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.

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.

Recent Advancements on COVID-19: A Comprehensive Review

Over the last few decades, there have been several global outbreaks of severe respiratory infections. The causes of these outbreaks were coronaviruses that had infected birds, mammals and humans. The outbreaks predominantly caused respiratory tract and gastrointestinal tract symptoms and other mild to very severe clinical signs. The current coronavirus disease-2019 (COVID-19) outbreak, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a rapidly spreading illness affecting millions of people worldwide. Among the countries most affected by the disease are the United States of America (USA), India, Brazil, and Russia, with France recording the highest infection, morbidity, and mortality rates. Since early January 2021, thousands of articles have been published on COVID-19. Most of these articles were consistent with the reports on the mode of transmission, spread, duration, and severity of the sickness. Thus, this review comprehensively discusses the most critical aspects of COVID-19, including etiology, epidemiology, pathogenesis, clinical signs, transmission, pathological changes, diagnosis, treatment, prevention and control, and vaccination.

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.

Sequential Organ Failure Assessment Outperforms Quantitative Chest CT Imaging Parameters for Mortality Prediction in COVID-19 ARDS

(1) Background: Respiratory insufficiency with acute respiratory distress syndrome (ARDS) and multi-organ dysfunction leads to high mortality in COVID-19 patients. In times of limited intensive care unit (ICU) resources, chest CTs became an important tool for the assessment of lung involvement and for patient triage despite uncertainties about the predictive diagnostic value. This study evaluated chest CT-based imaging parameters for their potential to predict in-hospital mortality compared to clinical scores. (2) Methods: 89 COVID-19 ICU ARDS patients requiring mechanical ventilation or continuous positive airway pressure mask ventilation were included in this single center retrospective study. AI-based lung injury assessment and measurements indicating pulmonary hypertension (PA-to-AA ratio) on admission CT, oxygenation indices, lung compliance and sequential organ failure assessment (SOFA) scores on ICU admission were assessed for their diagnostic performance to predict in-hospital mortality. (3) Results: CT severity scores and PA-to-AA ratios were not significantly associated with in-hospital mortality, whereas the SOFA score showed a significant association (p < 0.001). In ROC analysis, the SOFA score resulted in an area under the curve (AUC) for in-hospital mortality of 0.74 (95%-CI 0.63–0.85), whereas CT severity scores (0.53, 95%-CI 0.40–0.67) and PA-to-AA ratios (0.46, 95%-CI 0.34–0.58) did not yield sufficient AUCs. These results were consistent for the subgroup of more critically ill patients with moderate and severe ARDS on admission (oxygenation index <200, n = 53) with an AUC for SOFA score of 0.77 (95%-CI 0.64–0.89), compared to 0.55 (95%-CI 0.39–0.72) for CT severity scores and 0.51 (95%-CI 0.35–0.67) for PA-to-AA ratios. (4) Conclusions: Severe COVID-19 disease is not limited to lung (vessel) injury but leads to a multi-organ involvement. The findings of this study suggest that risk stratification should not solely be based on chest CT parameters but needs to include multi-organ failure assessment for COVID-19 ICU ARDS patients for optimized future patient management and resource allocation.

The radiological findings of COVID-19

Background/aim

Available information on the radiological findings of the 2019 novel coronavirus disease (COVID-19) is constantly updated. Ground glass opacities (GGOs) and consolidation with bilateral and peripheral distribution have been reported as the most common CT findings, but less typical features can also be identified. According to the reported studies, SARS-CoV-2 infection is not limited to the respiratory system, and it can also affect other organs. Renal dysfunction, gastrointestinal complications, liver dysfunction, cardiac manifestations, and neurological abnormalities are among the reported extrapulmonary features. This review aims to provide updated information for radiologists and all clinicians to better understand the radiological manifestations of COVID-19.

Materials and methods

Radiological findings observed in SARS-CoV-2 virus infections were explored in detail in PubMed and Google Scholar databases.

Results

The typical pulmonary manifestations of COVID-19 pneumonia were determined as GGOs and accompanying consolidations that primarily involve the periphery of the bilateral lower lobes. The most common extrapulmonary findings were increased resistance to flow in the kidneys, thickening of vascular walls, fatty liver, pancreas, and heart inflammation findings. However, these findings were not specific and significantly overlapped those caused by other viral diseases, and therefore alternative diagnoses should be considered in patients with negative diagnostic tests.

Conclusion

Radiological imaging plays a supportive role in the care of patients with COVID-19. Both clinicians and radiologists need to know associated pulmonary and extrapulmonary findings and imaging features to help diagnose and manage the possible complications of the disease at an early stage. They should also be familiar with CT findings in patients with COVID-19 since the disease can be incidentally detected during imaging performed with other indications.

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

This paper proposes a wavelet and artificial intelligence-enabled rapid and efficient testing procedure for patients with Severe Acute Respiratory Coronavirus Syndrome (SARS-nCoV) through a deep learning approach from thoracic X-ray images. Presently, the virus infection is diagnosed primarily by a process called the real-time Reverse Transcriptase-Polymerase Chain Reaction (rRT-PCR) based on its genetic prints. This whole procedure takes a substantial amount of time to identify and diagnose the patients infected by the virus. The proposed research uses a wavelet-based convolution neural network architectures to detect SARS-nCoV. CNN is pre-trained on the ImageNet and trained end-to-end using thoracic X-ray images. To execute Discrete Wavelet Transforms (DWT), the available mother wavelet functions from different families, namely Haar, Daubechies, Symlet, Biorthogonal, Coiflet, and Discrete Meyer, were considered. Two-level decomposition via DWT is adopted to extract prominent features peripheral and subpleural ground-glass opacities, often in the lower lobes explicitly from thoracic X-ray images to suppress noise effect, further enhancing the signal to noise ratio. The proposed wavelet-based deep learning models of both, two-class instances (COVID vs. Normal) and four-class instances (COVID-19 vs. PNA bacterial vs. PNA viral vs. Normal) were validated from publicly available databases using k-Fold Cross Validation (k-Fold CV) technique. In addition to these X-ray images, images of recent COVID-19 patients were further used to examine the model's practicality and real-time feasibility in combating the current pandemic situation. It was observed that the Symlet 7 approximation component with two-level manifested the highest test accuracy of 98.87%, followed by Biorthogonal 2.6 with an efficiency of 98.73%. While the test accuracy for Symlet 7 and Biorthogonal 2.6 is high, Haar and Daubechies with two levels have demonstrated excellent validation accuracy on unseen data. It was also observed that the precision, the recall rate, and the dice similarity coefficient for four-class instances were 98%, 98%, and 99%, respectively, using the proposed algorithm.

How Is the Risk of Major Sudden Infectious Epidemic Transmitted? A Grounded Theory Analysis Based on COVID-19 in China

The outbreak of a sudden infectious epidemic often causes serious casualties and property losses to the whole society. The COVID-19 epidemic that broke out in China at the end of December 2019, spread rapidly, resulting in large groups of confirmed diagnoses, and causing severe damage to China's society. This epidemic even now encompasses the globe. This paper takes the COVID-19 epidemic that has occurred in China as an example, the original data of this paper is derived from 20 Chinese media reports on COVID-19, and the grounded theory is used to analyze the original data to find the risk transmission rules of a sudden infectious epidemic. The results show that in the risk transmission of a sudden infectious epidemic, there are six basic elements: the risk source, the risk early warning, the risk transmission path, the risk transmission victims, the risk transmission inflection point, and the end of risk transmission. After a sudden infectious epidemic breaks out, there are three risk transmission paths, namely, a medical system risk transmission path, a social system risk transmission path, and a psychological risk transmission path, and these three paths present a coupling structure. These findings in this paper suggest that people should strengthen the emergency management of a sudden infectious epidemic by controlling of the risk source, establishing an efficient and scientific risk early warning mechanism and blocking of the risk transmission paths. The results of this study can provide corresponding policy implications for the emergency management of sudden public health events.

Acute respiratory distress syndrome in COVID-19: possible mechanisms and therapeutic management

COVID-19 pandemic is a serious concern in the new era. Acute respiratory distress syndrome (ARDS), and lung failure are the main lung diseases in COVID-19 patients. Even though COVID-19 vaccinations are available now, there is still an urgent need to find potential treatments to ease the effects of COVID-19 on already sick patients. Multiple experimental drugs have been approved by the FDA with unknown efficacy and possible adverse effects. Probably the increasing number of studies worldwide examining the potential COVID-19 related therapies will help to identification of effective ARDS treatment. In this review article, we first provide a summary on immunopathology of ARDS next we will give an overview of management of patients with COVID-19 requiring intensive care unit (ICU), while focusing on the current treatment strategies being evaluated in the clinical trials in COVID-19-induced ARDS patients.

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since of 2020. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

Prognostic impact of CT severity score in childhood cancer with SARS-CoV-2

Background

CT chest severity score (CTSS) is a semi-quantitative measure done to correlate the severity of the pulmonary involvement on the CT with the severity of the disease.

The objectives of this study are to describe chest CT criteria and CTSS of the COVID-19 infection in pediatric oncology patients, to find a cut-off value of CTSS that can differentiate mild COVID-19 cases that can be managed at home and moderate to severe cases that need hospital care.

A retrospective cohort study was conducted on 64 pediatric oncology patients with confirmed COVID-19 infection between 1 April and 30 November 2020. They were classified clinically into mild, moderate, and severe groups. CT findings were evaluated for lung involvement and CTSS was calculated and range from 0 (clear lung) to 20 (all lung lobes were affected).

Results

Overall, 89% of patients had hematological malignancies and 92% were under active oncology treatment. The main CT findings were ground-glass opacity (70%) and consolidation patches (62.5%). In total, 85% of patients had bilateral lung involvement, ROC curve  showed that the area under the curve of CTSS for diagnosing severe type was 0.842 (95% CI 0.737–0.948). The CTSS cut-off of 6.5 had 90.9% sensitivity and 69% specificity, with 41.7% positive predictive value (PPV) and 96.9% negative predictive value (NPV). According to the Kaplan–Meier analysis, mortality risk was higher in patients with CT score > 7 than in those with CTSS < 7.

Conclusion

Pediatric oncology patients, especially those with hematological malignancies, are more vulnerable to COVID-19 infection. Chest CT severity score > 6.5 (about 35% lung involvement) can be used as a predictor of the need for hospitalization.

Understanding Consumer Travel Behavior during COVID-19

Since the COVID-19 outbreak worldwide, the global tourism industry has taken a severe hit. To fully understand the impact of the pandemic on tourists’ travel behavior, an intercultural survey was carried out through a large-scale online questionnaire. This survey aims to determine whether cultural differences and different ages might play a role in tourists’ behavior during the COVID-19. Data collected from 942 respondents from mainland China and overseas through different age groups were subjected to data analysis. The results demonstrate cognition and consumer behavior differentiate culturally and significantly between different ages, which is highlighted when they choose travel modes, transportation, and companions. The implications of the study are also provided in the end.

Assessment of interobserver reliability and predictive values of CT semiquantitative and severity scores in COVID lung disease

Background

The coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and first reported in December 2019 at Wuhan, China, has since then progressed into an ongoing global pandemic. The primary organ targeted by the virus is the pulmonary system, leading to interstitial pneumonia and subsequent oxygen dependency and morbidity. Computed tomography (CT) has been used by various centers as an imaging modality for the assessment of severity of lung involvement in individuals. Two popular systems of scoring lung involvement on CT are CT semiquantitative score (SQ) and CT severity score (CT-SS), both of which assess extent of pulmonary involvement by interstitial pneumonia and are partly based upon subjective evaluation. Our cross-sectional observational study aims to assess the interobserver reliability of these scores, as well as to assess the statistical correlation between the respective CT scores to severity of clinical outcome.

Results

Both the SQ and CT-SS scores showed an excellent interobserver reliability (ICC 0.91 and 0.93, respectively, p < 0.05). The CT-SS was marginally more sensitive (99.2%) in detecting severe COVID pneumonia than SQ (86.5%). The positive predictive value of SQ (98.3%) is more than CT-SS (78%) for detecting severe disease. The similarity of interobserver reliability obtained for both scores reiterates the respective cutoff CT scores proposed by the above systems, as 18 for SQ and 19.5 for CT-SS.

Conclusion

Both the SQ and CT-SS scores display excellent interobserver reliability. The CT-SS was more sensitive in detecting severe COVID pneumonia and may thus be preferred over the SQ as an initial radiological tool in predicting severity of infection.

Ultra-low-dose chest CT protocol during the second wave of COVID-19 pandemic: a double-observer prospective study on 250 patients to evaluate its detection accuracy

Background

While the second wave of COVID-19 pandemic almost reached its climax, unfortunately, new viral strains are rapidly spreading, and numbers of infected young adults are rising. Consequently, chest high-resolution computed tomography (HRCT) demands are increasing, regarding patients’ screening, initial evaluation and follow up. This study aims to evaluate the detection accuracy of ultra-low-dose chest CT in comparison with the routine low-dose chest CT to reduce the irradiation exposure hazards.

Results

This study was prospectively conducted on 250 patients during the period from 15th December 2020 to 10th February 2021. All of the included patients were clinically suspected of COVID-19 infection. All patients were subjected to routine low-dose (45 mAs) and ultra-low-dose (22 mAs) chest CT examinations. Finally, all patients had confirmatory PCR swab tests and other dedicated laboratory tests. They included 149 males and 101 females (59.6%:40.4%). Their age ranged from 16 to 84 years (mean age 50 ± 34 SD). Patients were divided according to body weight; 104 patients were less than 80 kg, and 146 patients were more than 80 kg. HRCT findings were examined by two expert consultant radiologists independently, and data analysis was performed by other two expert specialist and consultant radiologists. The inter-observer agreement (IOA) was excellent (96–100%). The ultra-low-dose chest CT reached 93.53–96.84% sensitivity and 90.38–93.84% accuracy. The signal-to-noise ratio (SNR) is 12.8:16.1; CTDIvol (mGy) = 1.1 ± 0.3, DLP (mGy cm) = 42.2 ± 7.9, mean effective dose (mSv/mGy cm) = 0.59 and absolute cancer risk = 0.02 × 10^-4.

Conclusion

Ultra-low-dose HRCT can be reliably used during the second wave of COVID-19 pandemic to reduce the irradiation exposure hazards.

Relationship between chest CT scan findings with SOFA score, CRP, comorbidity, and mortality in ICU patients with COVID-19

OBJECTIVE

This study aimed to investigate the relationship between chest computed tomography (CT) scan findings with sequential organ failure assessment (SOFA) score, C-reactive protein (CRP), comorbidity, and mortality in intensive care unit (ICU) patients with coronavirus disease 19 (COVID-19).

METHOD

Adult patients (≥18 years old) with COVID-19 who were consecutively admitted to the Imam-Reza Hospital, Tabriz, East-Azerbaijan Province, North-West of Iran between March 2020 and August 2020 were screened and total of 168 patients were included. Demographic, clinical, and mortality data were gathered. Severity of disease was evaluated using the SOFA score system. CRP levels were measured and chest CT scans were performed.

RESULTS

Most of patients had multifocal and bilateral ground glass opacity (GGO) pattern in chest CT scan. There were significant correlations between SOFA score on admission with multifocal and bilateral GGO (P = .010 and P = .011, respectively). Significant relationships were observed between unilateral and bilateral GGO patterns with CRP (P = .049 and P = .046, respectively). There was significant relationship between GGO patterns with comorbidities including overweight/obesity, heart failure, cardiovascular diseases, and malignancy (P < .05). No significant relationships were observed between chest CT scan results with mortality (P > .05).

CONCLUSION

Multifocal bilateral GGO was the most common pattern. Although chest CT scan characteristics were significantly related with SOFA score, CRP, and comorbidity in ICU patients with COVID-19, a relationship with mortality was not significant.

Automated quantification of COVID-19 pneumonia severity in chest CT using histogram-based multi-level thresholding segmentation

Background

Chest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated.

Results

The Spearman’s correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (p < 0.0001).

Conclusions

The automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease.

COVID-19 pneumonia: a pictorial review of CT findings and differential diagnosis

Background

The gold standard for verifying COVID-19 mostly depends on microbiological tests like real-time polymerase chain reaction (RT-PCR). However, the availability of RT-PCR kits can be known as a problem and false negative results may be encountered. Although CT scan is not a screening tool for the diagnosis of COVID-19 pneumonia, given the widespread acquisition of it in the pandemic state, familiarity with different CT findings and possible differential diagnosis is essential in this regard.

Main text

In this review, we introduced the typical and atypical CT features of COVID-19 pneumonia, and discussed the main differential diagnosis of COVID-19 pneumonia.

Conclusions

The imaging findings in this viral pneumonia showed a broad spectrum, and there are no pathognomonic imaging findings for COVID-19 pneumonia. Although CT scan is not a diagnostic and screening tool, familiarity with different imaging findings and their differential diagnosis can be helpful in a rapid and accurate decision-making.

Imaging of COVID-19 simulators

Background

Coronavirus (COVID-19) pneumonia emerged in Wuhan, China, in December 2019. It was highly contagious spreading all over the world, with a rapid increase in the number of deaths. The reported cases have reached more than 14 million with more than 600,000 deaths around the world. So, the pandemic of COVID-19 became a surpassing healthcare crisis with an intensive load on the healthcare resources.

In this study, the aim was to differentiate COVID-19 pneumonia from its mimickers as atypical infection, interstitial lung diseases, and eosinophilic lung diseases based on CT, clinical, and laboratory findings.

Results

This retrospective study included 260 patients, of which 220 were confirmed as COVID-19 positive by two repeated RT-PCR test and 40 were classified as non-COVID by two repeated negative RT-PCR test or identification of other pathogens, other relevant histories, or clinical findings.

In this study, 158 patients were male (60.7 %) and 102 patients were female (39.3%). There was 60.9% of the COVID-19 group were male and 39.1% were female. Patients in the non-COVID group were significantly older (the mean age was 46.4) than those in the confirmed COVID-19 group (35.2y). In the COVID-19 group, there was exposure history to positive cases in 84.1% while positive exposure history was 20% in the non-COVID group.

Conclusion

The spectrum of CT imaging findings in COVID-19 pneumonia is wide that could be contributed by many other diseases making the interpretation of chest CTs nowadays challenging to differentiate between different diseases having the same signs and act as deceiving simulators in the era of COVID-19.

Chest CT manifestations with emphasis on the role of CT scoring and serum ferritin/lactate dehydrogenase in prognosis of coronavirus disease 2019 (COVID-19)

Background

In March 2020, the World Health Organization announced coronavirus disease 2019 (COVID-19) a pandemic, and because of the primary pulmonary manifestations of the disease, chest CT is essential in the evaluation of those patients. The aim of the study was to evaluate the role of chest CT findings and chest CT scoring along with serum ferritin and LDH in the prognosis of COVID-19 patients in a cohort of the Egyptian population.

Results

This retrospective study included 250 patients with positive RT-PCR for COVID-19, 138 males [55.2%] and 112 females [44.8%], age range 17–82 years with median 49.5. Two hundred patients had a positive significant correlation between age, serum ferritin, serum LDH, and CT score. Bilateral affection was 88% while unilaterality was 12%, and peripheral chest CT findings were stratified as follows: mild [score from 1 to 10], 114 patients [57%]; moderate [score from 11 to 19], 65 patients [32.5%]; and severe [score from 20 to 25], 21 patients [10.5%]. In severe cases, males constitute 85.7% while females were only 14.3%. Statistical and central distribution was 67%, peripheral was 31%, and central was 2%. Ground glass opacity (GGO) was the highest pattern 39.2%, consolidation 31.2%, fibrosis 15.2%, and CP 13.7%, with lymph nodes only 0.6%. Fifteen cases [6%] were critical; all showed severe scores ranging from 21 to 23 with three times increase in serum ferritin and four times increase in LDH. A follow-up study done to 8 cases [3.2%] showed an increase in CT scoring, serum ferritin, and serum LDH.

Conclusion

Chest CT findings are crucial for early diagnosis of COVID-19 disease especially for asymptomatic patients with old age and male sex considered risk factors for poor prognosis. Chest CT score, serum ferritin, and serum LDH help in predicting the short-term outcome of the patients aiming to decrease both morbidity and mortality.

Efficacy of chest X-ray in the diagnosis of COVID-19 pneumonia: comparison with computed tomography through a simplified scoring system designed for triage

Background

There is no consensus on the imaging modality to be used in the diagnosis and management of Coronavirus disease 2019 (COVID-19) pneumonia. The purpose of this study was to make a comparison between computed tomography (CT) and chest X-ray (CXR) through a scoring system that can be beneficial to the clinicians in making the triage of patients diagnosed with COVID-19 pneumonia at their initial presentation to the hospital.

Results

Patients with a negative CXR (30.1%) had significantly lower computed tomography score (CTS) (p < 0.001). Among the lung zones where the only infiltration pattern was ground glass opacity (GGO) on CT images, the ratio of abnormality seen on CXRs was 21.6%. The cut-off value of X-ray score (XRS) to distinguish the patients who needed intensive care at follow-up (n = 12) was 6 (AUC = 0.933, 95% CI = 0.886–0.979, 100% sensitivity, 81% specificity).

Conclusions

Computed tomography is more effective in the diagnosis of COVID-19 pneumonia at the initial presentation due to the ease detection of GGOs. However, a baseline CXR taken after admission to the hospital can be valuable in predicting patients to be monitored in the intensive care units.

Chest CT features associated with the clinical characteristics of patients with COVID-19 pneumonia

OBJECTIVES

Coronavirus disease 2019 (COVID-19) has rapidly swept across the world. This study aimed to explore the relationship between the chest CT findings and clinical characteristics of COVID-19 patients.

METHODS

Patients with COVID-19 confirmed by next-generation sequencing or RT-PCR who had undergone more than 4 serial chest CT procedures were retrospectively enrolled.

RESULTS

This study included 361 patients - 192 men and 169 women. On initial chest CT, more lesions were identified as multiple bilateral lungs lesions and localised in the peripheral lung. The predominant patterns of abnormality were ground-glass opacities (GGO) (28.5%), consolidation (13.0%), nodule (23.0%), fibrous stripes (5.3%) and mixed (30.2%). Severe cases were more common in patients with a mixed pattern (21.1%) and less common in patients with nodules (2.4%). During follow-up CT, the mediumtotal severity score (TSS) in patients with nodules and fibrous strips was significantly lower than that in patients with mixed patterns in all three stages (p < .01).

CONCLUSION

Chest CT plays an important role in diagnosing COVID-19. The CT features may vary by age. Different CT features are not only associated with clinical manifestation but also patient prognosis. Key messages The initial chest CT findings of COVID-19 could help us monitor and predict the outcome. Nodules were more common in non severe cases and had a favorable prognosis. The mixed pattern was more common in severe cases and usually had a relatively poor outcome.

COVID-19 pandemic: CT chest in COVID-19 infection and prediction of patient’s ICU needs

Background

With the tremendous rise in COVID-19 infection and the shortage of real-time reverse transcription-polymerase chain reaction (RT-PCR) testing, we aimed to assess the role of CT in the detection of COVID-19 infection and the correlation with the patients’ management. A retrospective study was conducted on 600 patients who presented with symptoms suspicious for COVID-19 infection between March and the end of June 2020. The current study followed the RSNA recommendations in CT reporting and correlated with the RT-PCR. CT was reviewed and the severity score was correlated with the patient’s management.

Results

Four hundred sixty-six patients were included with a mean age of 46 + 14.8 years and 63.3 % were males. Three hundred forty patients were confirmed positive by RT-PCR. CT sensitivity was 92.6% while the RT-PCR was the reference. The CT specificity showed a gradual increase with the CT probability reaching 97.6% with high probability CT features. Ground-glass opacities (GGO) was the commonest findings 85.9% with a high incidence of bilateral, peripheral, and multilobar involvement (88%, 92.8%, and 92.8% respectively). Consolidation was found in 81.5% of the ICU patients and was the dominant feature in 66.7% of the ICU cases. CT severity score was significantly higher in ICU patients with a score of ≥ 14.

Conclusions

COVID-19 infection showed typical CT features which can be used as a rapid and sensitive investigation. Two CT phenotypes identified with the predominant consolidation phenotype as well as severity score can be used to determine infection severity and ICU need.

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

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.

Epidemiology and factors associated with COVID-19 outbreak-related deaths in patients admitted to medical centers of Mazandaran University of Medical Sciences

BACKGROUND

The first case of COVID-19 was reported in Iran on February 19, 2020, in Qom. Since Mazandaran is one of the high-risk provinces with many patients and deaths, this study was conducted to investigate the epidemiological characteristics of COVID-19-related deaths in Mazandaran.

MATERIALS AND METHODS

In this descriptive study, demographic information and clinical findings in patients who died following COVID-19 in the medical centers of Mazandaran University of Medical Sciences from February 8, 2020, to October 10, 2020, were extracted. Data were analyzed by using SPSS 21. Logistic regression was used to compare the data. P < 0.05 was considered as the significance level.

RESULTS

Out of a total of 34,039 patients admitted during the 8 months, 2907 patients died. Of these, 1529 (52%) were male, and the rest were female. In terms of age, 10 cases in the age group of fewer than 15 years, 229 cases in the age group of 15-44 years, 864 patients in the age group of 45-64 years, and 1793 people in the age group of 65 years and over died. 2206 people (more than 75%) by personal visit referred to medical centers. The mortality rate was more than 8 cases per 100 hospitalized patients. Men were 16% more likely to die from COVID-19 than women.

DISCUSSION AND CONCLUSION

Older adults over 65 have the highest incidence and death rate due to this disease. The incidence rate was higher in women, and the death rate was higher in men, which differs from the national pattern.

Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19

Artificial intelligence (AI) technologies have been applied in various medical domains to predict patient outcomes with high accuracy. As AI becomes more widely adopted, the problem of model bias is increasingly apparent. In this study, we investigate the model bias that can occur when training a model using datasets for only one particular gender and aim to present new insights into the bias issue. For the investigation, we considered an AI model that predicts severity at an early stage based on the medical records of coronavirus disease (COVID-19) patients. For 5,601 confirmed COVID-19 patients, we used 37 medical records, namely, basic patient information, physical index, initial examination findings, clinical findings, comorbidity diseases, and general blood test results at an early stage. To investigate the gender-based AI model bias, we trained and evaluated two separate models-one that was trained using only the male group, and the other using only the female group. When the model trained by the male-group data was applied to the female testing data, the overall accuracy decreased-sensitivity from 0.93 to 0.86, specificity from 0.92 to 0.86, accuracy from 0.92 to 0.86, balanced accuracy from 0.93 to 0.86, and area under the curve (AUC) from 0.97 to 0.94. Similarly, when the model trained by the female-group data was applied to the male testing data, once again, the overall accuracy decreased-sensitivity from 0.97 to 0.90, specificity from 0.96 to 0.91, accuracy from 0.96 to 0.91, balanced accuracy from 0.96 to 0.90, and AUC from 0.97 to 0.95. Furthermore, when we evaluated each gender-dependent model with the test data from the same gender used for training, the resultant accuracy was also lower than that from the unbiased model.

A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

Objectives

An accurate and rapid diagnosis is crucial for the appropriate treatment of pulmonary tuberculosis (TB). This study aims to develop an artificial intelligence (AI)–based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB.

Methods

From December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning–based cascading framework was connected to create a processing pipeline. For training and validation of the model, 1921 lesions were manually labeled, classified according to six categories of critical imaging features, and visually scored regarding lesion involvement as the ground truth. A “TB score” was calculated based on a network-activation map to quantitively assess the disease burden. Independent testing datasets from two additional hospitals (dataset 2, n = 99; dataset 3, n = 86) and the NIH TB Portals (n = 171) were used to externally validate the performance of the AI model.

Results

CT scans of 526 participants (mean age, 48.5 ± 16.5 years; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of the validation cohort of 0.68. The overall classification accuracy of six pulmonary critical imaging findings indicative of TB of the independent datasets was 81.08–91.05%. A moderate to strong correlation was demonstrated between the AI model–quantified TB score and the radiologist-estimated CT score.

Conclusions

The proposed end-to-end AI system based on chest CT can achieve human-level diagnostic performance for early detection and optimal clinical management of patients with pulmonary TB.

Key Points

• Deep learning allows automatic detection, diagnosis, and evaluation of pulmonary tuberculosis.

• Artificial intelligence helps clinicians to assess patients with tuberculosis.

• Pulmonary tuberculosis disease activity and treatment management can be improved.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08365-z.

CT presentations of adult and pediatric SARS-CoV-2 patients: A review of early COVID-19 data

OBJECTIVE

Initial COVID-19 reports described a variety of clinical presentations, but lower respiratory abnormalities are most common and chest CT findings differ between adult and pediatric patients. We aim to summarize early CT findings to inform healthcare providers on the frequency of COVID-19 manifestations specific to adult or pediatric patients, and to determine if the sensitivity of CT justifies its use in these populations.

METHODS

PubMed was searched for the presence of the words "CT, imaging, COVID-19" in the title or abstract, and 17 large-scale PubMed and/or Scopus studies and case reports published between January 1, 2020 and April 15, 2020 were selected for data synthesis.

RESULTS

Initial CT scans identified ground-glass opacities and bilateral abnormalities as more frequent in adults (74%, n = 698, and 89%, n = 378, respectively) than children (60%, n = 25, and 37%, n = 46). At 14+ days, CT scans evidenced varied degrees of improvement in adults but no resolution until at least 26 days after the onset of flu-like symptoms. In pediatric patients, a third (n = 9) showed additional small nodular GGOs limited to a single lobe 3-5 days after an initial CT scan.

CONCLUSION

Early adult CT findings suggest the limited use of CT as a supplemental tool in diagnosing COVID-19 in symptomatic adult patients, with a particular focus on identifying right and left lower lobe abnormalities, GGOs, and interlobular septal thickening. Early pediatric CT findings suggest against the use of CT if RT-PCR is available given its significantly lower sensitivity in this population and radiation exposure.

Ultraviolet-A light increases mitochondrial anti-viral signaling protein in confluent human tracheal cells via cell-cell signaling

Mitochondrial antiviral signaling (MAVS) protein mediates innate antiviral responses, including responses to certain coronaviruses such as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). We have previously shown that ultraviolet-A (UVA) therapy can prevent virus-induced cell death in human ciliated tracheal epithelial cells (HTEpC) infected with coronavirus-229E (CoV-229E), and results in increased intracellular levels of MAVS. In this study, we explored the mechanisms by which UVA light can activate MAVS, and whether local UVA light application can activate MAVS at locations distant from the light source (e.g. via cell-to-cell communication). MAVS levels were compared in HTEpC exposed to 2 mW/cm² narrow band (NB)-UVA for 20 min and in unexposed controls at 30–40% and at 100% confluency, and in unexposed HTEpC treated with supernatants or lysates from UVA-exposed cells or from unexposed controls. MAVS was also assessed in different sections of confluent monolayer plates where only one section was exposed to NB-UVA. Our results showed that UVA increases the expression of MAVS protein. Further, cells in a confluent monolayer exposed to UVA conferred an elevation in MAVS in cells adjacent to the exposed section, and also in cells in the most distant sections which were not exposed to UVA. In this study, human ciliated tracheal epithelial cells exposed to UVA demonstrate increased MAVS protein, and also appear to transmit this influence to confluent cells not exposed to UVA, likely via cell-cell signaling.

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs

BACKGROUND

Chest radiographs (CXR) are frequently used as a screening tool for patients with suspected COVID-19 infection pending reverse transcriptase polymerase chain reaction (RT-PCR) results, despite recommendations against this. We evaluated radiologist performance for COVID-19 diagnosis on CXR at the time of patient presentation in the Emergency Department (ED).

MATERIALS AND METHODS

We extracted RT-PCR results, clinical history, and CXRs of all patients from a single institution between March and June 2020. 984 RT-PCR positive and 1043 RT-PCR negative radiographs were reviewed by 10 emergency radiologists from 4 academic centers. 100 cases were read by all radiologists and 1927 cases by 2 radiologists. Each radiologist chose the single best label per case: Normal, COVID-19, Other - Infectious, Other - Noninfectious, Non-diagnostic, and Endotracheal Tube. Cases labeled with endotracheal tube (246) or non-diagnostic (54) were excluded. Remaining cases were analyzed for label distribution, clinical history, and inter-reader agreement.

RESULTS

1727 radiographs (732 RT-PCR positive, 995 RT-PCR negative) were included from 1594 patients (51.2% male, 48.8% female, age 59 ± 19 years). For 89 cases read by all readers, there was poor agreement for RT-PCR positive (Fleiss Score 0.36) and negative (Fleiss Score 0.46) exams. Agreement between two readers on 1638 cases was 54.2% (373/688) for RT-PCR positive cases and 71.4% (679/950) for negative cases. Agreement was highest for RT-PCR negative cases labeled as Normal (50.4%, n = 479). Reader performance did not improve with clinical history or time between CXR and RT-PCR result.

CONCLUSION

At the time of presentation to the emergency department, emergency radiologist performance is non-specific for diagnosing COVID-19.

D-dimer, disease severity, and deaths (3D-study) in patients with COVID-19: a systematic review and meta-analysis of 100 studies

Hypercoagulability and the need for prioritizing coagulation markers for prognostic abilities have been highlighted in COVID-19. We aimed to quantify the associations of D-dimer with disease progression in patients with COVID-19. This systematic review and meta-analysis was registered with PROSPERO, CRD42020186661.We included 113 studies in our systematic review, of which 100 records (n = 38,310) with D-dimer data) were considered for meta-analysis. Across 68 unadjusted (n = 26,960) and 39 adjusted studies (n = 15,653) reporting initial D-dimer, a significant association was found in patients with higher D-dimer for the risk of overall disease progression (unadjusted odds ratio (uOR) 3.15; adjusted odds ratio (aOR) 1.64). The time-to-event outcomes were pooled across 19 unadjusted (n = 9743) and 21 adjusted studies (n = 13,287); a strong association was found in patients with higher D-dimers for the risk of overall disease progression (unadjusted hazard ratio (uHR) 1.41; adjusted hazard ratio (aHR) 1.10). The prognostic use of higher D-dimer was found to be promising for predicting overall disease progression (studies 68, area under curve 0.75) in COVID-19. Our study showed that higher D-dimer levels provide prognostic information useful for clinicians to early assess COVID-19 patients at risk for disease progression and mortality outcomes. This study, recommends rapid assessment of D-dimer for predicting adverse outcomes in COVID-19.

Pathogenic and transcriptomic differences of emerging SARS-CoV-2 variants in the Syrian golden hamster model

Background

Following the discovery of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid spread throughout the world, new viral variants of concern (VOC) have emerged. There is a critical need to understand the impact of the emerging variants on host response and disease dynamics to facilitate the development of vaccines and therapeutics.

Methods

Syrian golden hamsters are the leading small animal model that recapitulates key aspects of severe coronavirus disease 2019 (COVID-19). We performed intranasal inoculation of SARS-CoV-2 into hamsters with the ancestral virus (nCoV-WA1-2020) or VOC first identified in the United Kingdom (B.1.1.7, alpha) and South Africa (B.1.351, beta) and analyzed viral loads and host responses.

Findings

Similar gross and histopathologic pulmonary lesions were observed after infection with all three variants. Although differences in viral genomic copy numbers were noted in the lungs and oral swabs of challenged animals, infectious titers in the lungs were comparable between the variants. Antibody neutralization capacities varied, dependent on the original challenge virus and cross-variant protective capacity. Transcriptional profiling of lung samples 4 days post-challenge (DPC) indicated significant induction of antiviral pathways in response to all three challenges with a more robust inflammatory signature in response to B.1.1.7 infection. Furthermore, no additional mutations in the spike protein were detected at 4 DPC.

Interpretations

Although disease severity and viral shedding were not significantly different, the emerging VOC induced distinct humoral responses and transcriptional profiles compared to the ancestral virus. These observations suggest potential differences in acute early responses or alterations in immune modulation by VOC.

Funding

Intramural Research Program, NIAID, NIH; National Center for Research Resources, NIH; National Center for Advancing Translational Sciences, NIH.

Two-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans

COVID-19 stay threatening the health infrastructure worldwide. Computed tomography (CT) was demonstrated as an informative tool for the recognition, quantification, and diagnosis of this kind of disease. It is urgent to design efficient deep learning (DL) approach to automatically localize and discriminate COVID-19 from other comparable pneumonia on lung CT scans. Thus, this study introduces a novel two-stage DL framework for discriminating COVID-19 from community-acquired pneumonia (CAP) depending on the detected infection region within CT slices. Firstly, a novel U-shaped network is presented to segment the lung area where the infection appears. Then, the concept of transfer learning is applied to the feature extraction network to empower the network capabilities in learning the disease patterns. After that, multi-scale information is captured and pooled via an attention mechanism for powerful classification performance. Thirdly, we propose an infection prediction module that use the infection location to guide the classification decision and hence provides interpretable classification decision. Finally, the proposed model was evaluated on public datasets and achieved great segmentation and classification performance outperforming the cutting-edge studies.

Prognostic value of neutrophil-to-lymphocyte ratio in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Neutrophil-to-lymphocyte ratio (NLR) is an accessible and widely used biomarker. NLR may be used as an early marker of poor prognosis in patients with COVID-19.

OBJECTIVE

To evaluate the prognostic value of the NLR in patients diagnosed with COVID-19.

METHODS

We conducted a systematic review and meta-analysis. Observational studies that reported the association between baseline NLR values (ie, at hospital admission) and severity or all-cause mortality in COVID-19 patients were included. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). Random effects models and inverse variance method were used for meta-analyses. The effects were expressed as odds ratios (ORs) and their 95% confidence intervals (CIs). Small study effects were assessed with the Egger's test.

RESULTS

We analysed 61 studies (n = 15 522 patients), 58 cohorts, and 3 case-control studies. An increase of one unit of NLR was associated with higher odds of severity (OR 6.22; 95%CI 4.93 to 7.84; P < .001) and higher odds of all-cause mortality (OR 12.6; 95%CI 6.88 to 23.06; P < .001). In our sensitivity analysis, we found that 41 studies with low risk of bias and moderate heterogeneity (I2  = 53% and 58%) maintained strong association between NLR values and both outcomes (severity: OR 5.36; 95% CI 4.45 to 6.45; P < .001; mortality: OR 10.42 95% CI 7.73 to 14.06; P = .005).

CONCLUSIONS

Higher values of NLR were associated with severity and all-cause mortality in hospitalised COVID-19 patients.

SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images

Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability.

RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions

The segmentation of COVID-19 lesions from computed tomography (CT) scans is crucial to develop an efficient automated diagnosis system. Deep learning (DL) has shown success in different segmentation tasks. However, an efficient DL approach requires a large amount of accurately annotated data, which is difficult to aggregate owing to the urgent situation of COVID-19. Inaccurate annotation can easily occur without experts, and segmentation performance is substantially worsened by noisy annotations. Therefore, this study presents a reliable and consistent temporal-ensembling (RCTE) framework for semi-supervised lesion segmentation. A segmentation network is integrated into a teacher-student architecture to segment infection regions from a limited number of annotated CT scans and a large number of unannotated CT scans. The network generates reliable and unreliable targets, and to evenly handle these targets potentially degrades performance. To address this, a reliable teacher-student architecture is introduced, where a reliable teacher network is the exponential moving average (EMA) of a reliable student network that is reliably renovated by restraining the student involvement to EMA when its loss is larger. We also present a noise-aware loss based on improvements to generalized cross-entropy loss to lead the segmentation performance toward noisy annotations. Comprehensive analysis validates the robustness of RCTE over recent cutting-edge semi-supervised segmentation techniques, with a 65.87% Dice score.

Promising effects of gingerol against toxins: A review article

Ginger is a medicinal and valuable culinary plant. Gingerols, as an active constituent in the fresh ginger rhizomes of Zingiber officinale, exhibit several promising pharmacological properties. This comprehensive literature review was performed to assess gingerol's protective and therapeutic efficacy against the various chemical, natural, and radiational stimuli. Another objective of this study was to investigate the mechanism of anti-inflammatory, antioxidant, and antiapoptotic properties of gingerol. It should be noted that the data were gathered from in vivo and in vitro experimental studies. Gingerols can exert their protective activity through different mechanisms and cell signaling pathways. For example, these are mitogen-activated protein kinase (MAPK), nuclear factor-kappa B (NF-kB), Wnt/β-catenin, nuclear factor erythroid 2-related factor 2/antioxidant response element (Nrf2/ARE), transforming growth factor beta1/Smad3 (TGF-β1/Smad3), and extracellular signal-related kinase/cAMP-response element-binding protein (ERK/CREB). We hope that more researchers can benefit from this review to conduct preclinical and clinical studies, treat cancer, inflammation, and attenuate the side effects of drugs and industrial pollutants.

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https://github.com/SY575/COVID19-CT).

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https://github.com/SY575/COVID19-CT).