 (82.3), 221 (114.9), 236 (82.3), 276 (83.2).

Conclusion

Treatment with BARI and TCZ did not cause serious side effects. They could be considered early in patients with NI secondary to COVID19 and impaired PaO2/PaFi.

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.

COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images

Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.

Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features

Recognition of COVID-19 is a challenging task which consistently requires taking a gander at clinical images of patients. In this paper, the transfer learning technique has been applied to clinical images of different types of pulmonary diseases, including COVID-19. It is found that COVID-19 is very much similar to pneumonia lung disease. Further findings are made to identify the type of pneumonia similar to COVID-19. Transfer Learning makes it possible for us to find out that viral pneumonia is same as COVID-19. This shows the knowledge gained by model trained for detecting viral pneumonia can be transferred for identifying COVID-19. Transfer Learning shows significant difference in results when compared with the outcome from conventional classifications. It is obvious that we need not create separate model for classifying COVID-19 as done by conventional classifications. This makes the herculean work easier by using existing model for determining COVID-19. Second, it is difficult to detect the abnormal features from images due to the noise impedance from lesions and tissues. For this reason, texture feature extraction is accomplished using Haralick features which focus only on the area of interest to detect COVID-19 using statistical analyses. Hence, there is a need to propose a model to predict the COVID-19 cases at the earliest possible to control the spread of disease. We propose a transfer learning model to quicken the prediction process and assist the medical professionals. The proposed model outperforms the other existing models. This makes the time-consuming process easier and faster for radiologists and this reduces the spread of virus and save lives.

COVID-DAI: A novel framework for COVID-19 detection and infection growth estimation using computed tomography images

The COVID‐19 pandemic is spreading at a fast pace around the world and has a high mortality rate. Since there is no proper treatment of COVID‐19 and its multiple variants, for example, Alpha, Beta, Gamma, and Delta, being more infectious in nature are affecting millions of people, further complicates the detection process, so, victims are at the risk of death. However, timely and accurate diagnosis of this deadly virus can not only save the patients from life loss but can also prevent them from the complex treatment procedures. Accurate segmentation and classification of COVID‐19 is a tedious job due to the extensive variations in its shape and similarity with other diseases like Pneumonia. Furthermore, the existing techniques have hardly focused on the infection growth estimation over time which can assist the doctors to better analyze the condition of COVID‐19‐affected patients. In this work, we tried to overcome the shortcomings of existing studies by proposing a model capable of segmenting, classifying the COVID‐19 from computed tomography images, and predicting its behavior over a certain period. The framework comprises four main steps: (i) data preparation, (ii) segmentation, (iii) infection growth estimation, and (iv) classification. After performing the pre‐processing step, we introduced the DenseNet‐77 based UNET approach. Initially, the DenseNet‐77 is used at the Encoder module of the UNET model to calculate the deep keypoints which are later segmented to show the coronavirus region. Then, the infection growth estimation of COVID‐19 per patient is estimated using the blob analysis. Finally, we employed the DenseNet‐77 framework as an end‐to‐end network to classify the input images into three classes namely healthy, COVID‐19‐affected, and pneumonia images. We evaluated the proposed model over the COVID‐19‐20 and COVIDx CT‐2A datasets for segmentation and classification tasks, respectively. Furthermore, unlike existing techniques, we performed a cross‐dataset evaluation to show the generalization ability of our method. The quantitative and qualitative evaluation confirms that our method is robust to both COVID‐19 segmentation and classification and can accurately predict the infection growth in a certain time frame.

Evaluation of the Levels of Peripheral CD3+, CD4+, and CD8+ T Cells and IgG and IgM Antibodies in COVID-19 Patients at Different Stages of Infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects the stimulatory levels of cellular-mediated immunity, which plays an essential role in controlling SARS-CoV-2 infection. In fact, several studies have shown the association of lymphopenia with severe COVID-19 in patients. The aim of this study is to investigate the response of the immune system, including cell-mediated immunity and antibody production, during different stages of SARS-CoV-2 infection. Peripheral blood and serum samples were collected from patients with moderate infection, patients under medication (hospitalized), patients who had recovered, and healthy individuals (n = 80). Flow cytometry analysis was performed on peripheral blood samples to determine the cellular immunity profile of each patient. The data showed a significant reduction in the levels of CD3⁺, CD4⁺, and CD8⁺ T cells and CD45⁺ cells in the moderate and under-medication groups, suggesting lymphopenia in those patients. Also, enzyme-linked immunosorbent assay (ELISA) was conducted on the serum samples to measure the levels of antibodies, including IgM and IgG, in each patient. The results revealed a significant increase in the levels of IgM in the moderate infection and under-medication patients, thus indicating the production of IgM during the first week of infection. Furthermore, changes in the levels of IgG were significantly detected among recovered patients, indicating therefore a remarkable increase during the recovery stage of SARS-CoV-2 infection and thus a strong humoral-mediated immunity. In summary, the results of this study may help us to understand the main role of the cellular immune responses, including CD3⁺, CD4⁺, and CD8⁺ T cells, against SARS-CoV-2 infection. This understanding might support the development of SARS-CoV-2 treatments and vaccines in the near future. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 in China. This virus is a serious threat to people not only in China but also worldwide, where it has been detected in over 222 countries. It has been reported that ∼3.4% of SARS-CoV-2-infected patients have died. The significance of our study relies on the fact that an enzyme-linked immunosorbent assay and flow cytometry were used to measure the levels of antibodies and cellular immune response, respectively, from clinical samples of patients infected with SARS-CoV-2.

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.

Retinal vascular impairment matched to the pulmonary damage in early post-COVID 19 patients

BACKGROUND

Endothelium damage is a crucial element in the pathogenesis of SARS-Cov-2 infection. Most casualties in critical COVID-19 cases are due to ARDS, diffuse coagulopathy and cytokine storm. ARDS itself is a consequence of pulmonary endothelial cells damage. Damage to retinal capillary microcirculation in post-infective period has been investigated through Optical Coherence Tomography Angiography (OCTA). The aim of the present study is to find a correlation between signs of retinal vascular damage and pulmonary impairment.

METHODS

Patients admitted to hospital and subsequently recovered from COVID-19 infection were summoned 1 month later to undergo coherence tomography (CT) scan and OCTA examination.

RESULTS

The study population included 87 COVID-19 patients with a mean age of 54.28 ± 14.44 years. Oxygen therapy, non-invasive and invasive mechanical ventilation were necessary in 33, 11 and 4 patients respectively to provide respiratory support during the acute course of the disease. Pulmonary involvement interested 54 patients (62.1%). Peripheral (27.6%) or diffuse (29.9%) involvement and ground glass (GG) opacities (47.1%) represented the prevalent radiological finding. A reduced RCPI FI was independently correlated with the presence of reticulation pattern in CT scan (p = .019). Also, RNFL and GCC were thinner in patients who displayed reticulation pattern (respectively p = .025 and p = .015).

CONCLUSIONS

A reduction in RPCP-FI and RNFL and GCC thickness were independently correlated to the presence of CT reticulation pattern. This association can reflect cytokine induced remodeling in both organs as a consequence of systemic endothelial damage and inflammation.

Uma Apresentação Rara de COVID-19 com Embolia Pulmonar

A doença de coronavírus 2019 (COVID-19) foi relatada em quase todos os países do mundo desde dezembro de 2019. A infecção por SARS-CoV-2 é frequentemente assintomática ou com sintomas leves, mas também pode levar à hipóxia, um estado hiperinflamatório e coagulopatia. Os parâmetros de coagulação anormais estão associados a complicações trombóticas, incluindo embolia pulmonar na COVID-19, mas pouco se sabe sobre os mecanismos. A semelhança dos sintomas iniciais de ambas as doenças também pode ser confusa, portanto, os médicos devem estar cientes do potencial para condições concomitantes. Apresentamos aqui um caso que não apresentava opacidades em vidro fosco nos pulmões, mas apresentava embolia pulmonar e derrame pleural em associação com infecção por COVID-19.

Usefulness of Selected Peripheral Blood Counts in Predicting Death in Patients with Severe and Critical COVID-19

BACKGROUND

Immune dysregulation and hypoxemia are two important pathophysiological problems in patients with COVID-19 that affect peripheral blood count parameters. We hypothesized that assessment of the neutrophil-lymphocyte ratio (NLR) and red blood cell distribution width index (RDW-SD) could predict death in patients with severe and critical COVID-19.

METHODS

Seventy patients admitted to the intensive care unit (ICU) for COVID-19 acute respiratory failure were included in the study. RDW-SD and NLR on the day of ICU admission and peak values during the entire hospitalization were assessed. The primary endpoint was death before ICU discharge.

RESULTS

Patients who died had higher NLR on admission (20.3, IQR 15.3-30.2 vs. 11.0, IQR 6.8-16.9; p = 0.003) and higher RDW-SD (48.1 fL; IQR 43.1-50.5 vs. 43.9 fL; IQR 40.9-47.3, p = 0.01) than patients discharged from the ICU. NLR and RDW-SD values on ICU admission accurately predicted death in 76% (AUC = 0.76; 95%CI 0.65-0.86; p = 0.001; cut-off > 14.38) and 72% of cases (AUC = 0.72; 95%CI 0.60-0.82; p = 0.003; cut-off > 44.7 fL), respectively. Multivariable analysis confirmed that NLR > 14.38 on the day of ICU admission was associated with a 12-fold increased risk of death (logOR 12.43; 95%CI 1.61-96.29, p = 0.02), independent of other blood counts, clinical and demographic parameters.

CONCLUSIONS

Neutrophil-lymphocyte ratio determined on the day of ICU admission may be a useful biomarker predicting death in patients with severe and critical COVID-19.

Evaluation of chest CT scan finding in the patients with acute respiratory symptoms following positive results of RT-PCR-COVID19

BACKGROUND

Reverse transcription-polymerase chain reaction (RT-PCR) is a standard technique for diagnosing coronavirus disease 2019 (COVID-19). The parameters for the diagnosis of COVID-19 included the history of exposure to positive COVID-19 patients, clinical signs and symptoms related to the disease, inflammation factors in the blood test or positive antigen-antibody test, and chest computed tomography (CT) findings. The current study evaluated the chest CT scan findings in patients with respiratory problems following positive RT-PCR of COVID 19.

MATERIALS AND METHODS

This cross-sectional study was performed on 120 patients referred to Ali Ibn-Abi Talib Hospital in Rafsanjan, Kerman Province, Iran, with respiratory symptoms between Dec-2019 to Dec-2020. Two radiologists reviewed the chest CT scans of these patients using the checklist that included parameters such as the types of involvement (consolidation/grand-glass/crazy paving, etc.) and the patterns of involvement (central/peripheral), and the pleural findings.

RESULTS

The CT scan was conducted in 107 patients with a typical condition and 11 patients with an atypical form of the disease. The frequency of the typical CT image of COVID-19 in the male group was significantly higher than that in the female group (P=0.004). The frequency of reverse halo sign, septal thickening, cardiomegaly, and crazy paving was significantly higher in males than in females (P≤0.05). Also, there was a significant difference between age groups based on the number of involved lobes (P=0.04).

CONCLUSION

Chest CT scan is an important diagnostic method for COVID 19 with high sensitivity. The parameters in the CT scan are beneficial for the diagnosis of COVID 19. In addition, some characters in CT scans in the male gender are more specific.

Efficacy and safety of Bufei Huoxue capsules in the management of convalescent patients with COVID-19 infection: A multicentre, double-blind, and randomised controlled trial

BACKGROUND

As of September 17, 2021, coronavirus disease 2019 (COVID-19) has infected more than 226 million people in a worldwide pandemic, with conservative estimates suggesting that there are more than 204 million convalescent patients with COVID-19. Previous studies have indicated that patients in the recovery phase exhibit decreased function of multiple organs. In China, traditional Chinese medicine (TCM) treatment is recommended in the rehabilitation period of COVID-19; however, the safety and efficacy of such treatment remain to be confirmed.

AIM OF STUDY

The present study aimed to evaluate the efficacy and safety of Bufei Huoxue (BFHX) in restoring the functional status and exercise tolerance of patients recovering from COVID-19.

METHODS

A total of 131 patients in the rehabilitation period of COVID-19 infection were randomly divided into a Bufei Huoxue (BFHX) group (n = 66) and a placebo group (n = 65). BFHX or placebo was given orally three times a day (1.4 g/dose) for 90 days. The primary outcomes was to evaluate improvements in exercise tolerance and imaging manifestations on chest computed tomography (CT).

RESULTS

After the exclusion of two patients who withdrew prior to receiving any medications, 129 patients were recruited, including 64 patients in the BFHX group and 65 patients in the placebo group. After 3 months of treatment, the BFHX group exhibited greater attenuation of pneumonia lesions on chest CT than the placebo group (P<0.05). Improvements in 6-min walk distance (6MWD) relative to baseline were also significantly better in the BFHX group than in the placebo group (P<0.01). Scores on the Fatigue Assessment Inventory (FAI) were lower in the BFHX group than in the placebo group (P<0.05). Although the rate of adverse events was higher in the BFHX group than in the placebo group (9.38% vs. 4.62%), the difference was not significant (P=0.3241).

CONCLUSIONS

BFHX may exert strong rehabilitative effects on physiological activity in patients recovering from COVID-19, which may in turn attenuate symptoms of fatigue and improve exercise tolerance.

A deep adversarial model for segmentation-assisted COVID-19 diagnosis using CT images

The outbreak of coronavirus disease 2019 (COVID-19) is spreading rapidly around the world, resulting in a global pandemic. Imaging techniques such as computed tomography (CT) play an essential role in the diagnosis and treatment of the disease since lung infection or pneumonia is a common complication. However, training a deep network to learn how to diagnose COVID-19 rapidly and accurately in CT images and segment the infected regions like a radiologist is challenging. Since the infectious area is difficult to distinguish manually annotation, the segmentation results are time-consuming. To tackle these problems, we propose an efficient method based on a deep adversarial network to segment the infection regions automatically. Then, the predicted segment results can assist the diagnostic network in identifying the COVID-19 samples from the CT images. On the other hand, a radiologist-like segmentation network provides detailed information of the infectious regions by separating areas of ground-glass, consolidation, and pleural effusion, respectively. Our method can accurately predict the COVID-19 infectious probability and provide lesion regions in CT images with limited training data. Additionally, we have established a public dataset for multitask learning. Extensive experiments on diagnosis and segmentation show superior performance over state-of-the-art methods.

Cost-Sensitive Uncertainty Hypergraph Learning for Identification of Lymph Node Involvement With CT Imaging

Lung adenocarcinoma (LUAD) is the most common type of lung cancer. Accurate identification of lymph node (LN) involvement in patients with LUAD is crucial for prognosis and making decisions of the treatment strategy. CT imaging has been used as a tool to identify lymph node involvement. To tackle the shortage of high-quality data and improve the sensitivity of diagnosis, we propose a Cost-Sensitive Uncertainty Hypergraph Learning (CSUHL) model to identify the lymph node based on the CT images. We design a step named "Multi-Uncertainty Measurement" to measure the epistemic and the aleatoric uncertainty, respectively. Given the two types of attentional uncertainty weights, we further propose a cost-sensitive hypergraph learning to boost the sensitivity of diagnosing, targeting task-driven optimization of the clinical scenarios. Extensive qualitative and quantitative experiments on the real clinical dataset demonstrate our method is capable of accurately identifying the lymph node and outperforming state-of-the-art methods across the board.

Chest CT severity score: assessment of COVID‑19 severity and short-term prognosis in hospitalized Iranian patients

Background

The aim of this study was to evaluate the value of chest computed tomography (CT) severity score in the assessment of coronavirus disease 2019 (COVID‑19) severity and short-term prognosis.

Methods

In this cross-sectional study, we evaluated all patients who were referred to our university hospital, from 21 May 2020 to 22 June 2020 with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcription-polymerase chain reaction (RT-PCR) test. The patients suspected of having other respiratory diseases including influenza, according to an infectious disease specialist, and those without chest CT scan were excluded. A chest CT was obtained for all patients between days 4 and 7 days after symptom onset. Chest CT severity score was also calculated based on the degree of involvement of the lung lobes as 0%, (0 points), 1–25% (1 point), 26–50% (2 points), 51–75% (3 points), and 76–100% (4 points). The CT severity score was quantified by summing the 5 lobe indices (range 0–20). The ROC curve analysis was performed for the clinical value of CT scores in distinguishing the patients based on the severity of disease (mild/moderate group versus severe group), ICU admission, intubation requirement, and mortality.

Results

Of the 148 patients included, 93 patients recovered, while 55 patients died (mortality rate 37%). The area under the curve of CT score for discriminating of recovered patients from deceased individuals was 0.726, and the optimal CT score threshold was 15.5 with 61.8% sensitivity and 76.3% specificity. The best CT score cut-off for discriminating of patients based on the severity of disease was 12.5 with 68.3% sensitivity and 72.7% specificity. In addition, with CT score cut-off of 15.5, sensitivities of 70.8% and 51.6% and specificities of 78% and 72.6% were observed for intubation and ICU admission, respectively.

Conclusion

CT scan and semiquantitative scoring method could be beneficial and applicable in predicting the patient’s condition.

Indications for imaging studies in SARS-CoV-2 infected patients - recommendations of the Polish Medical Society of Radiology

The pandemic involving COVID-19 caused by the SARS-CoV-2 coronavirus, due to its severe symptoms and high transmission rate, has gone on to pose a control challenge for healthcare systems all around the world. We present the third version of the recommendations of the Polish Medical Society of Radiology (PMSR), presuming that our knowledge on COVID-19 will advance further rapidly, to the extent that further supplementation and modification will prove necessary. These recommendations involve rules of conduct, procedures, and safety measures that should be introduced in radiology departments, as well as indications for imaging studies.

WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments

The coronavirus disease 2019 (COVID-19) pandemic has caused a major outbreak around the world with severe impact on health, human lives, and economy globally. One of the crucial steps in fighting COVID-19 is the ability to detect infected patients at early stages and put them under special care. Detecting COVID-19 from radiography images using computational medical imaging method is one of the fastest ways to diagnose the patients. However, early detection with significant results is a major challenge, given the limited available medical imaging data and conflicting performance metrics. Therefore, this work aims to develop a novel deep learning-based computationally efficient medical imaging framework for effective modeling and early diagnosis of COVID-19 from chest x-ray and computed tomography images. The proposed work presents "WEENet" by exploiting efficient convolutional neural network to extract high-level features, followed by classification mechanisms for COVID-19 diagnosis in medical image data. The performance of our method is evaluated on three benchmark medical chest x-ray and computed tomography image datasets using eight evaluation metrics including a novel strategy of cross-corpse evaluation as well as robustness evaluation, and the results are surpassing state-of-the-art methods. The outcome of this work can assist the epidemiologists and healthcare authorities in analyzing the infected medical chest x-ray and computed tomography images, management of the COVID-19 pandemic, bridging the early diagnosis, and treatment gap for Internet of Medical Things environments.

Changing legislative thinking in China to better protect wild animals and human health

The COVID-19 pandemic has exposed the inadequacy of China's legal protection of wildlife. Long-standing illegal wildlife trade in China and worldwide increases the risk of zoonotic infectious diseases. We sought to improve the understanding of China' legal system for the protection of wildlife, which has at its core the wildlife protection law, by systematically evaluating the laws and regulations of China's Wildlife Protection Framework. We examined how existing legal documents (e.g., the Wildlife Protection Law 2018) are directly or indirectly related to wildlife conservation. The inherent defects of wildlife protection legislation include a narrow scope of protection, insufficient public participation, and inconsistent enforcement among responsible agencies. Solutions to improve China's Wildlife Protection Law include expanding the legal protection of wildlife, and improving monitoring of wildlife protection. Strengthening legislation will be the basis for effective regulation of the use of wild animals. We advocate the establishment of a sound wildlife protection legal system for resolving conflicts between humans and wild animals and preventing zoonotic disease, such a system will have a profound impact on the sustainable development of China's wildlife resources.

Specific delivering of RNAi using Spike's aptamer-functionalized lipid nanoparticles for targeting SARS-CoV-2: A strong anti-Covid drug in a clinical case study

Coronavirus (SARS-CoV-2) as a global pandemic has attracted the attention of many scientific centers to find the right treatment. We expressed and purified the recombinant receptor-binding domain (RBD) of the SARS-CoV-2 spike (S) protein, and specific RBD aptamers were designed using SELEX method. RNAi targeting nucleocapsid phosphoprotein was synthesized and human lung cells were inoculated with aptamer-functionalized lipid nanoparticles (LNPs) containing RNAi. The results demonstrated that RBD aptamer having KD values of 0.290 nm possessed good affinity. Based on molecular docking and efficacy prediction analysis, siRNA molecule was showed the best action. LNPs were appropriately functionalized by aptamer and contained RNAi molecules. Antiviral assay using q-PCR and ELISA demonstrated that LNP functionalized with 35 µm Apt and containing 30 nm RNAi/ml of cell culture had the best antiviral activity compared to other concentrations. Applied aptamer in the nanocarrier has two important functions. First, it can deliver the drug (RNAi) to the surface of epithelial cells. Second, by binding to the SARS-CoV-2 spike protein, it inhibits the virus entrance into cells. Our data reveal an interaction between the aptamer and the virus, and RNAi targeted the virus RNA. CT scan and the clinical laboratory tests in a clinical case study, a 36-year old man who presented with severe SARS-CoV-2, demonstrated that inhalation of 10 mg Apt-LNPs-RNAi nebulized/day for six days resulted in an improvement in consolidation and ground-glass opacity in lungs on the sixth day of treatment. Our findings suggest the treatment of SARS-CoV-2 infection through inhalation of Aptamer-LNPs-RNAi.

Early identification of novel coronavirus (COVID-19) pneumonia using clinical and radiographic findings

Introduction

The Japanese Respiratory Society (JRS) scoring system is a useful tool for identifying Mycoplasma pneumoniae pneumonia. Most COVID-19 pneumonia in non-elderly patients (aged <60 years) are classified as atypical pneumonia using the JRS scoring system. We evaluated whether physicians could distinguish between COVID-19 pneumonia and M. pneumoniae pneumonia using chest computed tomography (CT) findings. In addition, we investigated chest CT findings if there is a difference between the variant and non-variant strain.

Methods

This study was conducted at five institutions and assessed a total of 823 patients with COVID-19 pneumonia (335 had lineage B.1.1.7.) and 100 patients with M. pneumoniae pneumonia.

Results

In COVID-19 pneumonia, at the first CT examination, peripheral, bilateral ground-glass opacity (GGO) with or without consolidation or crazy-paving pattern was observed frequently. GGO frequently had a round morphology (39.2%). No differences were observed in the radiological findings between the non-B.1.1.7 groups and B.1.1.7 groups. The frequency of pleural effusion, lymphadenopathy, bronchial wall thickening and nodules (tree-in-bud and centrilobular) was low. In contrast to COVID-19 pneumonia, bronchial wall thickening (84%) was observed most frequently, followed by nodules (81%) in M. pneumoniae pneumonia. These findings were significantly higher in M. pneumoniae pneumonia than COVID-19 pneumonia.

Conclusions

Our results demonstrated that a combination of the JRS scoring system and chest CT findings is useful for the rapid presumptive diagnosis of COVID-19 pneumonia in patients aged <60 years. However, this clinical and radiographic diagnosis is not adapted to elderly people.

Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images

Segmentation of infections from CT scans is important for accurate diagnosis and follow-up in tackling the COVID-19. Although the convolutional neural network has great potential to automate the segmentation task, most existing deep learning-based infection segmentation methods require fully annotated ground-truth labels for training, which is time-consuming and labor-intensive. This paper proposed a novel weakly supervised segmentation method for COVID-19 infections in CT slices, which only requires scribble supervision and is enhanced with the uncertainty-aware self-ensembling and transformation-consistent techniques. Specifically, to deal with the difficulty caused by the shortage of supervision, an uncertainty-aware mean teacher is incorporated into the scribble-based segmentation method, encouraging the segmentation predictions to be consistent under different perturbations for an input image. This mean teacher model can guide the student model to be trained using information in images without requiring manual annotations. On the other hand, considering the output of the mean teacher contains both correct and unreliable predictions, equally treating each prediction in the teacher model may degrade the performance of the student network. To alleviate this problem, the pixel level uncertainty measure on the predictions of the teacher model is calculated, and then the student model is only guided by reliable predictions from the teacher model. To further regularize the network, a transformation-consistent strategy is also incorporated, which requires the prediction to follow the same transformation if a transform is performed on an input image of the network. The proposed method has been evaluated on two public datasets and one local dataset. The experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.

Study to assess aetiology, clinical and imaging characteristics of post Covid-19 pulmonary cavitation

Objective

The aim of this study is to determine the aetiology and characteristics of pulmonary cavities that developed in patients recovering from COVID-19 infection.

Materials and Methods

Between 1st May 2021 and 30st June 2021, we found 9 post COVID-19 patients who developed lung cavities on chest radiograph or CT during the follow-up period. These patients underwent routine blood examination, sputum examination and bronchoscopy to identify the aetiologies for the lung cavities.

Results

The duration from the onset of COVID-19 symptoms to the detection of lung cavities ranged from 18 to 82 days. Out of 7 patients, 4 had recovered from severe COVID-19 disease, 2 from moderate and 1 from mild disease. After the diagnostic workup, 5 patients were found to have COVID-19 associated pulmonary aspergillosis (CAPA), 1 patient with mucormycosis and 1 patient with mycobacterium infection. Two patients with CAPA also had bacterial infection; sputum culture from both these patients grew Klebsiella pneumonia.

Conclusion

Lung cavities can develop in patients recovering from COVID-19 pneumonia and fungal infection is the most common cause for such cavities.

Pulmonary Sequelae in Patients After Recovery From Coronavirus Disease 2019: A Follow-Up Study With Chest CT

Objective: The pulmonary sequelae of coronavirus disease 2019 (COVID-19) have not been comprehensively evaluated. We performed a follow-up study analyzing chest computed tomography (CT) findings of COVID-19 patients at 3 and 6 months after hospital discharge. Methods: Between February 2020 and May 2020, a total of 273 patients with COVID-19 at the Shenzhen Third People's Hospital were recruited and followed for 6 months after discharge. Chest CT scanning was performed with the patient in the supine position at end-inspiration. A total of 957 chest CT scans was obtained at different timepoints. A semi-quantitative score was used to assess the degree of lung involvement. Results: Most chest CT scans showed bilateral lung involvement with peripheral location at 3 and 6 months follow-up. The most common CT findings were ground-glass opacity and parenchymal band, which were found in 136 (55.3%) and 94 (38.2%) of the 246 patients at 3 months follow-up, and 82 (48.2%) and 76 (44.7%) of 170 patients at 6 months follow-up, respectively. The number of lobes involved and the total CT severity score declined over time. The total CT score gradually increased with the increasement of disease severity at both 3 months follow-up (trend test P < 0.001) and 6 months follow-up (trend test P < 0.001). Patients with different disease severity represented diverse CT patterns over time. Conclusions: The most common CT findings were ground-glass opacity and parenchymal bands at the 3 and 6 months follow-up. Patients with different disease severity represent diverse CT manifestations, indicating the necessary for long-term follow-up monitoring of patients with severe and critical conditions.

Clinical Symptoms of COVID-19 and Their Association with Disease Outcome

BACKGROUND

COVID-19 pandemic placed immense pressure on health systems. The current study aimed to evaluate the symptoms of COVID-19 in Isfahan province and their association with disease outcome.

MATERIALS AND METHODS

In this cross-sectional study, which was conducted on patients with a definite diagnosis of COVID-19, as a part of the investigations performed by the Deputy for Public Health of the Isfahan University of Medical Sciences, the frequency of underlying diseases, and general, respiratory, gastrointestinal, neurological, renal, cardiac, dermal, hearing symptoms are assessed using a checklist. The participants were followed up 1 month after definitive diagnosis, and the outcome of the disease (recovery or death) was recorded.

RESULTS

Of 300 patients, 143 (47.4%) were male and 157 (52.6%) were female. The mean age of patients was 43 ± 17.84 years. Muscle pain and fatigue were the most common early symptoms (63% and 60.3%, respectively). Age, sex, level of education, and occupation of patients and general, respiratory, and gastrointestinal symptoms and underlying diseases of respiratory disease showed a statistically significant association with the disease outcome (P < 0.05).

CONCLUSION

General, respiratory, and gastrointestinal symptoms were associated with an increased risk of death due to COVID-19. General, visual, and hearing symptoms, and diabetes, cardiovascular diseases, and respiratory disease had a statistically significant association with the hospitalization of patients.

Evaluating the Association Between Comorbidities and COVID-19 Severity Scoring on Chest CT Examinations Between the Two Waves of COVID-19: An Imaging Study Using Artificial Intelligence

Background

Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS).

Methodology

This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis.

Results

The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis.

Conclusions

This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous.

Cardiovascular Risk After SARS-CoV-2 Infection Is Mediated by IL18/IL18R1/HIF-1 Signaling Pathway Axis

Objectives

Currently, cardiovascular risk associated with COVID-19 has been brought to people's attention, but the mechanism is not clear. The aim of this study is to elucidate the mechanisms based on multiple omics data.

Methodology

Weighted gene co-expression network analysis (WGCNA) was used to identify key pathways. Combination analysis with aneurysm and atherosclerosis related pathways, hypoxia induced factor-1 (HIF-1) signaling were identified as key pathways of the increased cardiovascular risk associated with COVID-19. ScMLnet algorithm based on scRNA-seq was used to explore the regulation of HIF-1 pathway by intercellular communication. Proteomic analysis was used to detect the regulatory mechanisms between IL18 and HIF-1 signaling pathway. Pseudo time locus analysis was used to study the regulation of HIF1 signaling pathway in macrophages and vascular smooth muscle cells (VSMC) phenotypic transformation. The Virtual Inference of protein-activity by Enriched Regulon (VIPER) analysis was used to study the activity of regulatory proteins. Epigenetic analysis based on methylation revealed epigenetic changes in PBMC after SARS-CoV-2 infection. Potential therapeutic compounds were explored by using Cmap algorithm.

Results

HIF-1 signaling pathway is a common key pathway for aneurysms, atherosclerosis and SARS-CoV-2 infection. Intercellular communication analysis showed that macrophage-derived interleukin-18 (IL-18) activates the HIF-1 signaling pathway through IL18R1. Proteomic analysis showed that IL18/IL18R1 promote NF-κB entry into the nucleus, and activated the HIF-1 signaling pathway. Macrophage-derived IL18 promoted the M1 polarization of macrophages and the syntactic phenotype transformation of VSMCs. MAP2K1 mediates the functional regulation of HIF-1 signaling pathway in various cell types. Epigenetic changes in PBMC after COVID-19 infection are characterized by activation of the type I interferon pathway. MEK inhibitors are the promising compounds for the treatment of HIF-1 overactivation.

Conclusions

The IL18/IL18R1/HIF1A axis is expected to be an therapeutic target for cardiovascular protection after SARS-CoV-2 infection. MEK inhibitors may be an choice for cardiovascular protection after SARS-COV-2 infection.

Serum Lactate Dehydrogenase Level as a Prognostic Factor for COVID-19: A Retrospective Study Based on a Large Sample Size

Background: In this study, we investigated the relationship between serum lactate dehydrogenase (LDH) level and disease progression and prognosis of patients with COVID-19.

Methods: We retrospectively reviewed the information of 1,751 patients with COVID-19 from Leishenshan Hospital in Wuhan, China. Univariate and multivariate Cox regression analyses as well as Logistics regression analyses, and Kaplan-Meier curves were used to determine the association between LDH levels and the prognosis of COVID-19 patients.

Results: LDH was an independent risk factor for in-hospital death no matter it was taken as classified variable and continuous variable (all P = 0.001) but not for severe or critical illness status. The Kaplan-Meier curves for LDH level showed that an elevated level of LDH was associated with in-hospital death.

Conclusions: In patients with COVID-19, the increased LDH level is associated with a higher risk of negative clinical prognosis and higher mortality. This will provide a reference for clinicians and researchers to understand, diagnose, and treat patients with COVID-19. Further prospective studies with larger sample sizes are needed to verify these findings.

The characteristics of overseas imported COVID-19 cases and the effectiveness of screening strategies in Beijing, China

BACKGROUND

In March 2020, the WHO declared the novel coronavirus outbreak a global pandemic. While great success in coronavirus disease 2019 (COVID-19) control has been achieved in China, imported cases have become a major challenge. This study aimed to describe the epidemiological and clinical characteristics of imported COVID-19 cases and to assess the effectiveness of screening strategies in Beijing, China.

METHODS

This retrospective study included all imported cases transferred to Beijing Ditan Hospital from 29 February to 20 March 2020 who were screened by both chest computed tomography (CT) and reverse-transcriptase-polymerase chain reaction (RT-PCR) at the initial presentation. Demographic, clinical and laboratory data, in addition to chest CT imaging, were collected and analysed.

RESULTS

In total, 2545 cases were included, among which 71 (2.8%) were finally diagnosed with laboratory-confirmed COVID-19. The majority 63 (88.7%) were from Europe. The most common initial symptoms were cough and fever, which accounted for 49.3% and 42.3%, respectively. Only four cases (5.6%) had lymphocytopenia, and thirteen cases (18.3%) demonstrated elevated levels of C-reactive protein (CRP). All cases had normal serum levels of procalcitonin (PCT). At initial presentation, among the 71 confirmed cases, 59 (83.1%) had a positive RT-PCR assay, and 35 (49.3%) had a positive chest CT. Twelve (16.9%) had a negative RT-PCR assay but a positive chest CT.

CONCLUSIONS

A combination of RT-PCR and chest CT is an effective strategy for the screening of imported COVID-19 cases. Our findings provide important information and clinical evidence about the infection control of imported COVID-19 cases.

Optimization of Single-Dose VSV-Based COVID-19 Vaccination in Hamsters

The ongoing COVID-19 pandemic has resulted in global effects on human health, economic stability, and social norms. The emergence of viral variants raises concerns about the efficacy of existing vaccines and highlights the continued need for the development of efficient, fast-acting, and cost-effective vaccines. Here, we demonstrate the immunogenicity and protective efficacy of two vesicular stomatitis virus (VSV)-based vaccines encoding the SARS-CoV-2 spike protein either alone (VSV-SARS2) or in combination with the Ebola virus glycoprotein (VSV-SARS2-EBOV). Intranasally vaccinated hamsters showed an early CD8+ T cell response in the lungs and a greater antigen-specific IgG response, while intramuscularly vaccinated hamsters had an early CD4+ T cell and NK cell response. Intranasal vaccination resulted in protection within 10 days with hamsters not showing clinical signs of pneumonia when challenged with three different SARS-CoV-2 variants. This data demonstrates that VSV-based vaccines are viable single-dose, fast-acting vaccine candidates that are protective from COVID-19.

The use of ultrasound in establishing COVID-19 infection as part of a trauma evaluation

Purpose

The use of lung ultrasound for diagnosis of COVID-19 has emerged during the pandemic as a beneficial diagnostic modality due to its rapid availability, bedside use, and lack of radiation. This study aimed to determine if routine ultrasound (US) imaging of the lungs of trauma patients with COVID-19 infections who undergo extended focused assessment with sonography for trauma (EFAST) correlates with computed tomography (CT) imaging and X-ray findings, as previously reported in other populations.

Methods

This was a prospective, observational feasibility study performed at two level 1 trauma centers. US, CT, and X-ray imaging were retrospectively reviewed by a surgical trainee and a board-certified radiologist to determine any correlation of imaging findings in patients with active COVID-19 infection.

Results

There were 53 patients with lung US images from EFAST available for evaluation and COVID-19 testing. The overall COVID-19 positivity rate was 7.5%. COVID-19 infection was accurately identified by one patient on US by the trainee, but there was a 15.1% false-positive rate for infection based on the radiologist examination.

Conclusions

Evaluation of the lung during EFAST cannot be used in the trauma setting to identify patients with active COVID-19 infection or to stratify patients as high or low risk of infection. This is likely due to differences in lung imaging technique and the presence of concomitant thoracic injury.

Empowering the willing: the feasibility of tele-mentored self-performed pleural ultrasound assessment for the surveillance of lung health

Background

SARS-CoV-2 infection, manifesting as COVID-19 pneumonia, constitutes a global pandemic that is disrupting health-care systems. Most patients who are infected are asymptomatic/pauci-symptomatic can safely self-isolate at home. However, even previously healthy individuals can deteriorate rapidly with life-threatening respiratory failure characterized by disproportionate hypoxemic failure compared to symptoms. Ultrasound findings have been proposed as an early indicator of progression to severe disease. Furthermore, ultrasound is a safe imaging modality that can be performed by novice users remotely guided by experts. We thus examined the feasibility of utilizing common household informatic-technologies to facilitate self-performed lung ultrasound.

Methods

A lung ultrasound expert remotely mentored and guided participants to image their own chests with a hand-held ultrasound transducer. The results were evaluated in real time by the mentor, and independently scored by three independent experts [planned a priori]. The primary outcomes were feasibility in obtaining good-quality interpretable images from each anatomic location recommended for COVID-19 diagnosis.

Results

Twenty-seven adults volunteered. All could be guided to obtain images of the pleura of the 8 anterior and lateral lung zones (216/216 attempts). These images were rated as interpretable by the 3 experts in 99.8% (647/648) of reviews. Fully imaging one’s posterior region was harder; only 108/162 (66%) of image acquisitions was possible. Of these, 99.3% of images were interpretable in blinded evaluations. However, 52/54 (96%) of participants could image their lower posterior lung bases, where COVID-19 is most common, with 99.3% rated as interpretable.

Conclusions

Ultrasound-novice adults at risk for COVID-19 deterioration can be successfully mentored using freely available software and low-cost ultrasound devices to provide meaningful lung ultrasound surveillance of themselves that could potentially stratify asymptomatic/paucisymptomatic patients with early risk factors for serious disease. Further studies examining practical logistics should be conducted.

Trial Registration: ID ISRCTN/77929274 on 07/03/2015.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13089-021-00250-6.

Integrated Nutritional Supports for Diabetic Patients During COVID-19 Infection: A Comprehensive Review

BACKGROUND

Diabetes mellitus is an endocrine metabolic disorder, which affects the major organs in human and comorbid with others. Besides, diabetic patients are more prone to various infectious diseases as well as COVID-19 sporadic infection which is a high risk for patients with diabetes mellitus. To combat these infections and comorbid situations, an integrated balanced nutritional supportive could help in maintaining sound health and increase immunity for prevention and management of such type of viral infections.

OBJECTIVES

While information regarding nutritional supports in COVID-19 pandemic in diabetic patients is not available, this review aimed to accumulate the evidence from previous publications where studied about nutrition-based supports or interventions for viral diseases with special emphasis on respiratory infections.

METHODS

For reviewing, searches are done for getting journal articles into Google Scholar, Pub Med/Medline, Database of Open Access Journal and Science Direct for relevant data and information.

RESULTS

Integrated nutritional supports of both macronutrients and micronutrients guidelines, including home-based physical exercise schedule, is summarized in this comprehensive review for possible prevention and management of diabetic patients in COVID-19 infections. The immuneboosting benefits of some vitamins, trace elements, nutraceuticals and probiotics in viral infections of diabetic patients are also included.

CONCLUSION

There is an urgent need for a healthy diet and integrated nutritional supports with home-based physical activities for diabetic patients during the self-isolation period of COVID-19 Infection.

Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

[Influence of therapeutic exercises and hardware massage in electrostatic field on lung damage in patients with novel coronavirus pneumonia]

OBJECTIVE

To analyze the efficacy and safety of therapeutic exercises and chest hardware massage in electrostatic field in patients with COVID-associated viral pneumonia.

MATERIAL AND METHODS

We retrospectively analyzed 1551 patients admitted to the Clinical Hospital No. 1 (MEDSI Group JSC) with COVID-associated pneumonia between April 01, 2020 and June 15, 2021 (ICD-10 U07.1 and U07.2). Considering inclusion and exclusion criteria, we enrolled 153 patients. All patients were divided into comparable groups and subgroups depending on the methods of rehabilitation treatment and CT stage of viral pneumonia. Lung damage was assessed semi-automatically using Philips Portal v11 COPD software. Rehabilitation measures included therapeutic exercises and chest hardware massage in electrostatic field. therapeutic exercises.

RESULTS

Therapeutic exercises significantly reduced severity of lung damage in patients with viral pneumonia CT-2 and no oxygen support (from 28.05% [28; 29.5] at admission to 15.3% [14.2; 19.3] at discharge). It was not observed in patients without rehabilitation treatment and in patients undergoing therapeutic exercises and massage in electrostatic field.

CONCLUSION

Therapeutic exercises in patients with COVID-19 and baseline lung damage > 25% and < 50% (CT-2 stage) significantly reduce severity of lung damage at discharge compared to the control group.

Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation

Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a novel domain adaptation (DA) method with two components to address these problems. The first component is a stochastic class-balanced boosting sampling strategy that overcomes the imbalanced learning problem and improves the classification performance on poorly-predicted classes. The second component is a representation learning that guarantees three properties: 1) domain-transferability by prototype triplet loss, 2) discriminant by conditional maximum mean discrepancy loss, and 3) completeness by multi-view reconstruction loss. Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i.e., class centers) in a hyper-sphere manifold. Experiments on cross-site severity assessment of COVID-19 from CT images show that the proposed method can effectively tackle the imbalanced learning problem and outperform recent DA approaches.

"Serial ferritin titer" monitoring in COVID-19 pneumonia: valuable inflammatory marker in assessment of severity and predicting early lung fibrosis - prospective, multicentric, observational, and interventional study in tertiary care setting in India

Introduction

The COVID-19 pneumonia is a heterogeneous disease with variable effect on lung parenchyma, airways, and vasculature leading to long-term effects on lung functions.

Materials and methods

Multicentric, prospective, observational, and interventional study conducted during July 2020 to May 2021, in the MIMSR Medical College and Venkatesh Hospital Latur India, included 1000 COVID-19 cases confirmed with RT-PCR. All cases were assessed with lung involvement documented and categorized on HRCT thorax, oxygen saturation, inflammatory marker, ferritin at entry point, and follow-up during hospitalization. Age, gender, comorbidity, and use of BIPAP/NIV and outcome as with or without lung fibrosis as per CT severity were key observations. CT severity scoring is done as per universally accepted standard scoring tool as score < 7 as mild, 7–14 as moderate, and score > 15 as severe affection of the lung. Statistical analysis is done by using chi-square test.

Observations and analysis

In study of 1000 COVID-19 pneumonia cases, age (< 50 and > 50 years) and gender (male versus female) have significant association with ferritin in predicting severity of COVID-19 pneumonia (p < 0.00001) and (p < 0.010), respectively. CT severity score at entry point with ferritin level has significant correlation in severity scores < 8, 8–15, and > 15 documented in normal and abnormal ferritin level as in 190/110, 90/210, and 40/360, respectively (p < 0.00001). Ferritin level has significant association with duration of illness, i.e., DOI < 7 days, 8–15 days, and > 15 days of onset of symptoms documented normal and abnormal ferritin levels in 30/310, 160/300, and 130/70 cases, respectively (p < 0.00001). Comorbidity as diabetes mellitus, hypertension, COPD, IHD, and obesity has significant association in COVID-19 cases with normal and abnormal ferritin level respectively (p < 0.00001). Ferritin level has significant association with oxygen saturation in COVID-19 pneumonia cases; cases with oxygen saturation > 90%, 75–90%, and < 75% are observed as normal and abnormal ferritin level in 110/100, 150/340, and 60/240 cases, respectively (p < 0.00001). BIPAP/NIV requirement during the course of COVID-19 pneumonia in critical care setting has significant association with ferritin level; cases received BIPAP/NIV during hospitalization were documented normal and abnormal ferritin level in 155/445 and 165/235 cases, respectively (p < 0.00001). Timing of BIPAP/NIV requirement during course of COVID-19 pneumonia in critical care setting has significant association with ferritin level; cases received BIPAP/NIV at entry point < 1 day, 3–7 days, and after 7 days of hospitalization were documented significance in fourfold raised ferritin level in 110/70, 150/160, and 30/80 cases, respectively (p < 0.00001). Follow-up of ferritin titer during hospitalization as compared to entry point abnormal ferritin has significant association in post-COVID lung fibrosis (p < 0.00001). Follow-up of ferritin titer during hospitalization as compared to entry point normal ferritin has significant association in post-COVID lung fibrosis (p < 0.00001).

Conclusion

Ferritin is easily available, sensitive and reliable, cost-effective, and universally acceptable inflammatory marker in COVID-19 pandemic. Ferritin has very crucial role in COVID-19 pneumonia in predicting severity of illness and assessing response to treatment during hospitalization. Follow-up of ferritin titer during hospitalization and at discharge can be used as early predictor of post-COVID lung fibrosis.

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.

SARS, MERS and CoVID-19: An overview and comparison of clinical, laboratory and radiological features

In the 21st century, we have seen a total of three outbreaks by members of the coronavirus family. Although the first two outbreaks did not result in a pandemic, the third and the latest outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) culminated in a pandemic. This pandemic has been extremely significant on a social and international level. As these viruses belong to the same family, they are closely related. Despite their numerous similarities, they have slight distinctions that render them distinct from one another. The Severe Acute Respiratory Distress Syndrome and Middle East Respiratory Syndrome (MERS) cases were reported to have a very high case fatality rate of 9.5 and 34.4% respectively. In contrast, the CoVID-19 has a case fatality rate of 2.13%. Also, there are no clear medical countermeasures for these coronaviruses yet. We can cross information gaps, including cultural weapons for fighting and controlling the spread of MERS-CoV and SARS-CoV-2, and plan efficient and comprehensive defensive lines against coronaviruses that might arise or reemerge in the future by gaining a deeper understanding of these coronaviruses and the illnesses caused by them. The review thoroughly summarises the state-of-the-art information and compares the biochemical properties of these deadly coronaviruses with the clinical characteristics, laboratory features and radiological manifestations of illnesses induced by them, with an emphasis on comparing and contrasting their similarities and differences.

Weakly-supervised lesion analysis with a CNN-based framework for COVID-19

Objective.Lesions of COVID-19 can be clearly visualized using chest CT images, and hence provide valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions.Approach.A deep learning-based diagnosis branch is employed for classification of the CT image and then a lesion identification branch is leveraged to capture multiple types of lesions.Main Results.Our framework is verified on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation.Significance.The proposed approach integrates COVID-19 positive diagnosis and lesion analysis into a unified framework without extra pixel-wise supervision. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.

COVID-19 pandemic in flu season. Chest computed tomography - what we know so far

Chest computed tomography (CT) is proven to have high sensitivity in COVID-19 diagnosis. It is available in most emergency wards, and in contrast to polymerase chain reaction (PCR) it can be obtained in several minutes. However, its imaging features change during the course of the disease and overlap with other viral pneumonias, including influenza pneumonia. In this brief analysis we review the recent literature about chest CT features, useful radiological scales, and COVID-19 differentiation with other viral infections.

Clustering cases of Chlamydia psittaci pneumonia mimicking COVID-19 pneumonia

BACKGROUND

The onset symptoms of people infected by Chlamydia psittaci can mimic the coronavirus disease 2019 (COVID-19). However, the differences in laboratory tests and imaging features between psittacosis and COVID-19 remain unknown.

AIM

To better understand the two diseases and then make an early diagnosis and treatment.

METHODS

Six patients from two institutions confirmed as psittacosis by high-throughput genetic testing and 31 patients confirmed as COVID-19 were retrospectively included. The epidemiology, clinical characteristics, laboratory tests and computed tomography (CT) imaging features were collected and compared between the two groups. The follow-up CT imaging findings of patients with psittacosis were also investigated.

RESULTS

The white blood cell count (WBC), neutrophil count and calcium were more likely to be decreased in patients with COVID-19 but were increased in patients with psittacosis (all P = 0.000). Lymphocyte count and platelet count were higher in patients with psittacosis than in those with COVID-19 (P = 0.044, P = 0.035, respectively). Lesions in patients with psittacosis were more likely to be unilateral (P = 0.001), involve fewer lung lobes (P = 0.006) and have pleural effusions (P = 0.002). Vascular enlargement was more common in patients with COVID-19 (P = 0.003). Consolidation in lung CT images was absorbed in all 6 patients.

CONCLUSION

Psittacosis has the potential for human-to-human transmission. Patients with psittacosis present increased WBC count and neutrophil count and have specific CT imaging findings, including unilateral distribution, less involvement of lung lobes and pleural effusions, which might help us to differentiate it from COVID-19.