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DCs-based therapies: potential strategies in severe SARS-CoV-2 infection.

ABSTRACT: Pneumonia caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading globally. There have been strenuous efforts to reveal the mechanisms that the host defends itself against invasion by this virus. The immune system could play a crucial role in virus infection. Dendritic cell as sentinel of the immune system plays an irreplaceable role. Dendritic cells-based therapeutic approach may be a potential strategy for SARS-CoV-2 infection. In this review, the characteristics of coronavirus are described briefly. We focus on the essential functions of dendritic cell in severe SARS-CoV-2 infection. Basis of treatment based dendritic cells to combat coronavirus infections is summarized. Finally, we propose that the combination of DCs based vaccine and other therapy is worth further study.

SUBMITTER: Han J

PROVIDER: S-EPMC7757148 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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DCs-based therapies: potential strategies in severe SARS-CoV-2 infection.

Han Jian J Sun Jiazhi J Zhang Guixin G Chen Hailong H

International journal of medical sciences 20210101 2

Pneumonia caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading globally. There have been strenuous efforts to reveal the mechanisms that the host defends itself against invasion by this virus. The immune system could play a crucial role in virus infection. Dendritic cell as sentinel of the immune system plays an irreplaceable role. Dendritic cells-based therapeutic approach may be a potential strategy for SARS-CoV-2 infection. In this review, the characteristic ...[more]

PMID: 33390810

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Project description:Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.

Dataset Information

DCs-based therapies: potential strategies in severe SARS-CoV-2 infection.

Publications

DCs-based therapies: potential strategies in severe SARS-CoV-2 infection.

Similar Datasets

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