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1.
Respir Res ; 23(1): 296, 2022 Oct 31.
Статья в английский | MEDLINE | ID: covidwho-2098345

Реферат

BACKGROUND: Anticoagulant treatment is recommended for at least three months after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related acute pulmonary embolism (PE), but the persistent pulmonary clot burden after that time is unknown. METHODS: Lung perfusion was assessed by ventilation-perfusion (V/Q) SPECT/CT in 20 consecutive patients with SARS-CoV-2-associated acute PE after a minimum of three months anticoagulation therapy in a retrospective observational study. RESULTS: Remaining perfusion defects after a median treatment period of six months were observed in only two patients. All patients (13 men, seven women, mean age 55.6 ± 14.5 years) were on non-vitamin K direct oral anticoagulants (DOACs). No recurrent venous thromboembolism or anticoagulant-related bleeding complications were observed. Among patients with partial clinical recovery, high-risk PE and persistent pulmonary infiltrates were significantly more frequent (p < 0.001, respectively). INTERPRETATION: Temporary DOAC treatment seems to be safe and efficacious for resolving pulmonary clot burden in SARS-CoV-2-associated acute PE. Partial clinical recovery is more likely caused by prolonged SARS-CoV-2-related parenchymal lung damage rather than by persistent pulmonary perfusion defects.


Тема - темы
COVID-19 , Pulmonary Embolism , Male , Humans , Female , Adult , Middle Aged , Aged , SARS-CoV-2 , COVID-19/complications , Pulmonary Embolism/diagnostic imaging , Pulmonary Embolism/drug therapy , Lung/diagnostic imaging , Single Photon Emission Computed Tomography Computed Tomography , Anticoagulants/therapeutic use , Acute Disease , Perfusion
2.
J Intensive Care Med ; 37(12): 1614-1624, 2022 Dec.
Статья в английский | MEDLINE | ID: covidwho-2098205

Реферат

Introduction: The appraisal of disease severity and prediction of adverse outcomes using risk stratification tools at early disease stages is crucial to diminish mortality from coronavirus disease 2019 (COVID-19). While lung ultrasound (LUS) as an imaging technique for the diagnosis of lung diseases has recently gained a leading position, data demonstrating that it can predict adverse outcomes related to COVID-19 is scarce. The main aim of this study is therefore to assess the clinical significance of bedside LUS in COVID-19 patients who presented to the emergency department (ED). Methods: Patients with a confirmed diagnosis of SARS-CoV-2 pneumonia admitted to the ED of our hospital between March 2021 and May 2021 and who underwent a 12-zone LUS and a lung computed tomography scan were included prospectively. Logistic regression and Cox proportional hazard models were used to predict adverse events, which was our primary outcome. The secondary outcome was to discover the association of LUS score and computed tomography severity score (CT-SS) with the composite endpoints. Results: We assessed 234 patients [median age 59.0 (46.8-68.0) years; 59.4% M), including 38 (16.2%) in-hospital deaths for any cause related to COVID-19. Higher LUS score and CT-SS was found to be associated with ICU admission, intubation, and mortality. The LUS score predicted mortality risk within each stratum of NEWS. Pairwise analysis demonstrated that after adjusting a base prediction model with LUS score, significantly higher accuracy was observed in predicting both ICU admission (DBA -0.067, P = .011) and in-hospital mortality (DBA -0.086, P = .017). Conclusion: Lung ultrasound can be a practical prediction tool during the course of COVID-19 and can quantify pulmonary involvement in ED settings. It is a powerful predictor of ICU admission, intubation, and mortality and can be used as an alternative for chest computed tomography while monitoring COVID-19-related adverse outcomes.


Тема - темы
COVID-19 , Humans , Middle Aged , COVID-19/complications , COVID-19/diagnostic imaging , SARS-CoV-2 , Point-of-Care Systems , Lung/diagnostic imaging , Ultrasonography/methods , Tomography, X-Ray Computed
3.
Ultrasound Med Biol ; 47(2): 214-221, 2021 02.
Статья в английский | MEDLINE | ID: covidwho-2096090

Реферат

In this study, the utility of point-of-care lung ultrasound for clinical classification of coronavirus disease (COVID-19) was prospectively assessed. Twenty-seven adult patients with COVID-19 underwent bedside lung ultrasonography (LUS) examinations three times each within the first 2 wk of admission to the isolation ward. We divided the 81 exams into three groups (moderate, severe and critically ill). Lung scores were calculated as the sum of points. A rank sum test and bivariate correlation analysis were carried out to determine the correlation between LUS on admission and clinical classification of COVID-19. There were dramatic differences in LUS (p < 0.001) among the three groups, and LUS scores (r = 0.754) correlated positively with clinical severity (p < 0.01). In addition, moderate, severe and critically ill patients were more likely to have low (≤9), medium (9-15) and high scores (≥15), respectively. This study provides stratification criteria of LUS scores to assist in quantitatively evaluating COVID-19 patients.


Тема - темы
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Point-of-Care Systems , Ultrasonography/instrumentation , Ultrasonography/methods , Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Prospective Studies , Severity of Illness Index
4.
Indian Pediatr ; 59(7): 531-534, 2022 07 15.
Статья в английский | MEDLINE | ID: covidwho-2092983

Реферат

OBJECTIVE: To evaluate pulmonary functions in children with transfusion-dependent thalassemia, and its reversal (lung dysfunction) using intensive intravenous chelation with desferrioxamine (DFO) (4 weeks). METHODS: This descriptive study enrolled 77 children with transfusion-dependent thalassemia. Pulmonary function test (PFT) and iron load (serum ferritin (SF) and T2* MRI of heart and liver) were done. PFT included spirometry, total lung capacity (TLC) by helium dilution test and diffusion capacity by carbon monoxide (DLCO). Follow-up PFT was available for 13 children with moderate to severe lung dysfunction given intravenous DFO. RESULTS: 50 (68.8%) patients had lung dysfunction, most commonly diffusional impairment (48; 96%), and reduced TLC (11; 22%); and none had obstructive pattern. 9 (81.8%) patients with restrictive defect had moderate to severely deranged DLCO. PFT and T2* MRI values were inversely correlated with serum ferritin. Among 13 patients receiving intensive chelation for 4 weeks, significant improvement was noticed in forced expiratory volume in one minute/ forced vital capacity ratio (DFEV1/FVC) (P=0.009), DDLCO (P=0.006) and DSF (P=0.01). CONCLUSIONS: Pulmonary dysfunction is common in children with multi-transfused thalassemia, and routine screening by PFT needs to be part of the management guidelines.


Тема - темы
COVID-19 , Thalassemia , beta-Thalassemia , COVID-19/epidemiology , Child , Ferritins , Humans , Lung/diagnostic imaging , Pandemics , SARS-CoV-2
5.
Ter Arkh ; 94(4): 485-490, 2022 May 26.
Статья в Русский | MEDLINE | ID: covidwho-2091497

Реферат

AIM: To develop a protocol for ultrasound diagnostics of COVID-19 pneumonia and to assess the diagnostic capabilities of the method in comparison with computer tomography (CT). MATERIALS AND METHODS: The study included 59 patients with a new coronavirus infection. In order to identify changes in the lung tissue characteristic of a new coronavirus infection, we used a special protocol for ultrasound of the lungs, which was developed by us in such a way that the data obtained were compared by segment with the results of CT of the lungs. RESULTS: When comparing the results of lung ultrasound with the data of CT diagnostics, according to the new protocol, the percentage of lung tissue damage during ultrasound of the lungs averaged 70.8% in the group [62.5; 87.5], and according to the results of CT 70.0% [60.0; 72.5] (p=0.427). Thus, the ultrasound of the lung lesions was almost completely consistent with the changes revealed by CT. In order to assess the diagnostic value of lung ultrasound in identifying severe lung tissue lesions corresponding to CT 34, ROC analysis was performed, which showed the high diagnostic value of lung ultrasound in identifying severe lung tissue lesions. CONCLUSION: A new protocol was developed for assessing the severity of lung tissue damage according to ultrasound data, which showed a high diagnostic value in detecting COVID-19 pneumonia in comparison with CT. The results obtained give reason to recommend this protocol of ultrasound of the lungs as a highly sensitive method in diagnosing the severity of COVID-19 pneumonia. Its application is very important for dynamic examination of patients, especially in conditions of low availability of CT.


Тема - темы
COVID-19 , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Lung/diagnostic imaging , Tomography, X-Ray Computed/methods , Computers , Retrospective Studies
6.
Ter Arkh ; 94(4): 497-502, 2022 May 26.
Статья в Русский | MEDLINE | ID: covidwho-2091496

Реферат

Treatment of patients with long-term persistent symptoms after COVID-19 is an urgent problem for clinicians around the world. One of the most significant manifestations of post-COVID-19 syndrome is organizing pneumonia that is usually treat with corticosteroids. The paper presents a clinical case of typical course of post-COVID-19 organizing pneumonia in a patient without previous lung disease. Risk factors, diagnostic methods and treatment options in this group of patients are also discuss.


Тема - темы
COVID-19 , Pneumonia , Humans , COVID-19/diagnosis , Lung/diagnostic imaging , Pneumonia/diagnosis , Pneumonia/drug therapy , Pneumonia/etiology , Adrenal Cortex Hormones/therapeutic use
7.
EMBO Mol Med ; 14(11): e16283, 2022 Nov 08.
Статья в английский | MEDLINE | ID: covidwho-2091043

Реферат

Our current understanding of the spectrum of TB and COVID-19 lesions in the human lung is limited by a reliance on low-resolution imaging platforms that cannot provide accurate 3D representations of lesion types within the context of the whole lung. To characterize TB and COVID-19 lesions in 3D, we applied micro/nanocomputed tomography to surgically resected, postmortem, and paraffin-embedded human lung tissue. We define a spectrum of TB pathologies, including cavitary lesions, calcium deposits outside and inside necrotic granulomas and mycetomas, and vascular rearrangement. We identified an unusual spatial arrangement of vasculature within an entire COVID-19 lobe, and 3D segmentation of blood vessels revealed microangiopathy associated with hemorrhage. Notably, segmentation of pathological anomalies reveals hidden pathological structures that might otherwise be disregarded, demonstrating a powerful method to visualize pathologies in 3D in TB lung tissue and whole COVID-19 lobes. These findings provide unexpected new insight into the spatial organization of the spectrum of TB and COVID-19 lesions within the framework of the entire lung.


Тема - темы
COVID-19 , Mycobacterium tuberculosis , Tuberculosis , Humans , Lung/diagnostic imaging , Lung/pathology , Tomography, X-Ray Computed
8.
Korean J Radiol ; 21(4): 501-504, 2020 04.
Статья в английский | MEDLINE | ID: covidwho-2089760

Реферат

From December 2019, Coronavirus disease 2019 (COVID-19) pneumonia (formerly known as the 2019 novel Coronavirus [2019-nCoV]) broke out in Wuhan, China. In this study, we present serial CT findings in a 40-year-old female patient with COVID-19 pneumonia who presented with the symptoms of fever, chest tightness, and fatigue. She was diagnosed with COVID-19 infection confirmed by real-time reverse-transcriptase-polymerase chain reaction. CT showed rapidly progressing peripheral consolidations and ground-glass opacities in both lungs. After treatment, the lesions were shown to be almost absorbed leaving the fibrous lesions.


Тема - темы
Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , COVID-19 , Female , Fever/etiology , Humans , Lung/diagnostic imaging , Tomography, X-Ray Computed
9.
Technol Health Care ; 30(6): 1299-1314, 2022.
Статья в английский | MEDLINE | ID: covidwho-2089739

Реферат

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.


Тема - темы
COVID-19 , Humans , COVID-19/diagnostic imaging , Tomography, X-Ray Computed/methods , Machine Learning , Lung/diagnostic imaging , Algorithms , Retrospective Studies
10.
Saudi Med J ; 43(10): 1165-1167, 2022 Oct.
Статья в английский | MEDLINE | ID: covidwho-2081102

Реферат

To present an unusual and a rare pulmonary affection by coronavirus disease-19 (COVID-19), in which only one lung is affected. Coronavirus disease-19 attacks the lungs and interferes seriously with their functions. The attack is usually bilaterally, while a uni lateral pulmonary affection is unusual. The presentation, both clinical and radiological findings, bronchoscopy appearance, the strange operative findings of the resected mass, the uneventful post-operative course, in addition to the histopathological report, will be presented.In conclusion, unilateral lung affection is unusual and post-viral pneumonia COVID-19 should be considered as a possible aftermath, which may not be uncommon in Iraq.


Тема - темы
COVID-19 , Pneumonia, Viral , Humans , Pneumonia, Viral/complications , Pneumonia, Viral/diagnosis , Lung/diagnostic imaging , Bronchoscopy , Iraq
11.
PLoS One ; 17(10): e0276213, 2022.
Статья в английский | MEDLINE | ID: covidwho-2079761

Реферат

INTRODUCTION: Bedside lung ultrasound has gained a key role in each segment of the treatment chain during the COVID-19 pandemic. During the diagnostic assessment of the critically ill patients in ICUs, it is highly important to maximize the amount and quality of gathered information while minimizing unnecessary interventions (e.g. moving/rotating the patient). Another major factor is to reduce the risk of infection and the workload of the staff. OBJECTIVES: To serve these significant issues we constructed a feasibility study, in which we used a single-operator technique without moving the patient, only assessing the easily achievable lung regions at conventional BLUE points. We hypothesized that calculating this 'BLUE lung ultrasound score' (BLUE-LUSS) is a reasonable clinical tool. Furthermore, we used both longitudinal and transverse scans to measure their reliability and assessed the interobserver variability as well. METHODS: University Intensive Care Unit based, single-center, prospective, observational study was performed on 24 consecutive SARS-CoV2 RT-PCR positive, mechanically ventilated critically ill patients. Altogether 400 loops were recorded, rated and assessed off-line by 4 independent intensive care specialists (each 7+ years of LUS experience). RESULTS: Intraclass correlation values indicated good reliability for transversal and longitudinal qLUSS scores, while we detected excellent interrater agreement of both cLUSS calculation methods. All of our LUS scores correlated inversely and significantly to the P/F values. Best correlation was achieved in the case of longitudinal qLUSS (r = -0.55, p = 0.0119). CONCLUSION: Summarized score of BLUE-LUSS can be an important, easy-to-perform adjunct tool for assessing and quantifying lung pathology in critically ill ventilated patients at bedside, especially for the P/F ratio. The best agreement for the P/F ratio can be achieved with the longitudinal scans. Regarding these findings, assessing BLUE-points can be extended with the BLUE-LUSS for daily routine using both transverse and longitudinal views.


Тема - темы
COVID-19 , COVID-19/diagnostic imaging , Critical Illness , Feasibility Studies , Humans , Lung/diagnostic imaging , Pandemics , Prospective Studies , RNA, Viral , Reproducibility of Results , Respiration, Artificial , SARS-CoV-2 , Ultrasonography/methods
12.
PLoS One ; 17(10): e0271931, 2022.
Статья в английский | MEDLINE | ID: covidwho-2079704

Реферат

Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients. Annotated databases of X-rays can be of significant value in the design and development of algorithms for disease prediction. Further, explainability analysis for the performance of existing or new deep learning algorithms will be enhanced significantly with access to ground-truth abnormality annotations. The proposed COVID Abnormality Annotation for X-Rays (CAAXR) database is built upon the BIMCV-COVID19+ database which is a large-scale dataset containing COVID-19+ chest X-rays. The primary contribution of this study is the annotation of the abnormalities in over 1700 frontal chest X-rays. Further, we define protocols for semantic segmentation as well as classification for robust evaluation of algorithms. We provide benchmark results on the defined protocols using popular deep learning models such as DenseNet, ResNet, MobileNet, and VGG for classification, and UNet, SegNet, and Mask-RCNN for semantic segmentation. The classwise accuracy, sensitivity, and AUC-ROC scores are reported for the classification models, and the IoU and DICE scores are reported for the segmentation models.


Тема - темы
COVID-19 , Pneumonia , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Neural Networks, Computer , X-Rays
13.
Sci Rep ; 12(1): 17581, 2022 Oct 20.
Статья в английский | MEDLINE | ID: covidwho-2077106

Реферат

Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the identification of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos on a frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method and more surprisingly, the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score, despite being a form of inaccurate learning. We argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. The algorithm was trained using a ten-fold cross validation, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method significantly lowers the labelling effort, it must be verified on a larger consolidation/collapse dataset, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained experts' performance.


Тема - темы
COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , Ultrasonography/methods , Algorithms , Lung/diagnostic imaging
14.
Sci Rep ; 12(1): 17417, 2022 Oct 18.
Статья в английский | MEDLINE | ID: covidwho-2077093

Реферат

The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.


Тема - темы
COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , Bayes Theorem , Machine Learning , Tomography, X-Ray Computed , Lung/diagnostic imaging
15.
Respir Investig ; 60(6): 762-771, 2022 Nov.
Статья в английский | MEDLINE | ID: covidwho-2076679

Реферат

BACKGROUND: The purpose of this study was to assess the diagnostic accuracy of lung ultrasound (LUS) in determining the severity of coronavirus disease 2019 (COVID-19) pneumonia compared with thoracic computed tomography (CT) and establish the correlations between LUS score, inflammatory markers, and percutaneous oxygen saturation (SpO2). METHODS: This prospective observational study, conducted at Târgu-Mureș Pulmonology Clinic included 78 patients with confirmed severe acute respiratory syndrome coronavirus-2 infection via nasopharyngeal real-time-polymerase chain reaction (RT-PCR) (30 were excluded). Enrolled patients underwent CT, LUS, and blood tests on admission. Lung involvement was evaluated in 16 thoracic areas, using AB1 B2 C (letters represent LUS pattern) scores ranging 0-48. RESULTS: LUS revealed bilateral B-lines (97.8%), pleural irregularities with thickening/discontinuity (75%), and subpleural consolidations (70.8%). Uncommon sonographic patterns were alveolar consolidations with bronchogram (33%) and pleural effusion (2%). LUS score cutoff values of ≤14 and > 22 predicted mild COVID-19 (sensitivity [Se] = 84.6%; area under the curve [AUC] = 0.72; P = 0.002) and severe COVID-19 (Se = 50%, specificity (Sp) = 91.2%, AUC = 0.69; P = 0.02), respectively, and values > 29 predicted the patients' transfer to the intensive care unit (Se = 80%, Sp = 97.7%). LUS score positively correlated with CT score (r = 0.41; P = 0.003) and increased with the decrease of SpO2 (r = -0.49; P = 0.003), with lymphocytes decline (r = -0.52; P = 0.0001). Patients with consolidation patterns had higher ferritin and C-reactive protein than those with B-line patterns (P = 0.01; P = 0.03). CONCLUSIONS: LUS is a useful, non-invasive and effective tool for diagnosis, monitoring evolution, and prognostic stratification of COVID-19 patients.


Тема - темы
COVID-19 , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Lung/diagnostic imaging , Ultrasonography/methods , Tomography, X-Ray Computed/methods
16.
Med Sci (Basel) ; 10(4)2022 Oct 10.
Статья в английский | MEDLINE | ID: covidwho-2071634

Реферат

SARS-CoV-2-infected symptomatic patients often suffer from high fever and loss of appetite which are responsible for the deficit of fluids and of protein intake. Many patients admitted to the emergency room are, therefore, hypovolemic and hypoproteinemic and often suffer from respiratory distress accompanied by ground glass opacities in the CT scan of the lungs. Ischemic damage in the lung capillaries is responsible for the microscopic hallmark, diffuse alveolar damage (DAD) characterized by hyaline membrane formation, fluid invasion of the alveoli, and progressive arrest of blood flow in the pulmonary vessels. The consequences are progressive congestion, increase in lung weight, and progressive hypoxia (progressive severity of ARDS). Sequestration of blood in the lungs worsens hypovolemia and ischemia in different organs. This is most probably responsible for the recruitment of inflammatory cells into the ischemic peripheral tissues, the release of acute-phase mediators, and for the persistence of elevated serum levels of positive acute-phase markers and of hypoalbuminemia. Autopsy studies have been performed mostly in patients who died in the ICU after SARS-CoV-2 infection because of progressive acute respiratory distress syndrome (ARDS). In the death certification charts, after respiratory insufficiency, hypovolemic heart failure should be mentioned as the main cause of death.


Тема - темы
COVID-19 , Respiratory Distress Syndrome , Humans , SARS-CoV-2 , Hypovolemia , Lung/diagnostic imaging
17.
Am J Trop Med Hyg ; 102(5): 940-942, 2020 05.
Статья в английский | MEDLINE | ID: covidwho-2066918

Реферат

This case report underlines the appearance of a "walking pneumonia" in a novel coronavirus disease (COVID-19) patient, with evidence of progressive lung involvement on chest imaging studies. The patient traveled from Wuhan, Hubei, China, to Thailand in January 2020. One of her family members was diagnosed with COVID-19. She presented to the hospital because of her concern, but she was without fever or any respiratory symptoms. Three days earlier, her nasopharyngeal and throat swabs revealed a negative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Her initial chest radiography was abnormal, and her first sputum SARS-CoV-2 test yielded inconclusive results. A subsequent sputum test was positive for SARS-CoV-2. Diagnosis in this patient was facilitated by chest imaging and repeat viral testing. Thus, chest imaging studies might enhance capabilities for early diagnosis of COVID-19 pneumonia.


Тема - темы
Betacoronavirus , Clinical Laboratory Techniques , Coronavirus Infections , Lung/diagnostic imaging , Pandemics , Pneumonia, Viral , COVID-19 , COVID-19 Testing , COVID-19 Vaccines , Coronavirus Infections/diagnosis , Coronavirus Infections/physiopathology , Female , Humans , Middle Aged , Pneumonia, Viral/diagnosis , Pneumonia, Viral/physiopathology , Radiography , SARS-CoV-2
18.
Emerg Microbes Infect ; 11(1): 2680-2688, 2022 Dec.
Статья в английский | MEDLINE | ID: covidwho-2062778

Реферат

The long-term effect of coronavirus disease 2019 (COVID-19) has been rarely known. This study aimed to investigate healthy outcomes of COVID-19 survivors up to 2 years after the infection. A total of 155 COVID-19 patients, who were discharged from Shenzhen Third People's Hospital from February 2020 to April 2020, were enrolled and followed up until March 4, 2022. COVID-19 survivors received questionnaires of long COVID symptoms and psychological symptoms, pulmonary function tests, chest computed tomography (CT) scans and routine laboratory tests. Two years after infection, 36.6% of patients had at least one symptom of long COVID. Vision impairment and fatigue were the most common symptom. 35.0% of participants still had at least one psychological symptom of anxiety, depression, post-traumatic stress symptoms, and sleep difficulties. Radiographic abnormalities were presented in 50.7% of patients, with the most common features of fibrosis-like lesions and residual ground-glass opacity. Diffuse dysfunction (24.0%) was the main abnormalities of pulmonary function tests. Most laboratory parameters returned to normal range, while persistent abnormalities in kidney and liver function test were observed in a subset of participants after discharge. Two years after COVID-19 infection, persistent symptoms of long COVID and psychological symptoms, as well as abnormalities in pulmonary function tests and CT, were still common in a subset of recovering individuals. These findings were limited by the lack of a healthy control group and pre-COVID assessments, which should be confirmed by further large-scale studies.


Тема - темы
COVID-19 , Humans , SARS-CoV-2 , Prospective Studies , COVID-19 Testing , Lung/diagnostic imaging
19.
EBioMedicine ; 85: 104296, 2022 Nov.
Статья в английский | MEDLINE | ID: covidwho-2061073

Реферат

BACKGROUND: COVID-19 is characterized by a heterogeneous clinical presentation, ranging from mild symptoms to severe courses of disease. 9-20% of hospitalized patients with severe lung disease die from COVID-19 and a substantial number of survivors develop long-COVID. Our objective was to provide comprehensive insights into the pathophysiology of severe COVID-19 and to identify liquid biomarkers for disease severity and therapy response. METHODS: We studied a total of 85 lungs (n = 31 COVID autopsy samples; n = 7 influenza A autopsy samples; n = 18 interstitial lung disease explants; n = 24 healthy controls) using the highest resolution Synchrotron radiation-based hierarchical phase-contrast tomography, scanning electron microscopy of microvascular corrosion casts, immunohistochemistry, matrix-assisted laser desorption ionization mass spectrometry imaging, and analysis of mRNA expression and biological pathways. Plasma samples from all disease groups were used for liquid biomarker determination using ELISA. The anatomic/molecular data were analyzed as a function of patients' hospitalization time. FINDINGS: The observed patchy/mosaic appearance of COVID-19 in conventional lung imaging resulted from microvascular occlusion and secondary lobular ischemia. The length of hospitalization was associated with increased intussusceptive angiogenesis. This was associated with enhanced angiogenic, and fibrotic gene expression demonstrated by molecular profiling and metabolomic analysis. Increased plasma fibrosis markers correlated with their pulmonary tissue transcript levels and predicted disease severity. Plasma analysis confirmed distinct fibrosis biomarkers (TSP2, GDF15, IGFBP7, Pro-C3) that predicted the fatal trajectory in COVID-19. INTERPRETATION: Pulmonary severe COVID-19 is a consequence of secondary lobular microischemia and fibrotic remodelling, resulting in a distinctive form of fibrotic interstitial lung disease that contributes to long-COVID. FUNDING: This project was made possible by a number of funders. The full list can be found within the Declaration of interests / Acknowledgements section at the end of the manuscript.


Тема - темы
COVID-19 , Lung Diseases, Interstitial , Humans , Lung/diagnostic imaging , Lung/pathology , Lung Diseases, Interstitial/pathology , Fibrosis , Biomarkers/analysis , Ischemia/pathology
20.
PLoS One ; 17(10): e0274098, 2022.
Статья в английский | MEDLINE | ID: covidwho-2054336

Реферат

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.


Тема - темы
COVID-19 , Deep Learning , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Radiography, Thoracic/methods , X-Rays
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