COVID-19 bimodal clinical and pathological phenotypes.
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ABSTRACT:
SUBMITTER: Batah SS
PROVIDER: S-EPMC8787021 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A middle aged COVID-19 male patient presented 2 weeks after discharge with new onset of dyspnoea and desaturation. Radiological studies revealed right side pneumothorax and lower lobe cystic air space. Chest drain was inserted and on a later date the patient underwent thoracoscopic surgery where a large pneumatocele was identified. Deroofing and closure of sources of air leak were done. Histopathological examination demonstrated extensive fibrosis, intra-alveolar Haemorrhage and pneumocytes hyperplasia.
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Project description:Coronavirus disease 2019 (COVID-19), which is becoming a global pandemic, is caused by SARS-CoV-2 infection. In COVID-19, thrombotic events occur frequently, mainly venous thromboembolism (VTE), which is closely related to disease severity and clinical prognosis. Compared with historical controls, the occurrence of VTE in hospitalized and critical COVID-19 patients is incredibly high. However, the pathophysiology of thrombosis and the best strategies for thrombosis prevention in COVID-19 remain unclear, thus needing further exploration. Virchow's triad elements have been proposed as important risk factors for thrombotic diseases. Therefore, the three factors outlined by Virchow can also be applied to the formation of venous thrombosis in the COVID-19 setting. A thorough understanding of the complex interactions in these processes is important in the search for effective treatments for COVID-19. In this work, we focus on the pathological mechanisms of VTE in COVID-19 from the aspects of endothelial dysfunction, hypercoagulability, abnormal blood flow. We also discuss the treatment of VTE as well as the ongoing clinical trials of heparin anticoagulant therapy. In addition, according to the pathophysiological mechanism of COVID-19-associated thrombosis, we extended the range of antithrombotic drugs including antiplatelet drugs, antifibrinolytic drugs, and anti-inflammatory drugs, hoping to find effective drug therapy and improve the prognosis of VTE in COVID-19 patients.
| S-EPMC8706381 | biostudies-literature
Project description:Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the SARS-CoV-2 virus infection, incorporating the renin-angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of co-morbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age, co-morbidities such as obesity, diabetes, and hypertension, and dysregulated immune response 1,2 . We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8 + T cells and sufficient control of the innate immune response. Furthermore, the best treatment -or combination of treatments - depends on the pre-infection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19.
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Project description:PurposeHeterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.MethodsThis is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes.ResultsThe database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III.ConclusionWe identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.
| S-EPMC8011766 | biostudies-literature
Project description:Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, incorporating the renin-angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines, and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of comorbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age; comorbidities such as obesity, diabetes, and hypertension; and dysregulated immune response. We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8+ T cells and sufficient control of the innate immune response. Furthermore, the best treatment-or combination of treatments-depends on the preinfection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19.
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Project description: Not available
| S-BSST563 | biostudies-other
Project description:BACKGROUND:Rare variants in the low-density lipoprotein receptor related protein 10 gene (LRP10) have recently been implicated in the etiology of Parkinson's disease (PD) and dementia with Lewy bodies (DLB). OBJECTIVE:We searched for LRP10 variants in a new series of brain donors with dementia and Lewy pathology (LP) at autopsy, or dementia and parkinsonism without LP but with various other neurodegenerative pathologies. METHODS:Sanger sequencing of LRP10 was performed in 233 donors collected by the Netherlands Brain Bank. RESULTS:Rare, possibly pathogenic heterozygous LRP10 variants were present in three patients: p.Gly453Ser in a patient with mixed Alzheimer's disease (AD)/Lewy body disease (LBD), p.Arg151Cys in a DLB patient, and p.Gly326Asp in an AD patient without LP. All three patients had a positive family history for dementia or PD. CONCLUSION:Rare LRP10 variants are present in some patients with dementia and different brain pathologies including DLB, mixed AD/LBD, and AD. These findings suggest a role for LRP10 across a broad neurodegenerative spectrum.
| S-EPMC7505004 | biostudies-literature
Project description:The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global threat to human health and life. A useful pathological animal model accurately reflecting human pathology is needed to overcome the COVID-19 crisis. In the present study, COVID-19 cynomolgus monkey models including monkeys with underlying diseases causing severe pathogenicity such as metabolic disease and elderly monkeys were examined. Cynomolgus macaques with various clinical conditions were intranasally and/or intratracheally inoculated with SARS-CoV-2. Infection with SARS-CoV-2 was found in mucosal swab samples, and a higher level and longer period of viral RNA was detected in elderly monkeys than in young monkeys. Pneumonia was confirmed in all of the monkeys by computed tomography images. When monkeys were readministrated SARS-CoV-2 at 56 d or later after initial infection all of the animals showed inflammatory responses without virus detection in swab samples. Surprisingly, in elderly monkeys reinfection showed transient severe pneumonia with increased levels of various serum cytokines and chemokines compared with those in primary infection. The results of this study indicated that the COVID-19 cynomolgus monkey model reflects the pathophysiology of humans and would be useful for elucidating the pathophysiology and developing therapeutic agents and vaccines.
| S-EPMC8639365 | biostudies-literature
Project description:BackgroundMultiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment.MethodsWe analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 h of admission to train our algorithm. We conducted a survival analysis to compare the clinical outcomes across phenotypes. With the data split into training and validation sets (75/25 ratio), we developed a decision-tree-based model to facilitate the interpretation and assignment of the observed phenotypes.ResultsAgglomerative hierarchical clustering was the most robust algorithm. We identified three clinical phenotypes: 79 patients (14%) in Cluster 1, 275 patients (50%) in Cluster 2, and 203 (37%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Intensive care unit (ICU) admission and mechanical ventilation risks were the highest in Cluster 1. Using only two to four decision rules, the classification and regression tree (CART) phenotype assignment model achieved an AUC of 84% (81.5-86.5%, 95 CI) on the validation set.ConclusionsWe conducted a multidimensional phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. We also demonstrated the clinical usability of this approach, as phenotypes can be accurately assigned using a simple decision tree. Further research is still needed to properly incorporate these phenotypes in the management of patients with COVID-19.
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