2024MAY10: Our hosting provider is experiencing intermittent networking issues. We apologize for any inconvenience.

Searching across hundreds of databases

Our searching services are busy right now. Your search will reload in five seconds.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

Persistent alveolar type 2 dysfunction and lung structural derangement in post-acute COVID-19.

medRxiv : the preprint server for health sciences | 2022

SARS-CoV-2 infection can manifest as a wide range of respiratory and systemic symptoms well after the acute phase of infection in over 50% of patients. Key questions remain on the long-term effects of infection on tissue pathology in recovered COVID-19 patients. To address these questions we performed multiplexed imaging of post-mortem lung tissue from 12 individuals who died post-acute COVID-19 (PC) and compare them to lung tissue from patients who died during the acute phase of COVID-19, or patients who died with idiopathic pulmonary fibrosis (IPF), and otherwise healthy lung tissue. We find evidence of viral presence in the lung up to 359 days after the acute phase of disease, including in patients with negative nasopharyngeal swab tests. The lung of PC patients are characterized by the accumulation of senescent alveolar type 2 cells, fibrosis with hypervascularization of peribronchial areas and alveolar septa, as the most pronounced pathophysiological features. At the cellular level, lung disease of PC patients, while distinct, shares pathological features with the chronic pulmonary disease of IPF. which may help rationalize interventions for PC patients. Altogether, this study provides an important foundation for the understanding of the long-term effects of SARS-CoV-2 pulmonary infection at the microanatomical, cellular, and molecular level.

Pubmed ID: 36482970 RIS Download

Research resources used in this publication

None found

Additional research tools detected in this publication

Antibodies used in this publication

None found

Associated grants

None

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


scikit-learn (tool)

RRID:SCR_002577

scikit-learn: machine learning in Python

View all literature mentions