Coronavirus Disease 2019 Lung Research Articles

Clinical and Laboratory Features and Maternal Outcomes in Pregnant Women with Critical Lung Damage in the COVID-19

Introduction. The spread of the delta strain of the SARS-CoV-2 virus during the third and fourth waves of the pandemic led to the fact that a large cohort of pregnant women and maternity women needed respiratory support, standard treatment methods had no effect, and negative outcomes for the mother and fetus has increased.Purpose of the study. The aim is to determine clinical and laboratory features and maternal outcomes in pregnant women with the coronavirus disease 2019 (COVID-19) and critical lung damage.Materials and methods. A comparative study was conducted, followed by a retrospective analysis of clinical and laboratory features, maternal outcomes in 56 pregnant women with COVID-19 treated in this maternity hospital in the first-fourth wave of the pandemic. The research method is continuous, sequential. The first group (main) consisted of 28 women with severe or extremely severe form of the course of COVID-19 and critical lung damage (more 75 %); The second group (comparison) — 28 pregnant women with a moderate form of the disease and lung damage of 25–50 %. Screening assessment of the degree of endogenous intoxication was carried out. The severity of the disease was determined by the evaluation scale National Early Warning Score (NEWS).Results. Laboratory parameters of patients at admission and at the height of the disease have statistically significant differences in the general blood test, such as higher levels of leukocytes, eosinophils, rod-shaped neutrophils, hemoglobin, as well as lymphopenia and monocytopenia in women of the main group at the height of the disease. At the same time, the hematological indices of intoxication in the main group exceed the value of these indicators in the comparison group by two times. Patients of both groups had high levels of acute-phase markers of inflammation — C-reactive protein and ferritin, — an indicator of tissue destruction of lactate dehydrogenase, however, both at admission and at the height of the disease, these indicators were statistically significantly higher in the group of women with critical lung damage. In general, 20/28 patients (71.4 %) of group 1 had an extremely severe course of the disease, 8/28 (28.6 %) had a severe course. In second group, 28/28 patients (100 %) had moderate-severe NCI (p = 0.000), while recovery was noted in all of them. Outcomes of COVID-19 in women with critical lung damage: recovered — 25/28 (89 %); 3/28 (10.7 %) the patients died from complications of COVID-19 of an extremely severe degree.Discussion. The most frequent laboratory anomaly was leukocytosis and a rod-shaped shift in the midst of the disease in women with critical lung damage, as well as a statistically significant increase in the level of C-reactive protein, lactate dehydrogenase, D-dimer. Pulmonary complications (parapneumonic pleurisy, pneumothorax, pneumomediastinum, hydrothorax, lung abscess) and extrapulmonary complications (encephalopathy, panic attacks, acute liver injury, sepsis, thromboembolic complications) were observed only with critical lung damage.Conclusion. In the laboratory, at the height of the disease, in the group of pregnant women with critical lung damage, leukocytosis and stab shift are statistically significantly more common. Hematological indices of intoxication, leukocyte and hematological, confirm endogenous intoxication at the height of the disease. A statistically significantly higher level of aspartate aminotransferase, lactate dehydrogenase, C-reactive protein, D-dimer is determined upon admission to the hospital and at the height of the disease as markers of systemic inflammatory response and cytolysis, which demonstrates the defeat of several organ systems simultaneously.

GIFNet: an effective global infection feature network for automatic COVID-19 lung lesions segmentation.

The ongoing COronaVIrus Disease 2019 (COVID-19) pandemic carried by the SARS-CoV-2 virus spread worldwide in early 2019, bringing about an existential health catastrophe. Automatic segmentation of infected lungs from COVID-19 X-ray and computer tomography (CT) images helps to generate a quantitative approach for treatment and diagnosis. The multi-class information about the infected lung is often obtained from the patient's CT dataset. However, the main challenge is the extensive range of infected features and lack of contrast between infected and normal areas. To resolve these issues, a novel Global Infection Feature Network (GIFNet)-based Unet with ResNet50 model is proposed for segmenting the locations of COVID-19 lung infections. The Unet layers have been used to extract the features from input images and select the region of interest (ROI) by using the ResNet50 technique for training it faster. Moreover, integrating the pooling layer into the atrous spatial pyramid pooling (ASPP) mechanism in the bottleneck helps for better feature selection and handles scale variation during training. Furthermore, the partial differential equation (PDE) approach is used to enhance the image quality and intensity value for particular ROI boundary edges in the COVID-19 images. The proposed scheme has been validated on two datasets, namely the SARS-CoV-2 CT scan and COVIDx-19, for detecting infected lung segmentation (ILS). The experimental findings have been subjected to a comprehensive analysis using various evaluation metrics, including accuracy (ACC), area under curve (AUC), recall (REC), specificity (SPE), dice similarity coefficient (DSC), mean absolute error (MAE), precision (PRE), and mean squared error (MSE) to ensure rigorous validation. The results demonstrate the superior performance of the proposed system compared to the state-of-the-art (SOTA) segmentation models on both X-ray and CT datasets.

Cytomegalovirus Reactivation in Postrenal Transplant Patients Immediately in Association with Coronavirus Disease 2019 (COVID-19) Infection: A Case Report of 2 Patients and a Brief Review of the Literature

Introduction: The coronavirus disease 2019 (COVID-19) infection has been associated with multiple opportunistic infections, including secondary cytomegalovirus (CMV) reactivation. Several cases of CMV syndrome and invasive CMV infection have been reported following severe COVID-19 infection worldwide during the COVID-19 pandemic in both immunocompetent and immunosuppressed patients. A case-control study conducted in India showed that during the first and second waves, CMV was the most common co-infection among fatal cases of severe COVID-19 lung infection in Indian renal transplant recipients. Case Presentation: We present 2 cases of CMV disease in postrenal transplant patients who developed clinical manifestations of CMV co-infection after mild COVID-19 infection during the fourth wave of COVID-19 infection in India. In both cases, CMV infection was treated by discontinuing mycophenolate mofetil and administering ganciclovir injections. However, the treatment resulted in 2 contrasting clinical outcomes: Cure and death. Conclusions: We reviewed the recent literature on the increased incidence of CMV reactivation after both COVID-19 infection and vaccination in immunocompetent and immunocompromised populations. Subsequently, we discussed some clinical questions relevant to the population of postrenal transplant recipients after the detection of mild COVID-19 infection.

Molecular and immune signatures, and pathological trajectories of fatal COVID-19 lungs defined by in situ spatial single-cell transcriptome analysis.

Despite intensive studies during the last 3 years, the pathology and underlying molecular mechanism of coronavirus disease 2019 (COVID-19) remain poorly defined. In this study, we investigated the spatial single-cell molecular and cellular features of postmortem COVID-19 lung tissues using in situ sequencing (ISS). We detected 10 414 863 transcripts of 221 genes in whole-slide tissues and segmented them into 1 719 459 cells that were mapped to 18 major parenchymal and immune cell types, all of which were infected by SARS-CoV-2. Compared with the non-COVID-19 control, COVID-19 lungs exhibited reduced alveolar cells (ACs) and increased innate and adaptive immune cells. We also identified 19 differentially expressed genes in both infected and uninfected cells across the tissues, which reflected the altered cellular compositions. Spatial analysis of local infection rates revealed regions with high infection rates that were correlated with high cell densities (HIHD). The HIHD regions expressed high levels of SARS-CoV-2 entry-related factors including ACE2, FURIN, TMPRSS2 and NRP1, and co-localized with organizing pneumonia (OP) and lymphocytic and immune infiltration, which exhibited increased ACs and fibroblasts but decreased vascular endothelial cells and epithelial cells, mirroring the tissue damage and wound healing processes. Sparse nonnegative matrix factorization (SNMF) analysis of niche features identified seven signatures that captured structure and immune niches in COVID-19 tissues. Trajectory inference based on immune niche signatures defined two pathological routes. Trajectory A primarily progressed with increased NK cells and granulocytes, likely reflecting the complication of microbial infections. Trajectory B was marked by increased HIHD and OP, possibly accounting for the increased immune infiltration. The OP regions were marked by high numbers of fibroblasts expressing extremely high levels of COL1A1 and COL1A2. Examination of single-cell RNA-seq data (scRNA-seq) from COVID-19 lung tissues and idiopathic pulmonary fibrosis (IPF) identified similar cell populations consisting mainly of myofibroblasts. Immunofluorescence staining revealed the activation of IL6-STAT3 and TGF-β-SMAD2/3 pathways in these cells, likely mediating the upregulation of COL1A1 and COL1A2 and excessive fibrosis in the lung tissues. Together, this study provides a spatial single-cell atlas of cellular and molecular signatures of fatal COVID-19 lungs, which reveals the complex spatial cellular heterogeneity, organization, and interactions that characterized the COVID-19 lung pathology.

Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.

Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID-19 disease. COVID-19 detection from lung CT images is a rapid way of diagnosing patients. However, identifying the occurrence of infectious tissues and segmenting this from CT images implies several challenges. Therefore, efficient techniques termed as Remora Namib Beetle Optimization_ Deep Quantum Neural Network (RNBO_DQNN) and RNBO_Deep Neuro Fuzzy Network (RNBO_DNFN) are introduced for the identification as well as classification of COVID-19 lung infection. Here, the pre-processing of lung CT images is performed utilizing an adaptive Wiener filter, whereas lung lobe segmentation is performed employing the Pyramid Scene Parsing Network (PSP-Net). Afterwards, feature extraction is carried out wherein features are extracted for the classification phase. In the first level of classification, DQNN is utilized, tuned by RNBO. Furthermore, RNBO is designed by merging the Remora Optimization Algorithm (ROA) and Namib Beetle Optimization (NBO). If a classified output is COVID-19, then the second-level classification is executed using DNFN for further classification. Additionally, DNFN is also trained by employing the newly proposed RNBO. Furthermore, the devised RNBO_DNFN achieved maximum testing accuracy, with TNR and TPR obtaining values of 89.4%, 89.5% and 87.5%.

Lung transplantation for COVID-2019 respiratory failure in the United States: Outcomes 1-year posttransplant and the impact of preoperative extracorporeal membrane oxygenation support

ObjectivePatients with end-stage respiratory failure after severe coronavirus disease 2019 (COVID-19) infection may benefit from lung transplant; however, data on transplant outcomes and the impact of prolonged circulatory support before transplant in these patients are limited. MethodsWe assessed survival, postoperative complications, and the impact of pretransplant extracorporeal membrane oxygenation (ECMO) in patients undergoing lung transplant in the United States from August 2020 through March 2022 using records validated by United Network for Organ Sharing experts and extracted from the United Network for Organ Sharing database. ResultsIn 305 patients with COVID-19–related respiratory failure and validated data, survival for up to 1-year posttransplant did not differ between 188 patients with COVID-19–related acute respiratory distress syndrome and 117 patients with post-COVID-19 pulmonary fibrosis (P = .8). Pretransplant ECMO support (median 66 days) was required in 191 patients (63%), and venovenous ECMO was used in 91.2% of patients. One-, 6-, and 12-month survival was not significantly different between patients requiring ECMO and patients without ECMO (95.8% vs 99.1%, 93.1% vs 96.4%, 84.8% vs 90.9%, P = .2) In addition, 1-year survival was similar in recipients requiring ECMO for COVID-19 lung failure and recipients requiring ECMO for non-COVID-19 restrictive lung failure (84.8% vs 78.0%, P = .1). ConclusionsThese findings suggest that lung transplant in patients with COVID-19 respiratory failure yields acceptable 1-year outcomes. Despite an often more complex postoperative course, prolonged ECMO pretransplant in well-selected patients was associated with adequate clinical and functional status.

Serum lipid mediator profiles in COVID-19 patients and lung disease severity: a pilot study

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection is highly heterogeneous, ranging from asymptomatic to severe and fatal cases. COVID-19 has been characterized by an increase of serum pro-inflammatory cytokine levels which seems to be associated with fatal cases. By contrast, the role of pro-resolving lipid mediators (SPMs), involved in the attenuation of inflammatory responses, has been scarcely investigated, so further studies are needed to understand SPMs metabolism in COVID-19 and other infectious diseases. Our aim was to analyse the lipid mediator metabolome, quantifying pro- and anti-inflammatory serum bioactive lipids by LC–MS/MS in 7 non-infected subjects and 24 COVID-19 patients divided into mild, moderate, and severe groups according to the pulmonary involvement, to better understand the disease outcome and the severity of the pulmonary manifestations. Statistical analysis was performed with the R programming language (R Foundation for Statistical Computing, Vienna, Austria). All COVID-19 patients had increased levels of Prostaglandin E2. Severe patients showed a significant increase versus controls, mild- and moderate-affected patients, expressed as median (interquartile range), in resolvin E1 [112.6 (502.7) vs 0.0 (0.0) pg/ml in the other groups], as well as in maresin 2 [14.5 (7.0) vs 8.1 (4.2), 5.5 (4.3), and 3.0 (4.0) pg/ml, respectively]. Moreover, 14-hydroxy docosahexaenoic acid (14-HDHA) levels were also increased in severe vs control and mild-affected patients [24.7 (38.2) vs 2.4 (2.2) and 3.7 (6.4) ng/mL, respectively]. Resolvin D5 was also significantly elevated in both moderate [15.0 (22.4) pg/ml] and severe patients [24.0 (24.1) pg/ml] versus controls [0.0 (0.0) pg/ml]. These results were confirmed by sparse partial least squares discriminant analysis which highlighted the contribution of these mediators to the separation between each of the groups. In conclusion, the potent inflammatory response to SARS-CoV-2 infection involves not only pro- but also anti-inflammatory lipid mediators that can be quantified in easily accessible serum samples, suggesting the need to perform future research on their generation pathways that will help us to discover new therapeutic targets.

Inhibition of the mitochondrial pyruvate carrier simultaneously mitigates hyperinflammation and hyperglycemia in COVID-19.

The relationship between diabetes and coronavirus disease 2019 (COVID-19) is bidirectional: Although individuals with diabetes and high blood glucose (hyperglycemia) are predisposed to severe COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can also cause hyperglycemia and exacerbate underlying metabolic syndrome. Therefore, interventions capable of breaking the network of SARS-CoV-2 infection, hyperglycemia, and hyperinflammation, all factors that drive COVID-19 pathophysiology, are urgently needed. Here, we show that genetic ablation or pharmacological inhibition of mitochondrial pyruvate carrier (MPC) attenuates severe disease after influenza or SARS-CoV-2 pneumonia. MPC inhibition using a second-generation insulin sensitizer, MSDC-0602K (MSDC), dampened pulmonary inflammation and promoted lung recovery while concurrently reducing blood glucose levels and hyperlipidemia after viral pneumonia in obese mice. Mechanistically, MPC inhibition enhanced mitochondrial fitness and destabilized hypoxia-inducible factor-1α, leading to dampened virus-induced inflammatory responses in both murine and human lung macrophages. We further showed that MSDC enhanced responses to nirmatrelvir (the antiviral component of Paxlovid) to provide high levels of protection against severe host disease development after SARS-CoV-2 infection and suppressed cellular inflammation in human COVID-19 lung autopsies, demonstrating its translational potential for treating severe COVID-19. Collectively, we uncover a metabolic pathway that simultaneously modulates pulmonary inflammation, tissue recovery, and host metabolic health, presenting a synergistic therapeutic strategy to treat severe COVID-19, particularly in patients with underlying metabolic disease.

Meta-analysis of Lung Cancer Patients in COVID-19

Background: Lung cancer patients have a higher chance of getting infected and showing severe outcomes from coronavirus disease 2019 (COVID-19). This infection influences the respiratory system, albeit other organs are also involved with high risk related to health. The blend of COVID-19 disease and lung cancer predicts a higher mortality rate and more serious clinical results. Objective: This research reports the Systemic Review and Meta-analysis correlation between COVID-19 patients with lung cancer and comprehensive proof with regards to the mortality of these patients. Methods: A systematic review and meta-analysis are planned to evaluate the data from a PubMed systematic search on Lung Cancer Patients reported by COVID-19, as well as an efficient literature review and information research from 2019 to 2021. Results: 22 out of 3639 review and research literature assessments were gathered, and 10951 patients were COVID +ve and suffering from cancer, with 21% of the patients suffering from SCLC and NSCLC, and lung cancer accounting for 6% of the mortality. Conclusion: Lung cancer Patients who are suffering from COVID-19 additionally reflected the seriousness of the illness and higher rates of intensive care unit confirmations and mechanical ventilation. COVID-19 in patients with lung cancer is related to extreme disease and expanded mortality compared with patients with different tumours and everyone. There is conflicting proof of explicit lung cancer therapies' results. Until more conclusive data is available, lung cancer-coordinated therapy should be restarted as soon as possible in mild to moderate cases to avoid decline and cancer-related mortality.

Pulmonary Recovery 12 Months after Non-Severe and Severe COVID-19: The Prospective Swiss COVID-19 Lung Study

Background: Lung function impairment persists in some patients for months after acute coronavirus disease 2019 (COVID-19). Long-term lung function, radiological features, and their association remain to be clarified. Objectives: We aimed to prospectively investigate lung function and radiological abnormalities over 12 months after severe and non-severe COVID-19. Methods: 584 patients were included in the Swiss COVID-19 lung study. We assessed lung function at 3, 6, and 12 months after acute COVID-19 and compared chest computed tomography (CT) imaging to lung functional abnormalities. Results: At 12 months, diffusion capacity for carbon monoxide (DLCO_corr) was lower after severe COVID-19 compared to non-severe COVID-19 (74.9% vs. 85.2% predicted, p < 0.001). Similarly, minimal oxygen saturation on 6-min walk test and total lung capacity were lower after severe COVID-19 (89.6% vs. 92.2%, p = 0.004, respectively, 88.2% vs. 95.1% predicted, p = 0.011). The difference for forced vital capacity (91.6% vs. 96.3% predicted, p = 0.082) was not statistically significant. Between 3 and 12 months, lung function improved in both groups and differences in DLCO between non-severe and severe COVID-19 patients decreased. In patients with chest CT scans at 12 months, we observed a correlation between radiological abnormalities and reduced lung function. While the overall extent of radiological abnormalities diminished over time, the frequency of mosaic attenuation and curvilinear patterns increased. Conclusions: In this prospective cohort study, patients who had severe COVID-19 had diminished lung function over the first year compared to those after non-severe COVID-19, albeit with a greater extent of recovery in the severe disease group.

Conservative oxygen supplementation during helmet continuous positive airway pressure therapy in patients with COVID-19 and respiratory failure: a pilot study.

Respiratory failure is a severe complication in coronavirus disease 2019 (COVID-19) pneumonia that, in addition to oxygen therapy, may require continuous positive airway pressure (CPAP) support. It has been postulated that COVID-19 lung injury may share some features with those observed in hyperoxic acute lung injury. Thus, a correct target arterial oxygen tension (P aO2 ) during oxygen supplementation may be crucial to protect the lung from further tissue damage. The aims of this study were: 1) to evaluate the effects of conservative oxygen supplementation during helmet CPAP therapy on mortality and intensive care unit (ICU) admission in patients with COVID-19 and respiratory failure, and 2) to evaluate the effect of conservative oxygen supplementation on new-onset organ failure and secondary pulmonary infections. This was a single-centre, historically controlled study of patients with severe respiratory failure due to COVID-19 pneumonia, receiving either conservative or nonconservative oxygen supplementation during helmet CPAP. A cohort receiving conservative oxygen supplementation was studied prospectively in which oxygen supplementation was administered with a target P aO2 <100 mmHg. Results of this cohort were compared with those of a cohort who had received liberal oxygen supplementation. 71 patients were included in the conservative cohort and 75 in the nonconservative cohort. Mortality rate was lower in the conservative cohort (22.5% versus 62.7%; p<0.001). Rates of ICU admission and new-onset organ failure were lower in the conservative cohort (14.1% versus 37.3%; p=0.001 and 9.9% versus 45.3%; p<0.001, respectively). In patients with COVID-19 and severe respiratory failure, conservative oxygen supplementation during helmet CPAP was associated with improved survival, lower ICU admission rate and less new-onset organ failure.

Early Outcomes of COVID-19 Lung Transplantation Recipients in Korea: A Single-Center Study

Coronavirus disease 2019 (COVID-19) has been found to cause life-threatening respiratory failure, which can progress to irreversible lung damage. Lung transplantation can be a life-saving treatment in patients with terminal lung disease (e.g., acute respiratory distress syndrome caused by infection). This study aimed to present the clinical course and results after initial lung transplantation in patients with severe COVID-19 who did not recover even with optimal medical care. From August 2019 to February 2022, this study enrolled 10 patients with COVID-19 (5 men; median age, 55.7 years) who underwent lung transplantation at a single center in Korea. All patients' characteristics, clinical pathway, overall survival, complications, and operative data were collected and analyzed. Veno-venous extracorporeal membrane oxygenation or an oxygenator in a right ventricular assist device circuit was applied to 90% of the patients, and the median length of extracorporeal life support before operation was 48.5 days. There were no cases of mortality after a median follow-up of 372.8 days (interquartile range, 262.25-489 days). The major complications included the requirement for postoperative extracorporeal membrane oxygenation support in 2 cases (20%), re-transplantation in 1 case (10%), and re-exploration due to bleeding in 2 cases (20%). During the follow-up period, 3 out of 10 patients died. Excellent early outcomes were observed for patients who underwent lung transplantation. Thus, lung transplantation can be an effective and feasible treatment for patients with end-stage lung disease caused by COVID-19.

A Multi-centric Evaluation of Deep Learning Models for Segmentation of COVID-19 Lung Lesions on Chest CT Scans

Background: Chest computed tomography (CT) scan is one of the most common tools used for the diagnosis of patients with coronavirus disease 2019 (COVID-19). While segmentation of COVID-19 lung lesions by radiologists can be time-consuming, the application of advanced deep learning techniques for automated segmentation can be a promising step toward the management of this infection and similar diseases in the future. Objectives: This study aimed to evaluate the performance and generalizability of deep learning-based models for the automated segmentation of COVID-19 lung lesions. Patients and Methods: Four datasets (2 private and 2 public) were used in this study. The first and second private datasets included 297 (147 healthy and 150 COVID-19 cases) and 82 COVID-19 subjects. The public datasets included the COVID19-P20 (20 COVID-19 cases from 2 centers) and the MosMedData datasets (50 COVID-19 patients from a single center). Model comparisons were made based on the Dice similarity coefficient (DSC), receiver operating characteristic (ROC) curve, and area under the curve (AUC). The predicted CT severity scores by the model were compared with those of radiologists by measuring the Pearson’s correlation coefficients (PCC). Also, DSC was used to compare the inter-rater agreement of the model and expert against that of 2 experts on an unseen dataset. Finally, the generalizability of the model was evaluated, and a simple calibration strategy was proposed. Results: The VGG16-UNet model showed the best performance across both private datasets, with a DSC of 84.23% ± 1.73% on the first private dataset and 56.61% ± 1.48% on the second private dataset. Similar results were obtained on public datasets, with a DSC of 60.10% ± 2.34% on the COVID19-P20 dataset and 66.28% ± 2.80% on a combined dataset of COVID19-P20 and MosMedData. The predicted CT severity scores of the model were compared against those of radiologists and were found to be 0.89 and 0.85 on the first private dataset and 0.77 and 0.74 on the second private dataset for the right and left lungs, respectively. Moreover, the model trained on the first private dataset was examined on the second private dataset and compared against the radiologist, which revealed a performance gap of 5.74% based on DSCs. A calibration strategy was employed to reduce this gap to 0.53%. Conclusion: The results demonstrated the potential of the proposed model in localizing COVID-19 lesions on CT scans across multiple datasets; its accuracy competed with the radiologists and could assist them in diagnostic and treatment procedures. The effect of model calibration on the performance of an unseen dataset was also reported, increasing the DSC by more than 5%.

Hippo signaling pathway activation during SARS-CoV-2 infection contributes to host antiviral response.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), responsible for the Coronavirus Disease 2019 (COVID-19) pandemic, causes respiratory failure and damage to multiple organ systems. The emergence of viral variants poses a risk of vaccine failures and prolongation of the pandemic. However, our understanding of the molecular basis of SARS-CoV-2 infection and subsequent COVID-19 pathophysiology is limited. In this study, we have uncovered a critical role for the evolutionarily conserved Hippo signaling pathway in COVID-19 pathogenesis. Given the complexity of COVID-19-associated cell injury and immunopathogenesis processes, we investigated Hippo pathway dynamics in SARS-CoV-2 infection by utilizing COVID-19 lung samples and human cell models based on pluripotent stem cell-derived cardiomyocytes (PSC-CMs) and human primary lung air-liquid interface (ALI) cultures. SARS-CoV-2 infection caused activation of the Hippo signaling pathway in COVID-19 lung and in vitro cultures. Both parental and Delta variant of concern (VOC) strains induced Hippo pathway. The chemical inhibition and gene knockdown of upstream kinases MST1/2 and LATS1 resulted in significantly enhanced SARS-CoV-2 replication, indicating antiviral roles. Verteporfin, a pharmacological inhibitor of the Hippo pathway downstream transactivator, YAP, significantly reduced virus replication. These results delineate a direct antiviral role for Hippo signaling in SARS-CoV-2 infection and the potential for this pathway to be pharmacologically targeted to treat COVID-19.

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches-an international, multi-center comparative study.

The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset. In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0-25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger et al., 2020) and consecutive quantitative assessment of lung attenuation. The best threshold was investigated by training and testing a linear regression of deep-learning and threshold-based results in a five-fold cross validation strategy. Median CCS among 250 evaluated patients was 10 [6-15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97-0.98)]. Best attenuation threshold for identification of involved lung was -522 HU. While the relationship of deep-learning- and threshold-based quantification was linear and strong (r2 deep-learning vs. threshold=0.84), both automated quantification methods translated to the semi-quantitative CCS in a non-linear fashion, with an increasing slope towards higher CCS (r2 deep-learning vs. CCS= 0.80, r2 threshold vs. CCS=0.63). The manual semi-quantitative CCS underestimates the extent of COVID pneumonia in higher score ranges, which limits its clinical usefulness in cases of severe disease. Clinical implementation of fully automated methods, such as deep-learning or threshold-based approaches (best threshold in our multi-center dataset: -522 HU), might save time of trained personnel, abolish inter-reader variability, and allow for truly quantitative, linear assessment of COVID lung involvement.

Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System

The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. Thanks to the development of deep learning technology, some deep learning solutions for lung infection segmentation have been proposed. However, due to the scattered distribution, complex background interference and blurred boundaries, the accuracy and completeness of the existing models are still unsatisfactory. To this end, we propose a boundary guided semantic learning network (BSNet) in this paper. On the one hand, the dual-branch semantic enhancement module that combines the top-level semantic preservation and progressive semantic integration is designed to model the complementary relationship between different high-level features, thereby promoting the generation of more complete segmentation results. On the other hand, the mirror-symmetric boundary guidance module is proposed to accurately detect the boundaries of the lesion regions in a mirror-symmetric way. Experiments on the publicly available dataset demonstrate that our BSNet outperforms the existing state-of-the-art competitors and achieves a real-time inference speed of 44 FPS. The code and results of our BSNet can be found from the link of <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/rmcong/BSNet</uri> .

Efficient synthesis of novel colchicine-magnolol hybrids and evaluation of their inhibitory activity on key proteases of 2019-nCoV replication and acute lung injury

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or 2019-nCoV), is a life-threatening infectious condition. Acute lung injury is a common complication in patients with COVID-19. 3-chymotrypsin-like protease (3CLpro) of 2019-nCoV and neutrophil elastase are critical targets of COVID-19 and acute lung injury, respectively. Colchicine and magnolol are reported to exert inhibitory effects on inflammatory response, the severe comorbidity in both COVID-19 and acute lung injury. We thus designed and synthesized a series of novel colchicine-magnolol hybrids based on a two-step synthetic sequence. It was found that these novel hybrids provided unexpected inhibition on 3CLpro and neutrophil elastase, a bioactivity that colchicine and magnolol did not possess. These findings not only provide perquisites for further in vitro and in vivo investigation to confirm the therapeutic potentiality of novel colchicine-magnolol hybrids, but also suggest that the concurrent inhibition of 3CLpro and neutrophil elastase may enable novel colchicine-magnolol hybrids as effective multi-target drug compounds.

COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.

Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content-aware pre-activated residual UNet (CAPA-ResUNet), was proposed for segmenting COVID-19 lesions from CT slices. In this network, the pre-activated residual block was used for down-sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID-19 Lung CT Lesion Segmentation Challenge-2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA-ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content-aware residual UNet (CARes-UNet). The code is available at https://github.com/malu108/LungInfectionSeg.

An efficient deep neural network framework for COVID-19 lung infection segmentation

Since the outbreak of Coronavirus Disease 2019 (COVID-19) in 2020, it has significantly affected the global health system. The use of deep learning technology to automatically segment pneumonia lesions from Computed Tomography (CT) images can greatly reduce the workload of physicians and expand traditional diagnostic methods. However, there are still some challenges to tackle the task, including obtaining high-quality annotations and subtle differences between classes. In the present study, a novel deep neural network based on Resnet architecture is proposed to automatically segment infected areas from CT images. To reduce the annotation cost, a Vector Quantized Variational AutoEncoder (VQ-VAE) branch is added to reconstruct the input images for purpose of regularizing the shared decoder and the latent maps of the VQ-VAE are utilized to further improve the feature representation. Moreover, a novel proportions loss is presented for mitigating class imbalance and enhance the generalization ability of the model. In addition, a semi-supervised mechanism based on adversarial learning to the network has been proposed, which can utilize the information of the trusted region in unlabeled images to further regularize the network. Extensive experiments on the COVID-SemiSeg are performed to verify the superiority of the proposed method, and the results are in line with expectations.

MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.